Cheap, Solid Microcaps Far Outperform the S&P 500

September 4, 2022

A wise long-term investment for most investors is an S&P 500 index fund.  It’s just simple arithmetic, as Warren Buffett and Jack Bogle frequently observe: https://boolefund.com/warren-buffett-jack-bogle/

But you can do significantly better—roughly 18% per year (instead of 10% per year)—by systematically investing in cheap, solid microcap stocks.  The mission of the Boole Microcap Fund is to help you do just that.

Most professional investors never consider microcaps because their assets under management are too large.  Microcaps aren’t as profitable for them.  That’s why there continues to be a compelling opportunity for savvy investors.  Because microcaps are largely ignored, many get quite cheap on occasion.

Warren Buffett earned the highest returns of his career when he could invest in microcap stocks.  Buffett says he’d do the same today if he were managing small sums: https://boolefund.com/buffetts-best-microcap-cigar-butts/

Look at this summary of the CRSP Decile-Based Size and Return Data from 1927 to 2020:

Decile Market Cap-Weighted Returns Equal Weighted Returns Number of Firms (year-end 2020) Mean Firm Size (in millions)
1 9.67% 9.47% 179 145,103
2 10.68% 10.63% 173 25,405
3 11.38% 11.17% 187 12,600
4 11.53% 11.29% 203 6,807
5 12.12% 12.03% 217 4,199
6 11.75% 11.60% 255 2,771
7 12.01% 11.99% 297 1,706
8 12.03% 12.33% 387 888
9 11.55% 12.51% 471 417
10 12.41% 17.27% 1,023 99
9+10 11.71% 15.77% 1,494 199

(CRSP is the Center for Research in Security Prices at the University of Chicago.  You can find the data for various deciles here:  http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html)

The smallest two deciles—9+10—comprise microcap stocks, which typically are stocks with market caps below $500 million.  What stands out is the equal weighted returns of the 9th and 10th size deciles from 1927 to 2020:

Microcap equal weighted returns = 15.8% per year

Large-cap equal weighted returns = ~10% per year

In practice, the annual returns from microcap stocks will be 1-2% lower because of the difficulty (due to illiquidity) of entering and exiting positions.  So we should say that an equal weighted microcap approach has returned 14% per year from 1927 to 2020, versus 10% per year for an equal weighted large-cap approach.

Still, if you can do 4% better per year than the S&P 500 index (on average)—even with only a part of your total portfolio—that really adds up after a couple of decades.

 

VALUE SCREEN: +2-3%

By systematically implementing a value screen—e.g., low EV/EBITDA or low P/E—to a microcap strategy, you can add 2-3% per year.

 

IMPROVING FUNDAMENTALS: +2-3%

You can further boost performance by screening for improving fundamentals.  One excellent way to do this is using the Piotroski F_Score, which works best for cheap micro caps.  See:  https://boolefund.com/joseph-piotroski-value-investing/

 

BOTTOM LINE

If you invest in microcap stocks, you can get about 14% a year.  If you also use a simple screen for value, that adds at least 2% a year.  If, in addition, you screen for improving fundamentals, that adds at least another 2% a year.  So that takes you to 18% a year, which compares quite well to the 10% a year you could get from an S&P 500 index fund.

What’s the difference between 18% a year and 10% a year?  If you invest $50,000 at 10% a year for 30 years, you end up with $872,000, which is good.  If you invest $50,000 at 18% a year for 30 years, you end up with $7.17 million, which is much better.

Please contact me if you would like to learn more.

    • My email: jb@boolefund.com.
    • My cell: 206.518.2519

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

Superforecasting

August 28, 2022

Philip Tetlock is the world expert on the topic of making forecasts.  Tetlock and Dan Gardner have written an excellent book, Superforecasting: The Art and Science of Prediction, which summarizes the mental habits of people who have consistently been able to make useful predictions about the future.  Everyone would benefit if we could predict the future better.  This book teaches us how we can do this if we’re willing to put in the hard work required.

Here’s an outline:

    • An Optimistic Skeptic
    • Illusions of Knowledge
    • Keeping Score
    • Superforecasters
    • Supersmart?
    • Superquants?
    • Supernewsjunkies?
    • Perpetual Beta
    • Superteams
    • The Leader’s Dilemma
    • Are They Really So Super?
    • What’s Next?
    • Ten Commandments for Aspiring Superforecasters
Illustration by Maxim Popov.

 

AN OPTIMISTIC SKEPTIC

Tetlock writes that we are all forecasters.  Whenever we think about making an important decision—changing jobs, getting married, buying a home, making an investment, launching a product, or retiring—we make forecasts about the future.  Tetlock:

Often we do our own forecasting.  But when big events happen—markets crash, wars loom, leaders tremble—we turn to the experts, those in the know.  We look to people like Tom Friedman.

If you are a White House staffer, you might find him in the Oval Office with the president of the United States, talking about the Middle East.  If you are a Fortune 500 CEO, you might spot him in Davos, chatting in the lounge with hedge fund billionaires and Saudi princes.  And if you don’t frequent the White House or swanky Swiss hotels, you can read his New York Times columns and bestselling books that tell you what’s happening now, why, and what will happen next.  Millions do.

Tetlock compares Friedman to Bill Flack, who forecasts global events.  Bill recently retired from the Department of Agriculture.  He is fifty-five and spends some of his time forecasting.

Bill has answered roughly three hundred questions like “Will Russia officially annex additional Ukrainian territory in the next three months?” and “In the next year, will any country withdraw from the eurozone?”  They are questions that matter.  And they’re difficult.  Corporations, banks, embassies, and intelligence agencies struggle to answer such questions all the time.  “Will North Korea detonate a nuclear device before the end of this year?”  “How many additional countries will report cases of the Ebola virus in the next eight months?”  “Will India or Brazil become a permanent member of the UN Security Council in the next two years?”  Some of the questions are downright obscure, at least for most of us.  “Will NATO invite new countries to join the Membership Action Plan (MAP) in the next nine months?”  “Will the Kurdistan Regional Government hold a referendum on national independence this year?”  “If a non-Chinese telecommunications firm wins a contract to provide Internet services in the Shanghai Free Trade Zone in the next two years, will Chinese citizens have access to Facebook and/or Twitter?”  When Bill first sees one of these questions, he may have no clue how to answer it.  “What on earth is the Shanghai Free Trade Zone?” he may think.  But he does his homework.  He gathers facts, balances clashing arguments, and settles on an answer.

Tetlock continues:

No one bases decisions on Bill Flack’s forecasts, or asks Bill to share his thoughts on CNN.  He has never been invited to Davos to sit on a panel with Tom Friedman.  And that’s unfortunate.  Because Bill Flack is a remarkable forecaster.  We know that because each one of Bill’s predictions has been dated, recorded, and assessed for accuracy by independent scientific observers.  His track record is excellent.

Bill is not alone.  There are thousands of others answering the same questions.  All are volunteers.  Most aren’t as good as Bill, but about 2% are.  They include engineers and lawyers, artists and scientists, Wall Streeters and Main Streeters, professors and students… I call them superforecasters because that is what they are.  Reliable evidence proves it.  Explaining why they’re so good, and how others can learn to do what they do, is my goal in this book.

Tetlock points out that it would be interesting to compare superforecasters to an expert like Tom Friedman.  However, Friedman’s track record has never been rigorously tested.  Of course, there are endless opinions about Friedman’s track record.  Tetlock:

Every day, the news media deliver forecasts without reporting, or even asking, how good the forecasters who made the forecasts really are.  Every day, corporations and governments pay for forecasts that may be prescient or worthless or something in between.  And every day, all of us—leaders of nations, corporate executives, investors, and voters—make critical decisions on the basis of forecasts whose quality is unknown.  Baseball managers wouldn’t dream of getting out the checkbook to hire a player without consulting performance statistics… And yet when it comes to the forecasters who help us make decisions that matter far more than any baseball game, we’re content to be ignorant.

That said, forecasting is a skill.  This book, says Tetlock, will show you how.

Prior to his work with superforecasters, Tetlock conducted a 20-year research project in which close to 300 experts made predictions about the economy, stocks, elections, wars, and other issues.  The experts made roughly 28,000 predictions.  On the whole, the experts were no better than chance.  The average expert was no better than a dart-throwing chimpanzee.  Tetlock says he doesn’t mind the joke about the dart-throwing chimpanzee because it makes a valid point:

Open any newspaper, watch any TV news show, and you find experts who forecast what’s coming.  Some are cautious.  More are bold and confident.  A handful claim to be Olympian visionaries able to see decades into the future.  With few exceptions, they are not in front of the cameras because they possess any proven skill at forecasting.  Accuracy is seldom even mentioned.  Old forecasts are like old news—soon forgotten—and pundits are almost never asked to reconcile what they said with what actually happened.  The one undeniable talent that talking heads have is their skill at telling a compelling story with conviction, and that is enough.  Many have become wealthy peddling forecasting of untested value to corporate executives, government officials, and ordinary people who would never think of swallowing medicine of unknown efficacy and safety but who routinely pay for forecasts that are as dubious as elixirs sold from the back of a wagon.

Tetlock is optimistic about the ability of people to learn to be superforecasters.  But he’s also a “skeptic” when it comes to how precisely and how far into the future people can predict.  Tetlock explains:

In a world where a butterfly in Brazil can make the difference between just another sunny day in Texas and a tornado tearing through a town, it’s misguided to think anyone can see very far into the future.

Tetlock on why he’s optimistic:

We know that in so much of what people want to predict—politics, economics, finance, business, technology, daily life—predictability exists, to some degree, in some circumstances.  But there is so much else we do not know.  For scientists, not knowing is exciting.  It’s an opportunity to discover; the more that is unknown, the greater the opportunity.  Thanks to the frankly quite amazing lack of rigor in so many forecasting domains, this opportunity is huge.  And to seize it, all we have to do is set a clear goal—accuracy!—and get serious about measuring.

Tetlock and his research (and life) partner Barbara Mellers launched the Good Judgment Project (GJP) in 2011.  Tetlock:

Cumulatively, more than twenty thousand intellectually curious laypeople tried to figure out if protests in Russia would spread, the price of gold would plummet, the Nikkei would close above 9,500, war would erupt on the Korean peninsula, and many other questions about complex, challenging global issues.  By varying the experimental conditions, we could gauge which factors improved foresight, by how much, over which time frames, and how good forecasts could become if best practices were layered on each other.

The GJP was part of a much larger research effort sponsored by the Intelligence Advanced Research Projects Activity (IARPA).

IARPA is an agency within the intelligence community that reports to the director of National Intelligence and its job is to support daring research that promises to make American intelligence better at what it does.  And a big part of what American intelligence does is forecast global political and economic trends.

(IARPA logo via Wikimedia Commons)

Tetlock continues:

…IARPA created a forecasting tournament in which five scientific teams led by top researchers in the field would compete to generate accurate forecasts on the sorts of tough questions intelligence analysts deal with every day.  The Good Judgment Project was one of those five teams… By requiring teams to forecast the same questions at the same time, the tournament created a level playing field—and a rich treasure trove of data about what works, how well, and when.  Over four years, IARPA posed nearly five hundred questions about world affairs… In all, we gathered over one million individual judgments about the future.

In year 1, GJP beat the official control group by 60%.  In year 2, we beat the control group by 78%.  GJP also beat its university-affiliated competitors, including the University of Michigan and MIT, by hefty margins, from 30% to 70%, and even outperformed professional intelligence analysts with access to classified data.  After two years, GJP was doing so much better than its academic competitors that IARPA dropped the other teams.

What Tetlock learned was two key things:  some people clearly can predict certain events; and the habits of thought of these forecasters can be learned and cultivated “by any intelligent, thoughtful, determined person.”

There’s a question about whether computers can be trained to outpredict superforecasters.  Probably not for some time.  It’s more likely, says Tetlock, the superforecasters working with computers will outperform computers alone and superforecasters alone.  Think of freestyle chess where chess experts using computers are often stronger than computers alone and than human experts alone.

 

ILLUSIONS OF KNOWLEDGE

Archie Cochrane was supposed to die from cancer.  A specialist had done surgery to remove a lump.  During the surgery, it appeared that the cancer had spread.  So the specialist removed the pectoralis minor during the surgery.  He then told Archie Cochrane that he didn’t have long to live.  However, when a pathologist later examined the tissue that had been removed during the surgery, he learned that Cochrane didn’t have cancer at all.  This was a good thing because Archie Cochrane eventually became a revered figure in medicine.  Tetlock comments:

We have all been too quick to make up our minds and too slow to change them.  And if we don’t examine how we make these mistakes, we will keep making them.  This stagnation can go on for years.  Or a lifetime.  It can even last centuries, as the long and wretched history of medicine illustrates.

(Photograph from 1898, via Wikimedia Commons)

Tetlock continues:

When George Washington fell ill in 1799, his esteemed physicians bled  him relentlessly, doused him with mercury to cause diarrhea, induced vomiting, and raised blood-filled blisters by applying hot cups to the old man’s skin.  A physician in Aristotle’s Athens, or Nero’s Rome, or medieval Paris, or Elizabethan London would have nodded at much of that hideous regime.

Washington died… It’s possible that the treatments helped but not enough to overcome the disease that took Washington’s life, or that they didn’t help at all, or that the treatments even hastened Washington’s death.  It’s impossible to know which of these conclusions is true merely by observing that one outcome.  Even with many such observations, the truth can be difficult or impossible to tease out.  There are just too many factors involved, too many possible explanations, too many unknowns.  And if physicians are already inclined to think the treatments work—which they are, or they wouldn’t prescribe them—all that ambiguity is likely to be read in favor of the happy conclusion that the treatments really are effective.

Rigorous experimentation was needed.  But that was never done.  Tetlock brings up the example of Galen, the second-century physician to Roman emperors.  Galen’s writings were “the indisputable source of medical authority for more than a thousand years.”  But Galen never did any experiments.  Galen was a tad overconfident, writing: “All who drink of this treatment recover in a short time, except those whom it does not help, who all die.  It is obvious, therefore, that it fails only in incurable cases.”  Tetlock comments:

Galen was an extreme example but he is the sort of figure who pops up repeatedly in the history of medicine.  They are men (always men) of strong conviction and profound trust in their own judgment.  They embrace treatments, develop bold theories for why they work, denounce rivals as quacks and charlatans, and spread their insights with evangelical passion.

Tetlock again:

Not until the twentieth century did the idea of randomized trial experiments, careful measurement, and statistical power take hold… Randomly assigning people to one group or the other would mean whatever differences there are among them should balance out if enough people participated in the experiment.  Then we can confidently conclude that the treatment caused any differences in observed outcomes.  It isn’t perfect.  There is no perfection in our messy world.  But it beats wise men stroking their chins.

The first serious trials were attempted only after World War II.

But still the physicians and scientists who promoted the modernization of medicine routinely found that the medical establishment wasn’t interested, or was even hostile to their efforts.  “Too much that was being done in the name of health care lacked scientific validation,” Archie Cochrane complained about medicine in the 1950s and 1960s… Physicians and the institutions they controlled didn’t want to let go of the idea that their judgment alone revealed the truth, so they kept doing what they did because they had always done it that way—and they were backed up by respected authority.  They didn’t need scientific validation.  They just knew.  Cochrane despised this attitude.  He called it “the God complex.”

Tetlock describes the two systems we have in our brains: System 1 and System 2.

System 2 is the familiar realm of conscious thought.  It consists of everything we choose to focus on.  By contrast, System 1 is largely a stranger to us.  It is the realm of automatic perceptual and cognitive operations—like those you are running right now to transform the print on this page into a meaningful sentence or to hold the book while reaching for a glass and taking a sip.  We have no awareness of these rapid-fire processes but we could not function without them.  We would shut down.

The numbering of the two systems is not arbitrary.  System 1 comes first.  It is fast and constantly running in the background.  If a question is asked and you instantly know the answer, it sprang from System 1.  System 2 is charged with interrogating that answer.  Does it stand up to scrutiny?  Is it backed by evidence?  This process takes time and effort, which is why the standard routine in decision making is this: System 1 delivers an answer, and only then can System 2 get involved, starting with an examination of what System 1 decided.

Tetlock notes that in the Paleolithic world in which our brains evolved, System 1’s ability to make quick decisions helped us to survive.  A shadow in the grass was immediately assumed to be dangerous.  There was no time for System 2 to second guess.  If System 1 was functioning properly, we would already be running by the time we were consciously aware of the shadow in the grass.

Although System 2 can be trained to think rationally, mathematically, and in terms of statistics, both System 1 and System 2 naturally look first for evidence that confirms a given hypothesis.  The tendency to look for and see only confirming evidence, while disregarding potentially disconfirming evidence, is called confirmation bias.

Confirmation bias is one reason we tend to be overconfident.  Tetlock quotes Daniel Kahneman:

“It is wise to take admissions of uncertainty seriously, but declarations of high confidence mainly tell you that an individual has constructed a coherent story in his mind, not necessarily that the story is true.”

One thing that System 1 does if it faces a question is to substitute an easier question and answer that.  Tetlock calls this bait and switch.  Tetlock gives an example.  The first question is, “Should I worry about the shadow in the long grass?”  System 1 automatically looks for an easier question, like, “Can I easily recall a lion attacking someone from the long grass?”  If the answer to the easier question is “yes,” then the answer to the original question is also “yes.”  Tetlock calls our reliance on the automatic operations of System 1 tip-of-your-nose perspective.

That said, there are areas in life where a human can develop expertise to the point where System 1 intuition can be trusted.  Examples include fire fighting and chess.  Tetlock:

It’s pattern recognition.  With training or experience, people can encode patterns deep in their memories in vast number and intricate detail—such as the estimated fifty thousand to one hundred thousand chess positions that top players have in their repertoire.  If something doesn’t fit a pattern—like a kitchen fire giving off more heat than a kitchen fire should—a competent expert senses it immediately.

Note that developing such expertise requires working in a world with valid cues.  Valid cues exist in the world of firefighting and chess, but much less so in the world of stocks, for example.  If there’s a lot of randomness and it takes a long time to get valid feedback from decisions—which is the case in the world of stocks—then it can take much longer to develop true expertise.

 

KEEPING SCORE

In order to tell whether someone is able to make forecasts with some accuracy, the forecasts must be specific enough to be measured.  Also, by measuring forecasts, it becomes possible for people to improve.

Photo by Redwall

Tetlock writes:

In 1984, with grants from the Carnegie and MacArthur foundations, the National Research Council—the research arm of the United States National Academy of Sciences—convened a distinguished panel charged with nothing less than “preventing nuclear war.”…

The panel did its due diligence.  It invited a range of experts—intelligence analysts, military officers, government officials, arms control experts, and Sovietologists—to discuss the issues.  They… were an impressive bunch.  Deeply informed, intelligent, articulate.  And pretty confident that they knew what was happening and where we were heading.

Both liberals and conservatives agreed that the next Soviet leader would be a Communist Party man.  Both liberals and conservative were confident of their views.

But then something unexpected happened.  The Politburo appointed Mikhail Gorbachev as the next general secretary of the Communist Party of the Soviet Union.  Tetlock:

Gorbachev changed direction swiftly and abruptly.  His policies of glasnost (openness) and perestroika (restructuring) liberalized the Soviet Union.  Gorbachev also sought to normalize relations with the United States and reverse the arms race…

Few experts saw this coming.  And yet it wasn’t long before most of those who didn’t see it coming grew convinced that they knew exactly why it had happened, and what was coming next.

Tetlock comments:

My inner cynic started to suspect that no matter what had happened the experts would have been just as adept at downplaying their predictive failures and sketching an arc of history that made it appear that they saw it coming all along.  After all, the world had just witnessed a huge surprise involving one of the most consequential matters imaginable.  If this didn’t induce a shiver of doubt, what would?  I was not questioning the intelligence or integrity of these experts, many of whom had won big scientific prizes or held high government offices… But intelligence and integrity are not enough.  The national security elites looked a lot like the renowned physicians from the prescientific era… But tip-of-your-nose delusions can fool anyone, even the best and the brightest—perhaps especially the best and the brightest.

In order for forecasts to be measurable, they have to have a specific time frame and what is expected to happen must be clearly defined.  Then there’s the issue of probability.  It’s easy to judge a forecast that something will definitely happen by some specific time.  Jonathan Schell predicted, in his influential book The Fate of the Earth, that a nuclear war would happen by 1985.  Since that didn’t happen, obviously Schell’s prediction was wrong.  But Tetlock asks, what if Schell had said that a nuclear war was “very likely”?  Tetlock:

The only way to settle this definitively would be to rerun history hundreds of times, and if civilization ends in piles of irradiated rubble in most of those reruns, we would know Schell was right.  But we can’t do that, so we can’t know.

Furthermore, phrases such as “very likely” are a problem.  Consider the experience of Sherman Kent.

In intelligence circles, Sherman Kent is a legend.  With a PhD in history, Kent left a faculty position at Yale to join the Research and Analysis Branch of the newly created Coordinator of Information (COI) in 1941.  The COI became the Office of Strategic Services (OSS).  The OSS became the Central Intelligence Agency (CIA).  By the time Kent retired from the CIA in 1967, he had profoundly shaped how the American intelligence community does what it calls intelligence analysis—the methodological examination of the information collected by spies and surveillance to figure out what it means, and what will happen next.

…forecasting is all about estimating the likelihood of something happening, which Kent and his colleagues did for many years at the Office of National Estimates—an obscure but extraordinarily influential bureau whose job was to draw on all information available to the CIA, synthesize it, and forecast anything and everything that might help top officeholders in the US government decide what to do next.

The stakes were high, and Kent weighted each word carefully.  Nonetheless, there was some confusion because they used phrases like “very likely” and “a serious possibility” instead of assigning precise probabilities.  When Kent asked each team member what they had in mind as a specific probability, it turned out that each person had a different probability in mind even though they had all agreed on “a serious possibility.”  The opinions ranged from 20 percent to 80 percent.  Kent grew concerned.

Kent was right to worry.  In 1961, when the CIA was planning to topple the Castro government by landing a small army of Cuban expatriates at the Bay of Pigs, President John F. Kennedy turned to the military for an unbiased assessment.  The Joint Chiefs of Staff concluded that the plan had a “fair chance” of success.  The man who wrote the words “fair chance” later said he had in mind odds of 3 to 1 against success.  But Kennedy was never told precisely what “fair chance” meant and, not unreasonably, he took it to be a much more positive assessment.  Of course we can’t be sure that if the Chiefs had said “We feel it’s 3 to 1 the invasion will fail” that Kennedy would have called it off, but it surely would have made him think harder about authorizing what turned out to be an unmitigated disaster.

In order to solve this problem, Kent suggested specific probabilities be associated with specific phrases.  For instance, “almost certain” would mean a 93% chance, plus or minus 6%, while “probable” would mean a 75% chance, plus or minus 12%.

Unfortunately, Kent’s scheme was never adopted.  Some felt specific probabilities were unnatural.  Others thought it made you sound like a bookie.  (Kent’s famous response: “I’d rather be a bookie than a goddamn poet.”)  Still others objected that specific probabilities seemed too much like objective facts instead of subjective judgments, which they were.  The answer to that objection was simply to make it understood that the estimates were just guesses—opinions—and nothing more.

Tetlock observes that there’s a fundamental obstacle to adopting specific probabilities.  The obstacle has to do with what Tetlock calls the wrong-side-of-maybe fallacy.

If a meteorologist says there’s a 70% chance of rain, is she wrong if it doesn’t rain?  Not necessarily.  To see how right or wrong the estimate is, we would have to rerun the day a hundred times.  If it rained in 70% of the reruns, then the meteorologist would have been exactly right with her 70% estimate.  Unfortunately, however, that’s not how people judge it.  If the meteorologist estimates a 70% chance of rain, then if it rains, she was right, but if it doesn’t rain, she was wrong, according to how most people think about it.  Tetlock writes:

The prevalence of this elementary error has a terrible consequence.  Consider that if an intelligence agency says there is a 65% chance that an event will happen, it risks being pilloried if it does not—and because the forecast itself says there is a 35% chance it will not happen, that’s a big risk.  So what’s the safe thing to do?  Stick with elastic language… If the event happens, “a fair chance” can retroactively be stretched to mean something considerably bigger than 50%—so the forecaster nailed it.  If it doesn’t happen, it can be shrunk to something much smaller than 50%—and again the forecaster nailed it.  With perverse incentives like these, it’s no wonder people prefer rubbery words over firm numbers.

Tetlock observes that it wasn’t until after the debacle regarding Saddam Hussein’s purported weapons of mass destruction, and the reforms that followed, that expressing probabilities with numbers became more accepted.  But that’s within the intelligence community.  In the broader community, especially in the media, very vague language is still common.  Tetlock:

If we are serious about measuring and improving, this won’t do.  Forecasts must have clearly defined terms and timelines.  They must use numbers.  And one more thing is essential: we must have lots of forecasts.

Consider the meteorologist again.  If she makes a new forecast each day, then over time her track record can be determined.  If she’s perfect, then 70% of the time she says there’s a 70% chance of rain, it rains, and so forth.  That would be perfect calibration.  If it only rains 40% of the time she says there’s a 70% chance of rain, then she’s overconfident.  If it rains 80% of the time she says there’s a 30% chance of rain, then she’s underconfident.

Of course, when you consider something like presidential elections, which happen every four years, it could take a long time to build about enough predictions for testing purposes.  And some events are even rarer than every four years.

Besides calibration, there is also “resolution.”  If someone assigns very high probabilities—80% to 100%—to things that happen, and very low probabilities—0% to 20%—to things that don’t happen, then they have good resolution.

The math behind this system was developed by Glenn W. Brier in 1950, hence results are called Brier scores.  In effect, Brier scores measure the distance between what you forecast and what actually happened… Perfection is 0.  A hedged fifty-fifty call, or random guessing in the aggregate, will produce a Brier score of 0.5.  A forecast that is wrong to the greatest possible extent—saying there is a 100% chance that something will happen and it doesn’t, every time—scores a disastrous 2.0, as far from The Truth as it is possible to get.

Tetlock writes about the 20 years he spent gathering roughly 28,000 predictions by 284 experts.  This was the Expert Political Judgment (EPJ) project.  Tetlock:

If you didn’t know the punch line of EPJ before you read this book, you do now: the average expert was roughly as accurate as a dart-throwing chimpanzee.

Tetlock then notes that there were two statistically distinguishable groups.  One group failed to do better than random guessing.  The second group beat the chimp, but not by much.

So why did one group do better than the other?  It wasn’t whether they had PhDs or access to classified information.  Nor was it what they thought—whether they were liberals or conservatives, optimists or pessimists.  The critical factor was how they thought.

Tetlock explains:

One group tended to organize their thinking around Big Ideas, although they didn’t agree on which Big Ideas were true or false… As ideologically diverse as they were, they were united by the fact that their thinking was so ideological.  They sought to squeeze complex problems into the preferred cause-effect templates and treated what did not fit as irrelevant distractions.  Allergic to wishy-washy answers, they kept pushing their analyses to the limit (and then some), using terms like “furthermore” and “moreover” while piling up reasons why they were right and others wrong.  As a result, they were unusually confident and likelier to declare things “impossible” or “certain.”  Committed to their conclusions, they were reluctant to change their minds even when their predictions clearly failed.  They would tell us, “Just wait.”

The other group consisted of more pragmatic experts who drew on many analytical tools, with the choice of tool hinging on the particular problem they faced.  These experts gathered as much information from as many sources as they could.  When thinking, they often shifted mental gears, sprinkling their speech with transition markers such as “however,” “but,” “although,” and “on the other hand.”  They talked about possibilities and probabilities, not certainties.  And while no one likes to say “I was wrong,” these experts more readily admitted it and changed their minds.

The first group above are called hedgehogs, while the second group are called foxes.  Hedgehogs know one big thing, while foxes know many things.  Foxes beat hedgehogs on both calibration and resolution.  Moreover, hedgehogs actually did slightly worse than random guessing.

Tetlock compares the hedgehog’s Big Idea to a pair of green-tinted glasses that he never takes off.

So the hedgehog’s one Big Idea doesn’t improve his foresight.  It distorts it.  And more information doesn’t help because it’s all seen through the same tinted glasses.  It may increase the hedgehog’s confidence, but not his accuracy.  That’s a bad combination.  The predictable result?  When hedgehogs in the EPJ research made forecasts on the subjects they knew the most about—their own specialties—their accuracy declined.

Perhaps not surprisingly, the more famous an expert in EPJ was, the less accurate.

That’s not because editors, producers, and the public go looking for bad forecasters.  They go looking for hedgehogs, who just happen to be bad forecasters.  Animated by a Big Idea, hedgehogs tell tight, simple, clear stories that grab and hold audiences… Better still, hedgehogs are confident… The simplicity and confidence of the hedgehog impairs foresight, but it calms nerves—which is good for the careers of hedgehogs.

What about foxes in the media?

Foxes don’t fare so well in the media.  They’re less confident, less likely to say something is “certain” or “impossible,” and are likelier to settle on shades of “maybe.”  And their stories are complex, full of “howevers” and “on the other hands,” because they look at problems one way, then another, and another.  This aggregation of many perspectives is bad TV.  But it’s good forecasting.  Indeed, it’s essential.

 

SUPERFORECASTERS

Tetlock writes:

After invading in 2003, the United States turned Iraq upside down looking for WMDs but found nothing.  It was one of the worst—arguably the worst—intelligence failure in modern history.

The question, however, is not whether the Intelligence Community’s conclusion was correct, but whether it was reasonable on the basis of known information.  Was it reasonable?  Yes.  It sure looked like Saddam was hiding something.  Else why play hide-and-seek with UN arms inspectors that risks triggering an invasion and your own downfall?

It’s difficult to evaluate whether the conclusion was reasonable because, looking back, we know that it was wrong.

This particular bait and switch—replacing “Was it a good decision?” with “Did it have a good outcome?”—is both popular and pernicious.

(Illustration by Alain Lacroix)

Think of it in terms of poker.  A beginner may overestimate his odds, bet big, get lucky and win.  But that doesn’t mean the bet was a good decision.  Similarly, a good poker pro may correctly estimate her odds, bet big, get unlucky and lose.  But that doesn’t mean the bet was a bad decision.

In this case, the evidence seems to show that the conclusion of the Intelligence Community (IC) regarding Iraq’s WMDs was reasonable on the basis of known information, even though it turned out later to be wrong.

However, even though the IC’s conclusion was reasonable, it could have been better because it would have expressed less certainty had all the information been carefully considered.  In other words, the IC would have reached the same conclusion, but it wouldn’t have been associated with a probability so close to 100%.  Tetlock:

The congressional resolution authorizing the use of force might not have passed and the United States might not have invaded.  Stakes rarely get much higher than thousands of lives and trillions of dollars.

The IC didn’t even admit the possibility that they could be wrong about Iraq’s WMDs.  Normally, if there was any doubt at all, you’d have some analysts required to present an opposing view.  But nothing like that happened because the IC was so certain of its conclusion.

In 2006 the Intelligence Advanced Research Projects Activity (IARPA) was created.  Its mission is to fund cutting-edge research with the potential to make the intelligence community smarter and more effective…

In 2008 the Office of the Director of National Intelligence—which sits atop the entire network of sixteen intelligence agencies—asked the National Research Council to form a committee.  The task was to synthesize research on good judgment and help the IC put that research to good use.  By Washington’s standards, it was a bold (or rash) thing to do.  It’s not every day that a bureaucracy pays one of the world’s most respected scientific institutions to produce an objective report that might conclude that the bureaucracy is clueless.

The report delivered was delivered two years later.

“The IC should not rely on analytical methods that violate well-documented behavioral principles or that have no evidence of efficacy beyond their intuitive appeal,” the report noted.  The IC should “rigorously test current and proposed methods under conditions that are as realistic as possible.  Such an evidence-based approach to analysis will promote the continuous learning need to keep the IC smarter and more agile than the nation’s adversaries.”

The IC does a good job teaching its analysts the correct process for doing research and reaching judgments.  The IC also does a good job holding analysts accountable for following the correct process.  However, the IC doesn’t hold analysts accountable for the accuracy of their judgments.  There’s no systematic tracking of the accuracy of judgments.  That’s the biggest problem.  IARPA decided to do something about this.

IARPA would sponsor a massive tournament to see who could invent the best methods of making the sorts of forecasts that intelligence analysts make every day.  Will the president of Tunisia flee to a cushy exile in the next month?  Will an outbreak of H5N1 in China kill more than ten in the next six months?  Will the euro fall below $1.20 in the next twelve months?

[…]

The research teams would compete against one another and an independent control group.  Teams had to beat the combined forecast—the “wisdom of the crowd”—of the control group, and by margins we all saw as intimidating.  In the first year, IARPA wanted teams to beat that standard by 20%—and it wanted that margin of victory to grow to 50% by the fourth year.

But that was only part of IARPA’s plan.  Within each team, researchers could run Archie Cochrane-style experiments to assess what really works against internal control groups.  Researchers might think, for example, that giving forecasters a basic training exercise would improve their accuracy… Give the training to one randomly chosen group of forecasters but not another.  Keep all else constant.  Compare results.

Tetlock put together a team of 3,200 forecasters.  His team was called the Good Judgment Project (GJP).  Tetlock called IARPA’s tournament “gutsy” because of what it could reveal.

Here’s one possible revelation: Imagine you get a couple of hundred ordinary people to forecast geopolitical events.  You see how often they revise their forecasts and how accurate those forecasts prove to be and use that information to identify the forty or so who are the best.  Then you have everyone makes lots more forecasts.  This time, you calculate the average forecast of the whole group—“the wisdom of the crowd”—but with extra weight given to those forty top forecasters.  Then you give the forecast a final tweak: You “extremize” it, meaning you push it closer to 100% or zero.  If the forecast is 70% you might bump it up to, say, 85%.  If it’s 30%, you might reduce it to 15%.

Now imagine that the forecasts you produce this way beat those of every other group and method available, often by large margins.  Your forecasts even beat those of professional intelligence analysts inside the government who have access to classified information—by margins that remain classified.

Think how shocking it would be to the intelligence professionals who have spent their lives forecasting geopolitical events—to be beaten by a few hundred ordinary people and some simple algorithms.

It actually happened.  What I’ve described is the method we used to win IARPA’s tournament.  There is nothing dazzlingly innovative about it.  Even the extremizing tweak is based on a pretty simple insight: When you combine the judgments of a large group of people to calculate the “wisdom of the crowd” you collect all the relevant information that is dispersed among all those people.  But none of those people has access to all that information… What would happen if every one of those people were given all the information?  They would become more confident—raising their forecasts closer to 100% or zero.  If you then calculated the “wisdom of the crowd” it too would be more extreme.  Of course it’s impossible to give every person all the relevant information—so we extremize to simulate what would happen if we could.

Tetlock continues:

Thanks to IARPA, we now know a few hundred ordinary people and some simple math can not only compete with professionals supported by a multibillion-dollar apparatus but also beat them.

And that’s just one of the unsettling revelations IARPA’s decision made possible.  What if the tournament discovered ordinary people who could—without the assistance of any algorithmic magic—beat the IC?  Imagine how threatening that would be.

Tetlock introduces one of the best forecasters on GJP: Doug Lorch, a retired computer programmer who “doesn’t look like a threat to anyone.”  Out of intellectual curiosity, Lorch joined GJP.

Note that in the IARPA tournament, a forecaster could update her forecast in real time.  She may have thought there was a 60% chance some event would happen by the six-month deadline, and then read something convincing her to update her forecast to 75%.  For scoring purposes, each update counts as a separate forecast.  Tetlock:

Over four years, nearly five hundred questions about international affairs were asked of thousands of GJP’s forecasters, generating well over one million judgments about the future.  But even at the individual level, the numbers quickly added up.  In year 1 alone, Doug Lorch made roughly one thousand separate forecasts.

Doug’s accuracy was as impressive as his volume.  At the end of the first year, Doug’s overall Brier score was 0.22, putting him in the fifth spot among the 2,800 competitors in the Good Judgment Project…

In year 2, Doug joined a superforecaster team and did even better, with a final Brier score of 0.14, making him the best forecaster of the 2,800 GJP volunteers.  He also beat by 40% a prediction market in which traders bought and sold futures contracts on the outcomes of the same questions.  He was the only person to beat the extremizing algorithm.  And Doug not only beat the control group’s “wisdom of the crowd,” he surpassed it by more than 60%, meaning that he single-handedly exceeded the fourth-year performance target that IARPA set for multimillion-dollar research programs that were free to use every trick in the forecasting textbook for improving accuracy.

Tetlock points out that Doug Lorch was not uniquely gifted:

There were 58 others among the 2,800 volunteers who scored at the top of the charts in year 1.  They were our first class of superforecasters.  At the end of year 1, their collective Brier score was 0.25, compared with 0.37 for all the other forecasters—and that gap grew in later years so that by the end of the four-year tournament, superforecasters had outperformed regulars by 60%.  Another gauge of how good superforecasters were is how much further they could see into the future.  Across all four years of the tournament, superforecasters looking out three hundred days were more accurate than regular forecasters looking out one hundred days.  In other words, regular forecasters needed to triple their foresight to see as far as superforecasters.

What if the superforecasters’ performance was due to luck?  After all, if you start with 2,800 people flipping coins, some of those people by sheer luck will flip a high proportion of heads.  If luck was a significant factor, then we should expect regression to the mean: the superforecasters in year 1 should perform less well, on the whole, in year 2.  But that didn’t happen.  That doesn’t mean luck isn’t a factor, because it is a factor in many of the questions that were asked.  Tetlock writes:

So we have a mystery.  If chance is playing a significant role, why aren’t we observing significant regression of superforecasters as a whole toward the overall mean?  An offsetting process must be pushing up superforecasters’ performance numbers.  And it’s not hard to guess what that was: after year 1, when the first cohort of superforecasters was identified, we congratulated them, anointed them “super,” and put them on teams with fellow superforecasters.  Instead of regressing toward the mean, their scores got even better.  This suggests that being recognized as “super” and placed on teams of intellectually stimulating colleagues improved their performance enough to erase regression to the mean we would otherwise have seen.  In years 3 and 4, we harvested fresh crops of superforecasters and put them to work in elite teams.  That gave us more apples-to-apples comparisons.  The next cohorts continued to do as well or better than they did in the previous year, again contrary to the regression hypothesis.

That’s not to say there was no regression to the mean.  Each year, roughly 30% of the individual superforecasters fall from the top 2% the next year.  This confirms that luck is a factor.  As Tetlock points out, even superstar athletes occasionally look less than stellar.

 

SUPERSMART?

Tetlock introduces Sanford “Sandy” Sillman.  In 2008, he was diagnosed with multiple sclerosis, which was debilitating.  Walking was difficult.  Even typing was a challenge.  Sandy had to retire from his job as an atmospheric scientist.

How smart is Sandy?  He earned a double major in math and physics from Brown University, plus a master of science degree from MIT’s technology and policy program, along with a second master’s degree, in applied mathematics, from Harvard, and finally a PhD in applied physics from Harvard.  Furthermore, Sandy’s intelligence isn’t confined to math and physics.  He is fluent in French, Russian, Italian, and Spanish.

In year 1 of GJP, Sandy finished with an overall Brier score of 0.19, which put him in a tie for overall champion.

There’s an obvious question about whether superforecasters are simply more knowledgeable and intelligent than others.  Tetlock and Barbara Mellers tested forecasters.  It turns out that although superforecasters have well above average intelligence, they did not score off-the-charts high and most fall well short of so-called genius territory, which if often defined as the top 1%, or an IQ of 135 and up.

Tetlock concludes that knowledge and intelligence help, but they add little beyond a certain threshold.

But having the requisite knowledge and intelligence is not enough.  Many clever and informed forecasters in the tournament fell far short of superforecaster accuracy.  And history is replete with brilliant people who made forecasts that proved considerably less than prescient.

For someone to become a superforecaster, she must develop the right habits of thinking.   Tetlock gives an example.

DAVOS/SWITZERLAND, 01/28/2001 – President of the Palestinian Authority Yasser Arafat.  (Wikimedia Commons)

On October 12, 2004, Yasser Arafat became severely ill with vomiting and abdominal pain.  On November 11, 2004, Arafat was pronounced dead.  There was speculation that he had been poisoned.  In July 2012, scientists at Switzerland’s Lausanne University Institute of Radiation Physics announced that they had tested some of Arafat’s belonging and found high levels of Polonium-210, a radioactive element that can be deadly if ingested.

Two separate agencies, one in France and one in Switzerland, decided to test Arafat’s body.  So IARPA asked forecasters the following question: “Will either the French or Swiss inquiries find elevated levels of polonium in the remains of Yasser Arafat’s body?”

How would someone answer this question?  Most people would follow their hunch about the matter.  Some people might feel that “Israel would never do that!”, while others might feel that “Of course Israel did it!”  Following your hunch is not the right way to approach the question.  How did the superforecaster Bill Flack answer the question?

…Bill asked himself how Arafat’s remains could have been contaminated with enough polonium to trigger a positive result.  Obviously, “Israel poisoned Arafat” was one way.  But because Bill carefully broke the question down, he realized there were others.  Arafat had many Palestinian enemies.  They could have poisoned him.  It was also possible that there had been “intentional postmortem contamination by some Palestinian faction looking to give the appearance that Israel had done a Litvinenko on Arafat,” Bill told me later.  These alternatives mattered because each additional way Arafat’s body could have been contaminated with polonium increased the probability that it was.

What’s the next step?  Before getting to this, Tetlock describes in detail an American family, the Renzettis, who have one child and asks how likely it is that they have a pet.  The first step in answering that question is to learn what percentage of American households have a pet.

Statisticians call that the best rate—how common something is within a broader class.  Daniel Kahneman has a much more evocative visual term for it.  He calls it the “outside view”—in contrast to the “inside view,” which is the specifics of the particular case.  A few minutes with Google tells me about 62% of American households own pets.  That’s the outside view here.  Starting with the outside view means I will start by estimating that there is a 62% chance the Renzettis have a pet.  Then I will turn to the inside view—all those details about the Renzettis—and use them to adjust that initial 62% up or down.

Tetlock comments:

It’s natural to be drawn to the inside view.  It’s usually concrete and filled with engaging detail we can use to craft a story about what’s going on.  The outside view is typically abstract, bare, and doesn’t lend itself so readily to storytelling.  So even smart, accomplished people routinely fail to consider the outside view.

Tetlock writes:

Here we have a famous person who is dead.  Major investigative bodies think there is enough reason for suspicion that they are exhuming the body.  Under those circumstances, how often would the investigation turn up evidence of poisoning?  I don’t know and there is no way to find out.  But I do know there is at least a prima facie case that persuades courts and medical investigators that this is worth looking into.  It has to be considerably above zero.  So let’s say it’s at least 20%.  But the probability can’t be 100% because if it were that clear and certain the evidence would have been uncovered before burial.  So let’s so the probability cannot be higher than 80%.  That’s a big range.  The midpoint is 50%.  So that outside view can serve as our starting point.

Someone might wonder why you couldn’t start with the inside view and then consider the outside view.  This wouldn’t work, however, due to anchoring.  When we make estimates, we tend to start with some number and adjust.  If we start with the outside view, we have reasonable starting point, but if we start with the inside view, we may end up anchoring on a number that is not a reasonable starting point.

Having established the outside view on the Arafat question, we next turn to the inside view.  What are the hypotheses?  Israel could have poisoned Arafat.  Arafat’s Palestinian enemies could have poisoned him.  Arafat’s remains could have been contaminated to make it look like he was poisoned.  Tetlock:

Start with the first hypothesis: Israel poisoned Yassar Arafat with polonium.  What would it take for that to be true?

    • Israel had, or could obtain, polonium.
    • Israel wanted Arafat dead badly enough to take a big risk.
    • Israel had the ability to poison Arafar with polonium.

Each of these elements could then be researched—looking for evidence pro and con—to get a sense of how likely they are to be true, and therefore how likely the hypothesis is to be true.  Then it’s on to the next hypothesis.  And the next.

This sounds like detective work because it is—or to be precise, it is detective work as real investigators do it, not the detectives on TV shows.  It’s methodical, slow, and demanding.

Tetlock concludes:

A brilliant puzzle solver may have the raw material for forecasting, but if he doesn’t also have an appetite for questioning basic, emotionally charged beliefs he will often be at a disadvantage relative to a less intelligent person who has a greater capacity for self-critical thinking.  It’s not the raw crunching power you have that matters most.  It’s what you do with it.

[…]

For superforecasters, beliefs are hypotheses to be tested, not treasures to be guarded.

 

SUPERQUANTS?

Perhaps superforecasters are exceptionally good using math to make their forecasts?  Although occasionally a superforecaster will consult a mathematical model, the vast majority of the time a superforecaster uses very little math.

That said, superforecasters tend to be granular in their probability estimates.  Is this justified?  Tetlock:

So how can we know that the granularity we see among superforecasters is meaningful?… The answer lies in the tournament data.  Barbara Mellers has shown that granularity predicts accuracy: the average forecaster who sticks with the tens—20%, 30%, 40%—is less accurate than the finer-grained forecaster who uses fives—20%, 25%, 30%—and still less accurate than the even finer-grained forecaster who uses ones—20%, 21%, 22%.

 

SUPERNEWSJUNKIES?

Tetlock:

Superforecasting isn’t a paint-by-numbers method but superforecasters often tackle questions in a roughly similar way—one that any of us can follow: Unpack the question into components.  Distinguish as sharply as you can between the known and unknown and leave no assumptions unscrutinized.  Adopt the outside view and put the problem into a comparative perspective that downplays its uniqueness and treats it as a special case of a wider class of phenomena.  Then adopt the inside view that plays up the uniqueness of the problem.  Also explore the similarities and differences between your views and those of others—and pay special attention to prediction markets and other methods of extracting wisdom from crowds.  Synthesize all these different views into a single vision as acute as that of a dragonfly.  Finally, express your judgment as precisely as you can, using a finely grained scale of probability.

This is just the beginning.  The next step, which is typically repeated many times, is to update your predictions as you get new information.

Photo by Marek Uliasz.

Superforecasters update their forecasts much more frequently than regular forecasters.  One might think that superforecasters are super because they are news junkies.  However, their initial forecasts were at least 50% more accurate than those of regular forecasters.  Furthermore, properly updating forecasts on the basis of new information requires the same skills used in making the initial forecasts.   Tetlock:

…there are two dangers a forecaster faces after making the initial call.  One is not giving enough weight to new information.  That’s underreaction.  The other danger is overreacting to new information, seeing it as more meaningful than it is, and adjusting a forecast too radically.

Both under- and overreaction can diminish accuracy.  Both can also, in extreme cases, destroy a perfectly good forecast.

One typical reason for underreaction to new information is that people often become committed to their beliefs.  Especially when they have publicly committed to their beliefs and when the have an ego investment.  Superforecasters, by contrast, have no trouble changing their minds on the basis of new information.

While superforecasters update their forecasts much more often than regular forecasters, they do so in small increments.  They tend not to overreact.

 

PERPETUAL BETA

Superforecasters have a growth mindset.  They believe they can get better with work, and they do.

(Illustration by Tereza Paskova)

Tetlock notes that John Maynard Keynes failed twice as an investor, and was almost wiped out during the first failure, before he settled on a value investing approach.  Keynes came to understand value investing through his own reflection.  He didn’t learn about it from Ben Graham, who is regarded as the father of value investing.  In any case, Keynes turned out to be enormously successful as an investor despite investing during the Great Depression of the 1930s.

Keynes had a growth mindset: try, fail, analyze, adjust, try again.

Improving requires getting good feedback.  Meteorologists get feedback on whether their forecasts were correct on a daily basis.  Bridge players also get fairly immediate feedback on how well they’re playing.  The trouble for forecasters is twofold:  first, the forecasts must be specific enough to be testable; and second, the time lag between the forecast and the result is often long, unlike for meteorologists or bridge players.

Pulling It All Together—Portrait of a Superforecaster

In philosophic outlook, they tend to be:

    • Cautious: Nothing is certain.
    • Humble: Reality is infinitely complex.
    • Nondeterministic: What happens is not meant to be and does not have to happen.

In their abilities and thinking styles, they tend to be:

    • Actively Open-minded: Beliefs are hypotheses to be tested, not treasures to be protected.
    • Intelligent and Knowledgeable, with a “Need for Cognition”: Intellectually curious, enjoy puzzles and mental challenges.
    • Reflective: Introspective and self-critical.
    • Numerate: Comfortable with numbers.

In their methods of forecasting, they tend to be:

    • Pragmatic: Not wedded to any idea or agenda.
    • Analytical: Capable of stepping back from the tip-of-your-nose perspective and considering other views.
    • Dragonfly-eyed: Value diverse views and synthesize them into their own.
    • Probabilistic: Judge using many grades of maybe.
    • Thoughtful Updaters: When facts change, they change their minds.
    • Good Intuitive Psychologists: Aware of the value of checking thinking for cognitive and emotional biases.

In their work ethic, they tend to have:

    • A Growth Mindset: Believe it’s possible to get better.
    • Grit: Determined to keep at it however long it takes.

Tetlock concludes by noting that the strongest predictor of a forecaster becoming a superforecaster is perpetual beta, the degree to which one is committed to belief updating and self-improvement.  Perpetual beta is three times as powerful a predictor as its closet rival, intelligence.

 

SUPERTEAMS

(Photo by Chrisharvey)

Tetlock writes:

[Groups] let people share information and perspectives.  That’s good.  It helps make dragonfly eye work, and aggregation is critical to accuracy.  Of course aggregation can only do its magic when people form judgments independently, like the fairgoers guessing the weight of the ox.  The independence of judgments ensures that errors are more or less random, so they cancel each other out.  When people gather and discuss in a group, independence of thought and expression can be lost.  Maybe one person is a loudmouth who dominates the discussion, or a bully, or a superficially impressive talker, or someone with credentials that cow others into line.  In so many ways, a group can get people to abandon independent judgment and buy into errors.  When that happens, the mistakes will pile up, not cancel out.

The GJP randomly assigned several hundred forecasters to work alone and several hundred to work in teams.  At the end of the first year, the teams had been 23% more accurate than the individuals.  GJP kept experimenting.

The results speak for themselves.  On average, when a forecaster did well enough in year 1 to become a superforecaster, and was put on a superforecaster team in year 2, that person became 50% more accurate.  An analysis in year 3 got the same result.  Given that these were collections of strangers tenuously connected in cyberspace, we found that result startling.

Tetlock adds:

How did superteams do so well?  By avoiding the extremes of groupthink and Internet flame wars.  And by fostering minicultures that encouraged people to challenge each other respectfully, admit ignorance, and request help.

 

THE LEADER’S DILEMMA

Good leaders have to be confident and decisive.  But good leaders also need good forecasts in order to make good decisions.  We’ve seen that effective forecasting requires self-critical questioning and doubt.  How can a leader be both a forecaster and a leader?  Tetlock:

Fortunately, the contradiction between being a superforecaster and a superleader is more apparent than real.  In fact, the superforecaster model can help make good leaders superb and the organizations they lead smart, adaptable, and effective.  The key is an approach to leadership and organization first articulated by a nineteenth-century Prussian general, perfected by the German army of World War II, made foundational doctrine by the modern American military, and deployed by many successful corporations today.  You might even find it at your neighborhood Walmart.

Helmuth von Moltke the Elder (1800-1891). Photo by Georgios Kollidas.

Helmuth von Moltke was a famous nineteenth-century Prussian general.  One of Moltke’s axioms is: “In war, everything is uncertain.”  While having a plan is important, you can never entirely trust your plan.  Moltke: “No plan of operations extends with certainty beyond the first encounter with the enemy’s main strength.”  Moltke also wrote that, “It is impossible to lay down binding rules” that apply in all circumstances.  “Two cases will never be exactly the same” in war.  Improvisation is essential.  Tetlock:

Moltke trusted that his officers were up to the task… In Germany’s war academies, scenarios were laid out and students were invited to suggest solutions and discuss them collectively.  Disagreement was not only permitted, it was expected, and even the instructor’s views could be challenged… Even the views of generals were subject to scrutiny.

Tetlock continues:

What ties all of this together… is the command principle of Auftragstaktik.  Usually translated today as “mission command,” the basic idea is simple. “War cannot be conducted from the green table,” Moltke wrote, using an expression that referred to top commanders at headquarters.  “Frequent and rapid decisions can be shaped only on the spot according to estimates of local conditions.”  Decision-making power must be pushed down the hierarchy so that those on the ground—the first to encounter surprises on the evolving battlefield—can respond quickly.  Of course those on the ground don’t see the bigger picture.  If they made strategic decisions the army would lose coherence and become a collection of tiny units, each seeking its own ends.  Auftragstaktik blended strategic coherence and decentralized decision making with a simple principle: commanders were to tell subordinates what their goal is but not how to achieve it.

 

ARE THEY REALLY SO SUPER?

Are the superforecasters super primarily due to innate abilities or primarily due to specific habits they’ve developed?   Tetlock answers:

They score higher than average on measures of intelligence and open-mindedness, although they are not off the charts.  What makes them so good is less what they are than what they do—the hard work of research, the careful thought and self-criticism, the gathering and synthesizing of other perspectives, the granular judgments and relentless updating.

Tetlock again:

My sense is that some superforecasters are so well practiced in System 2 corrections—such as stepping back to take the outside view—that these techniques have become habitual.  In effect, they are now part of their System 1… No matter how physically or cognitively demanding a task may be—cooking, sailing, surgery, operatic singing, flying fighter jets—deliberative practice can make it second nature.

Very little is predictable five or more years into the future.  Tetlock confirmed this fact in his EPJ research: Expert predictions declined towards chance five years out.  And yet governments need to make plans that extend five or more years into the future.  Tetlock comments:

Probability judgments should be explicit so we can consider whether they are as accurate as they can be.  And if they are nothing but a guess, because that’s the best we can do, we should say so.  Knowing what we don’t know is better than thinking we know what we don’t.

Tetlock adds:

Kahneman and other pioneers of modern psychology have revealed that our minds crave certainty and when they don’t find it, they impose it.  In forecasting, hindsight bias is the cardinal sin.  Recall how experts stunned by the Gorbachev surprise quickly became convinced it was perfectly explicable, even predictable, although they hadn’t predicted it.

Brushing off surprises makes the past look more predictable than it was—and this encourages the belief that the future is much more predictable that it is.

Tetlock concludes:

Savoring how history could have generated an infinite array of alternative outcomes and could now generate a similar array of alternative futures, is like contemplating the one hundred billion known stars in our galaxy and the one hundred billion known galaxies.  It instills profound humility.

…But I also believe that humility should not obscure the fact that people can, with considerable effort, make accurate forecasts about at least some developments that really do matter.

 

WHAT’S NEXT?

Tetlock hopes that the forecasting lessons discussed in his book will be widely adopted:

Consumer of forecasts will stop being gulled by pundits with good stories and start asking pundits how their past predictions fared—and reject answers that consist of nothing but anecdotes and credentials.  Just as we now expect a pill to have been tested in peer-reviewed experiments before we swallow it, we will expect forecasters to establish the accuracy of their forecasting with rigorous testing before we heed their advice.  And forecasters themselves will realize… that these higher expectations will ultimately benefit them, because it is only with the clear feedback that comes from rigorous testing that they can improve their foresight.  It could be huge—an “evidence-based forecasting” revolution similar to the “evidence-based medicine” revolution, with consequences every bit as significant.

Or nothing may change.  Revolutionaries aren’t supposed to say failure is possible, but let’s think like superforecasters here and acknowledge that things may go either way.

Change

Tetlock writes about a Boston doctor named Ernest Amory Codman who proposed an idea he called the End Result System.

Hospitals should record what ailments incoming patients had, how they were treated, and—most important—the end result of each case.  These records should be compiled and statistics released so consumers could choose hospitals on the basis of good evidence.  Hospitals would respond to consumer pressure by hiring and promoting doctors on the same basis.  Medicine would improve, to the benefit of all.

Today, hospitals do much of what Codman suggested, and physicians have embraced such measurement.  But when Codman first put forth his idea, the medical establishment rejected it.  Hospitals didn’t want to pay for record keepers.  And doctors were already respected.  Keeping score could only hurt their reputations.   Codman was fired from Massachusetts General Hospital.  And Codman lost his teaching post at Harvard.  Eventually, however, his core idea was accepted.

Other areas of society are following the example of evidence-based medicine.  There’s evidence-based government policy.  There’s evidence-based philanthropy, led by the Gates Foundation.  There’s evidence-based management of sports teams.

One possible criticism of superforecasting is that it doesn’t deal with big enough questions.  Tetlock responds that a lot of smaller questions can ultimately shed light on bigger questions.  He calls this Bayesian question clustering.  For instance, in considering a big question like whether there will be another Korean war, you can focus on smaller questions related to missile launches, nuclear tests, cyber attacks, and artillery shelling.  The answers are cumulative.  The more yeses, the more likely the overall situation will end badly.

It’s interesting that hedgehog-minded experts are often good at coming up with big questions even though they’re usually not good at making forecasts.  Tetlock writes:

While we may assume that a superforecaster would also be a superquestioner, we don’t actually know that.  Indeed, my best scientific guess is that they often are not.  The psychological recipe for the ideal superforecaster may prove to be quite different from that for the ideal superquestioner, as superb question generation often seems to accompany a hedgehog-like incisiveness and confidence that one has a Big Idea grasp of the deep drivers of an event.  That’s quite a different mindset from the foxy eclecticism and sensitivity to uncertainty that characterizes superb forecasting.

Tetlock continues:

Superforecasters and superquestioners need to acknowledge each other’s complementary strengths, not dwell on each other’s alleged weaknesses.  Friedman poses provocative questions that superforecasters should use to sharpen their foresight; superforecasters generate well-calibrated answers that superquestioners should use to fine-tune and occasionally overhaul their mental models of reality…

…But there’s a much bigger collaboration I’d like to see.  It would be the Holy Grail of my research program: using forecasting tournaments to depolarize unnecessarily polarized policy debates and make us collectively smarter.

 

TEN COMMANDMENTS FOR ASPIRING SUPERFORECASTERS

Triage.

Focus on questions where your hard work is likely to pay off.  Don’t waste time either on easy “clocklike” questions (where simple rules of thumb can get you close to the right answer) or on impenetrable “cloud-like” questions (where even fancy statistical models can’t beat the dart-throwing chimp).  Concentrate on questions in the Goldilocks zone of difficulty, where effort pays off the most.

Break seemingly intractable problems into tractable sub-problems.

Decompose the problem into its knowable and unknowable parts.  Flush ignorance into the open.  Expose and examine your assumptions.  Dare to be wrong by making your best guesses.  Better to discover errors quickly than to hide them behind vague verbiage… The surprise is now often remarkably good probability estimates arise from a remarkably crude series of assumptions and guesstimates.

Strike the right balance between inside and outside views.

Superforecasters know that there is nothing new under the sun.  Nothing is 100% “unique.”… Superforecasters are in the habit of posing the outside-view question: How often do things of this sort happen in situations of this sort?

Strike the right balance between under- and overreacting to evidence.

The best forecasters tend to be incremental belief updaters, often moving from probabilities of, say, 0.4 to 0.35 or from 0.6 to 0.65, distinctions too subtle to capture with vague verbiage, like “might” or “maybe,” but distinctions that, in the long run, define the difference between good and great forecasters.

Yet superforecasters also know how to jump, to move their probability estimates fast in response to diagnostic signals.  Superforecasters are not perfect Bayesian updaters but they are better than most of us.  And that is largely because they value this skill and work hard at cultivating it.

Look for the clashing causal forces at work in each problem.

For every good policy argument, there is typically a counterargument that is at least worth acknowledging.  For instance, if you are a devout dove who believes that threatening military action never brings peace, be open to the possibility that you might be wrong about Iran.  And the same advice applies if you are a devout hawk who believes that soft “appeasement” policies never pay off.  Each side should list, in advance, the signs that would nudge them toward the other.

Now here comes the really hard part.  In classical dialectics, thesis meets antithesis, producing synthesis.  In dragonfly eye, one view meets another and another and another—all of which must be synthesized into a single image.  There are no paint-by-number rules here.  Synthesis is an art that requires reconciling irreducibly subjective judgments.  If you do it well, engaging in this process of synthesizing should transform you from a cookie-cutter dove or hawk into an odd hybrid creature, a dove-hawk, with a nuanced view of when tougher or softer policies are likelier to work.

Strive to distinguish as many degrees of doubt as the problem permits but no more.

Few things are either certain or impossible.  And “maybe” isn’t all that informative.  So your uncertainty dial needs more than three settings.  Nuance matters.  The more degrees of uncertainty you can distinguish, the better a forecaster you are likely to be.  As in poker, you have an advantage if you are better than your competitors at separating 60/40 bets from 40/60—or 55/45 from 45/55.  Translating vague-verbiage hunches into numeric probabilities feels unnatural at first but it can be done.  It just requires patience and practice.  The superforecasters have shown what is possible.

Strike the right balance between under- and overconfidence, between prudence and decisiveness.

Superforecasters understand the risks both of rushing to judgment and of dawdling too long near “maybe.”…They realize that long-term accuracy requires getting good scores on both calibration and resolution… It is not enough just to avoid the most recent mistake.  They have to find creative ways to tamp down both types of forecasting errors—misses and false alarms—to the degree a fickle world permits such uncontroversial improvements in accuracy.

Look for the errors behind your mistakes but beware of rearview-mirror hindsight biases.

Don’t try to justify or excuse your failures.  Own them!  Conduct unflinching postmortems: Where exactly did I go wrong?  And remember that although the more common error is to learn too little from failure and to overlook flaws in your basic assumptions, it is also possible to learn too much (you may have been basically on the right track but made a minor technical mistake that had big ramifications).  Also don’t forget to do postmortems on your successes too.  Not all successes imply that your reasoning was right.  You may have just lucked out by making offsetting errors.  And if you keep confidently reasoning along the same lines, you are setting yourself up for a nasty surprise.

Bring out the best in others and let others bring out the best in you.

Master the fine arts of team management, especially perspective taking (understanding the arguments of the other side so well that you can reproduce them to the other’s satisfaction), precision questioning (helping others to clarify their arguments so they are not misunderstood), and constructive confrontation (learning to disagree without being disagreeable).  Wise leaders know how fine the line can be between a helpful suggestion and micromanagerial meddling or between a rigid group and a decisive one or between a scatterbrained group and an open-minded one.

Master the error-balancing bicycle.

Implementing each commandment requires balancing opposing errors… Learning requires doing, with good feedback that leaves no ambiguity about whether you are succeeding… or whether you are failing… Also remember that practice is not just going through the motions of making forecasts, or casually reading the news and tossing out probabilities.  Like all other known forms of expertise, superforecasting is the product of deep, deliberative practice.

Don’t treat commandments as commandments.

Guidelines are the best we can do in a world where nothing is certain or exactly repeatable.  Superforecasting requires constant mindfulness, even when—perhaps especially when—you are dutifully trying to follow these commandments.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

Seeking Wisdom

August 21, 2022

In his pursuit of wisdom, Peter Bevelin was inspired by Charlie Munger’s idea:

I believe in the discipline of mastering the best of what other people have ever figured out.

Bevelin was also influenced by Munger’s statement that Charles Darwin was one of the best thinkers who ever lived.  Despite the fact that many others had much higher IQ’s.  Bevelin:

Darwin’s lesson is that even people who aren’t geniuses can outthink the rest of mankind if they develop certain thinking habits.

(Photo by Maull and Polyblank (1855), via Wikimedia Commons)

In the spirit of Darwin and Munger, and with the goal of gaining a better understanding of human behavior, Bevelin read books in biology, psychology, neuroscience, physics, and mathematics.  Bevelin took extensive notes.  The result is the book, Seeking Wisdom: From Darwin to Munger.

Here’s the outline:

PART ONE:  WHAT INFLUENCES OUR THINKING

  • Our anatomy sets the limits for our behavior
  • Evolution selected the connections  that produce useful behavior for survival and reproduction
  • Adaptive behavior for survival and reproduction

PART TWO:  THE PSYCHOLOGY OF MISJUDGMENTS

  • Misjudgments explained by psychology
  • Psychological reasons for mistakes

PART THREE:  THE PHYSICS AND MATHEMATICS OF MISJUDGMENTS

  • Systems thinking
  • Scale and limits
  • Causes
  • Numbers and their meaning
  • Probabilities and number of possible outcomes
  • Scenarios
  • Coincidences and miracles
  • Reliability of case evidence
  • Misrepresentative evidence

PART FOUR:  GUIDELINES TO BETTER THINKING

  • Models of reality
  • Meaning
  • Simplification
  • Rules and filters
  • Goals
  • Alternatives
  • Consequences
  • Quantification
  • Evidence
  • Backward thinking
  • Risk
  • Attitudes

(Photo by Nick Webb)

 

Part One:  What Influences Our Thinking

OUR ANATOMY SETS THE LIMITS FOR OUR BEHAVIOR

Bevelin quotes Nobel Laureate Dr. Gerald Edelman:

The brain is the most complicated material object in the known universe.  If you attempted to count the number of connections, one per second, in the mantle of the brain (the cerebral cortex), you would finish counting 32 million years later.  But that is not the whole story.  The way the brain is connected—its neuroanatomical pattern—is enormously intricate.  Within this anatomy a remarkable set of dynamic events take place in hundredths of a second and the number of levels controlling these events, from molecules to behavior, is quite large.

Neurons can send signals—electrochemical pulses—to specific target cells over long distances.  These signals are sent by axons, thin fibers that extend from neurons to other parts of the brain.  Axons can be quite long.

(Illustration by ustas)

Some neurons emit electrochemical pulses constantly while other neurons are quiet most of the time.  A single axon can have several thousand synaptic connections.  When an electrochemical pulse travels along an axon and reaches a synapse, it causes a neurotransmitter (a chemical) to be released.

The human brain contains approximately 100 trillion synapses.  From wikipedia:

The functions of these synapses are very diverse: some are excitatory (exciting the target cell); others are inhibitory; others work by activating second messenger systems that change the internal chemistry of their target cells in complex ways.  A large number of synapses are dynamically modifiable; that is, they are capable of changing strength in a way that is controlled by the patterns of signals that pass through them.  It is widely believed that activity-dependent modification of synapses is the brain’s primary mechanism for learning and memory.

Most of the space in the brain is taken up by axons, which are often bundled together in what are called nerve fiber tracts.  A myelinated axon is wrapped in a fatty insulating sheath of myelin, which serves to greatly increase the speed of signal propagation.  (There are also unmyelinated axons).  Myelin is white, making parts of the brain filled exclusively with nerve fibers appear as light-colored white matter, in contrast to the darker-colored grey matter that marks areas with high densities of neuron cell bodies.

Genes, life experiences, and randomness determine how neurons connect.

Also, everything that happens in the brain involves many areas at once (the left brain versus right brain distinction is not strictly accurate).  This is part of why the brain is so flexible.  There are different ways for the brain to achieve the same result.

 

EVOLUTION SELECTED THE CONNECTIONS THAT PRODUCE USEFUL BEHAVIOR FOR SURVIVAL AND REPRODUCTION

Bevelin writes:

If certain connections help us interact with our environment, we use them more often than connections that don’t help us.  Since we use them more often, they become strengthened.

Evolution has given us preferences that help us classify what is good or bad.  When these values are satisfied (causing either pleasure or less pain) through the interaction with our environment, these neural connections are strengthened.  These values are reinforced over time because they give humans advantages for survival and reproduction in dealing with their environment.

(Illustration by goce risteski)

If a certain behavior is rewarding, the neural connections associated with that behavior get strengthened.  The next time the same situation is encountered, we feel motivated to respond in the way that we’ve learned brings pleasure (or reduces pain).  Bevelin:

We do things that we associate with pleasure and avoid things that we associate with pain.

 

ADAPTIVE BEHAVIOR FOR SURVIVAL AND REPRODUCTION

Bevelin:

The consequences of our actions reinforce certain behavior.  If the consequences were rewarding, our behavior is likely to be repeated.  What we consider rewarding is individual specific.  Rewards can be anything from health, money, job, reputation, family, status, or power.  In all of these activities, we do what works.  This is how we adapt.  The environment selects our future behavior.

Illustration by kalpis

Especially in a random environment like the stock market, it can be difficult to figure out what works and what doesn’t.  We may make a good decision based on the odds, but get a poor outcome.  Or we may make a bad decision based on the odds, but get a good outcome.  Only over the course of many decisions can we tell if our investment process is probably working.

 

Part Two:  The Psychology of Misjudgments

Bevelin quotes the Greek philosopher and orator, Dio Chrysostom:

“Why oh why are human beings so hard to teach, but so easy to deceive.”

MISJUDGMENTS EXPLAINED BY PSYCHOLOGY

Illustration by intheskies

Bevelin lists 28 reasons for misjudgments and mistakes:

  1. Bias from mere association—automatically connecting a stimulus with pain or pleasure; including liking or disliking something associated with something bad or good.  Includes seeing situations as identical because they seem similar.  Also bias from Persian Messenger Syndrome—not wanting to be the carrier of bad news.
  2. Underestimating the power of incentives (rewards and punishment)—people repeat actions that result in rewards and avoid actions that they are punished for.
  3. Underestimating bias from own self-interest and incentives.
  4. Self-serving bias—overly positive view of our abilities and future.  Includes over-optimism.
  5. Self-deception and denial—distortion of reality to reduce pain or increase pleasure.  Includes wishful thinking.
  6. Bias from consistency tendency—being consistent with our prior commitments and ideas even when acting against our best interest or in the face of disconfirming evidence.  Includes Confirmation Bias—looking for evidence that confirms our actions and beliefs and ignoring or distorting disconfirming evidence.
  7. Bias from deprival syndrome—strongly reacting (including desiring and valuing more) when something we like and have (or almost have) is (or threatens to be) taken away or “lost.”  Includes desiring and valuing more what we can’t have or what is (or threatens to be) less available.
  8. Status quo bias and do-nothing syndrome—keeping things the way they are.  Includes minimizing effort and a preference for default options.
  9. Impatience—valuing the present more highly than the future.
  10. Bias from envy and jealousy.
  11. Distortion by contrast comparison—judging and perceiving the absolute magnitude of something not by itself but based only on its difference to something else presented closely in time or space or to some earlier adaptation level.  Also underestimating the consequences over time of gradual changes.
  12. The anchoring effect—People tend to use any random number as a baseline for estimating an unknown quantity, despite the fact that the unknown quantity is totally unrelated to the random number.  (People also overweigh initial information that is non-quantitative.)
  13. Over-influence by vivid or the most recent information.
  14. Omission and abstract blindness—only seeing stimuli we encounter or that grabs our attention, and neglecting important missing information or the abstract.  Includes inattentional blindness.
  15. Bias from reciprocation tendency—repaying in kind what others have done for or to us like favors, concessions, information, and attitudes.
  16. Bias from over-influence by liking tendency—believing, trusting, and agreeing with people we know and like.  Includes bias from over-desire for liking and social acceptance and for avoiding social disapproval.  Also bias from disliking—our tendency to avoid and disagree with people we don’t like.
  17. Bias from over-influence by social proof—imitating the behavior of many others or similar others.  Includes crowd folly.
  18. Bias from over-influence by authority—trusting and obeying a perceived authority or expert.
  19. The Narrative Fallacy (Bevelin uses the term “Sensemaking”)—constructing explanations that fit an outcome.  Includes being too quick in drawing conclusions.  Also Hindsight Bias: Thinking events that have happened were more predictable than they were.
  20. Reason-respecting—complying with requests merely because we’ve been given a reason.  Includes underestimating the power in giving people reasons.
  21. Believing first and doubting later—believing what is not true, especially when distracted.
  22. Memory limitations—remembering selectively and wrong.  Includes influence by suggestions.
  23. Do-something syndrome—acting without a sensible reason.
  24. Mental confusion from say-something syndrome—feeling a need to say something when we have nothing to say.
  25. Emotional arousal—making hasty judgments under the influence of intense emotions.  Includes exaggerating the emotional impact of future events.
  26. Mental confusion from stress.
  27. Mental confusion from physical or psychological pain, and the influence of chemicals or diseases.
  28. Bias from over-influence by the combined effect of many psychological tendencies operating together.

 

PSYCHOLOGICAL REASONS FOR MISTAKES

Bevelin notes that his explanations for the 28 reasons for misjudgments is based on work by Charles Munger, Robert Cialdini, Richard Thaler, Robyn Dawes, Daniel Gilbert, Daniel Kahneman, and Amos Tversky.  All are psychologists except for Thaler (economist) and Munger (investor).

1. Mere Association

Bevelin:

Association can influence the immune system.  One experiment studied food aversion in mice.  Mice got saccharin-flavored water (saccharin has incentive value due to its sweet taste) along with a nausea-producing drug.  Would the mice show signs of nausea the next time they got saccharin water alone?  Yes, but the mice also developed infections.  It was known that the drug in addition to producing nausea, weakened the immune system, but why would saccharin alone have this effect?  The mere paring of the saccharin with the drug caused the mouse immune system to learn the association.  Therefore, every time the mouse encountered the saccharin, its immune system weakened making the mouse more vulnerable to infections.

If someone brings us bad news, we tend to associate that person with the bad news—and dislike them—even if the person didn’t cause the bad news.

2. Incentives (Reward and Punishment)

Incentives are extremely important.   Charlie Munger:

I think I’ve been in the top 5% of my age cohort all my life in understanding the power of incentives, and all my life I’ve underestimated it.  Never a year passes that I don’t get some surprise that pushes my limit a little farther.

Munger again:

From all business, my favorite case on incentives is Federal Express.  The heart and soul of their system—which creates the integrity of the product—is having all their airplanes come to one place in the middle of the night and shift all the packages from plane to plane.  If there are delays, the whole operation can’t deliver a product full of integrity to Federal Express customers.  And it was always screwed up.  They could never get it done on time.  They tried everything—moral suasion, threats, you name it.  And nothing worked.  Finally, somebody got the idea to pay all these people not so much an hour, but so much a shift—and when it’s all done, they can all go home.  Well, their problems cleared up over night.

People can learn the wrong incentives in a random environment like the stock market.  A good decision based on the odds may yield a bad result, while a bad decision based on the odds may yield a good result.  People tend to become overly optimistic after a success (even if it was good luck) and overly pessimistic after a failure (even if it was bad luck).

3. Self-interest and Incentives

“Never ask the barber if you need a haircut.”

Munger has commented that commissioned sales people, consultants, and lawyers have a tendency to serve the transaction rather than the truth.  Many others—including bankers and doctors—are in the same category.  Bevelin quotes the American actor Walter Matthau:

“My doctor gave me six months to live.  When I told him I couldn’t pay the bill, he gave me six more months.”

If they make unprofitable loans, bankers may be rewarded for many years while the consequences of the bad loans may not occur for a long time.

When designing a system, careful attention must be paid to incentives.  Bevelin notes that a new program was put in place in New Orleans: districts that showed improvement in crime statistics would receive rewards, while districts that didn’t faced cutbacks and firings.  As a result, in one district, nearly half of all serious crimes were re-classified as minor offences and never fully investigated.

4. Self-serving Tendencies and Overoptimism 

We tend to overestimate our abilities and future prospects when we are knowledgeable on a subject, feel in control, or after we’ve been successful.

Bevelin again:

When we fail, we blame external circumstances or bad luck.  When others are successful, we tend to credit their success to luck and blame their failures on foolishness.  When our investments turn into losers, we had bad luck.  When they turn into winners, we are geniuses.  This way we draw the wrong conclusions and don’t learn from our mistakes.  We also underestimate luck and randomness in outcomes.

5. Self-deception and Denial

Munger likes to quote Demosthenes:

Nothing is easier than self-deceit.  For what each man wishes, that he also believes to be true.

People have a strong tendency to believe what they want to believe.  People prefer comforting illusions to painful truths.

Richard Feynman:

The first principle is that you must not fool yourself—and you are the easiest person to fool.

6. Consistency

Bevelin:

Once we’ve made a commitment—a promise, a choice, taken a stand, invested time, money, or effort—we want to remain consistent.  We want to feel that we’ve made the right decision.  And the more we have invested in our behavior the harder it is to change.

The more time, money, effort, and pain we invest in something, the more difficulty we have at recognizing a mistaken commitment.  We don’t want to face the prospect of a big mistake.

For instance, as the Vietnam War became more and more a colossal mistake, key leaders found it more and more difficult to recognize the mistake and walk away.  The U.S. could have walked away years earlier than it did, which would have saved a great deal of money and thousands of lives.

Bevelin quotes Warren Buffett:

What the human being is best at doing is interpreting all new information so that their prior conclusions remain intact.

Even scientists, whose job is to be as objective as possible, have a hard time changing their minds after they’ve accepted the existing theory for a long time.  Physicist Max Planck:

A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die and a new generation grows up that is familiar with it.

7. Deprival Syndrome

Bevelin:

When something we like is (or threatens to be) taken away, we often value it higher.  Take away people’s freedom, status, reputation, money, or anything they value, and they get upset… The more we like what is taken away or the larger the commitment we’ve made, the more upset we become.  This can create hatreds, revolts, violence, and retaliations.

Fearing deprival, people will be overly conservative or will engage in cover-ups.

A good value investor is wrong roughly 40 percent of the time.  However, due to deprival syndrome and loss aversion—the pain of a loss is about 2 to 2.5 times greater than the pleasure of an equivalent gain—investors have a hard time admitting their mistakes and moving on.  Admitting a mistake means accepting a loss of money and also recognizing our own fallibility.

Furthermore, deprival syndrome makes us keep trying something if we’ve just experienced a series of near misses.  We feel that “we were so close” to getting some reward that we can’t give up now, even if the reward may not be worth the expected cost.

Finally, the harder it is to get something, the  more value we tend to place on it.

8. Status Quo and Do-Nothing Syndrome

We feel worse about a harm or loss if it results from our action than if it results from our inaction.  We prefer the default option—what is selected automatically unless we change it.  However, as Bevelin points out, doing nothing is still a decision and the cost of doing nothing could be greater than the cost of taking an action.

In countries where being an organ donor is the default choice, people strongly prefer to be organ donors.  But in countries where not being an organ donor is the default choice, people prefer not to be organ donors.  In each case, most people simply go with the default option—the status quo.  But society is better off if most people are organ donors.

9. Impatience

We value the present more than the future.  We often seek pleasure today at the cost of a potentially better future.  It’s important to understand that pain and sacrifice today—if done for the right reasons—can lead to greater happiness in the future.

10. Envy and Jealousy

Charlie Munger and Warren Buffett often point out that envy is a stupid sin because—unlike other sins like gluttony—there’s no upside.  Also, jealousy is among the top three motives for murder.

It’s best to set goals and work towards them without comparing ourselves to others.  Partly by chance, there are always some people doing better and some people doing worse.

11. Contrast Comparison

The classic demonstration of contrast comparison is to stick one hand in cold water and the other hand in warm water.  Then put both hands in a buck with room temperature water.  Your cold hand will feel warm while your warm hand will feel cold.

Bevelin writes:

We judge stimuli by differences and changes and not absolute magnitudes.  For example, we evaluate stimuli like temperature, loudness, brightness, health, status, or prices based on their contrast or difference from a reference point (the prior or concurrent stimuli or what we have become used to).  This reference point changes with new experiences and context.

How we value things depends on what we compare them with.

Salespeople, after selling the main item, often try to sell add-ons, which seem cheap by comparison.  If you buy a car for $50,000, then adding an extra $1,000 for leather doesn’t seem like much.  If you buy a computer for $1,500, then adding an extra $50 seems inconsequential.

Bevelin observes:

The same thing may appear attractive when compared to less attractive things and unattractive when compared to more attractive things.  For example, studies show that a person of average attractiveness is seen as less attractive when compared to more attractive others.

One trick some real estate agents use is to show the client a terrible house at an absurdly high price first, and then show them a merely mediocre house at a somewhat high price.  The agent often makes the sale.

Munger has remarked that some people enter into a bad marriage because their previous marriage was terrible.  These folks make the mistake of thinking that what is better based on their own limited experience is the same as what is better based on the experience of many different people.

Another issue is that something can gradually get much worse over time, but we don’t notice it because each increment is small.  It’s like the frog in water where the water is slowly brought to the boiling point.  For instance, the behavior of some people may get worse and worse and worse.  But we fail to notice because the change is too gradual.

12. Anchoring

The anchoring effect:  People tend to use any random number as a baseline for estimating an unknown quantity, despite the fact that the unknown quantity is totally unrelated to the random number.  (People also overweigh initial information that is non-quantitative.)

Daniel Kahneman and Amos Tversky did one experiment where they spun a wheel of fortune, but they had secretly programmed the wheel so that it would stop on 10 or 65.   After the wheel stopped, participants were asked to estimate the percentage of African countries in the UN.   Participants who saw “10” on the wheel guessed 25% on average, while participants who saw “65” on the wheel guessed 45% on average, a huge difference.

Behavioral finance expert James Montier has run his own experiment on anchoring.   People are asked to write down the last four digits of their phone number.   Then they are asked whether the number of doctors in their capital city is higher or lower than the last four digits of their phone number.   Results:  Those whose last four digits were greater than 7,000 on average report 6,762 doctors, while those with telephone numbers below 2,000 arrived at an average 2,270 doctors.  (James Montier, Behavioural Investing, Wiley 2007, page 120)

Those are just two experiments out of many.  The anchoring effect is “one of the most reliable and robust results of experimental psychology,” says Kahneman.  Furthermore, Montier observes that the anchoring effect is one reason why people cling to financial forecasts, despite the fact that most financial forecasts are either wrong, useless, or impossible to time.

When faced with the unknown, people will grasp onto almost anything.  So it is little wonder that an investor will cling to forecasts, despite their uselessness. 

13. Vividness and Recency

Bevelin explains:

The more dramatic, salient, personal, entertaining, or emotional some information, event, or experience is, the more influenced we are.  For example, the easier it is to imagine an event, the more likely we are to think that it will happen.

We are easily influenced when we are told stories because we relate to stories better than to logic or fact.  We love to be entertained.  Information we receive directly, through our eyes or ears has more impact than information that may have more evidential value.  A vivid description from a friend or family member is more believable than true evidence.  Statistical data is often overlooked.  Studies show that jurors are influenced by vivid descriptions.  Lawyers try to present dramatic and memorable testimony.

The media capitalizes on negative events—especially if they are vivid—because negative news sells.  For instance, even though the odds of being in a plane crash are infinitesimally low—one in 11 million—people become very fearful when a plane crash is reported in the news.  Many people continue to think that a car is safer than a plane, but you are over 2,000 times more likely to be in a car crash than a plane crash.  (The odds of being in a car crash are one in 5,000.)

14. Omission and Abstract Blindness

We see the available information.  We don’t see what isn’t reported.  Missing information doesn’t draw our attention.  We tend not to think about other possibilities, alternatives, explanations, outcomes, or attributes.  When we try to find out if one thing causes another, we only see what happened, not what didn’t happen.  We see when a procedure works, not when it doesn’t work.  When we use checklists to find out possible reasons for why something doesn’t work, we often don’t see that what is not on the list in the first place may be the reason for the problem.

Often we don’t see things right in front of us if our attention is focused elsewhere.

15. Reciprocation

Munger:

The automatic tendency of humans to reciprocate both favors and disfavors has long been noticed as it is in apes, monkeys, dogs, and many less cognitively gifted animals.  The tendency facilitates group cooperation for the benefit of members.

Unfortunately, hostility can get extreme.  But we have the ability to train ourselves.  Munger:

The standard antidote to one’s overactive hostility is to train oneself to defer reaction.  As my smart friend Tom Murphy so frequently says, ‘You can always tell the man off tomorrow, if it is such a good idea.’

Munger then notes that the tendency to reciprocate favor for favor is also very intense.  On the whole, Munger argues, the reciprocation tendency is a positive:

Overall, both inside and outside religions, it seems clear to me that Reciprocation Tendency’s constructive contributions to man far outweigh its destructive effects…

And the very best part of human life probably lies in relationships of affection wherein parties are more interested in pleasing than being pleased—a not uncommon outcome in display of reciprocate-favor tendency.

Guilt is also a net positive, asserts Munger:

…To the extent the feeling of guilt has an evolutionary base, I believe the most plausible cause is the mental conflict triggered in one direction by reciprocate-favor tendency and in the opposite direction by reward superresponse tendency pushing one to enjoy one hundred percent of some good thing… And if you, like me… believe that, averaged out, feelings of guilt do more good than harm, you may join in my special gratitude for reciprocate-favor tendency, no matter how unpleasant you find feelings of guilt.

16. Liking and Disliking

Munger:

One very practical consequence of Liking/Loving Tendency is that it acts as a conditioning device that makes the liker or lover tend (1) to ignore faults of, and comply with wishes of, the object of his affection, (2) to favor people, products, and actions merely associated with the object of his affection [this is also due to Bias from Mere Association] and (3) to distort other facts to facilitate love.

We’re naturally biased, so we have to be careful in some situations.

On the other hand, Munger points out that loving admirable persons and ideas can be very beneficial.

…a man who is so constructed that he loves admirable persons and ideas with a special intensity has a huge advantage in life.  This blessing came to both Buffett and myself in large measure, sometimes from the same persons and ideas.  One common, beneficial example for us both was Warren’s uncle, Fred Buffett, who cheerfully did the endless grocery-store work that Warren and I ended up admiring from a safe distance.  Even now, after I have known so many other people, I doubt if it is possible to be a nicer man than Fred Buffett was, and he changed me for the better.

Warren Buffett:

If you tell me who your heroes are, I’ll tell you how you’re gonna turn out.  It’s really important in life to have the right heroes.  I’ve been very lucky in that I’ve probably had a dozen or so major heroes.  And none of them have ever let me down.  You want to hang around with people that are better than you are.  You will move in the direction of the crowd that you associate with.

Disliking: Munger notes that Switzerland and the United States have clever political arrangements to “channel” the hatreds and dislikings of individuals and groups into nonlethal patterns including elections.

But the dislikings and hatreds never go away completely…  And we also get the extreme popularity of very negative political advertising in the United States.

Munger explains:

Disliking/Hating Tendency also acts as a conditioning device that makes the disliker/hater tend to (1) ignore virtues in the object of dislike, (2) dislike people, products, and actions merely associated with the object of dislike, and (3) distort other facts to facilitate hatred.

Distortion of that kind is often so extreme that miscognition is shockingly large.  When the World Trade Center was destroyed, many Pakistanis immediately concluded that the Hindus did it, while many Muslims concluded that the Jews did it.  Such factual distortions often make mediation between opponents locked in hatred either difficult or impossible.  Mediations between Israelis and Palestinians are difficult because facts in one side’s history overlap very little with facts from the other side’s.

17. Social Proof

Munger comments:

The otherwise complex behavior of man is much simplified when he automatically thinks and does what he observes to be thought and done around him.  And such followership often works fine…

Psychology professors love Social-Proof Tendency because in their experiments it causes ridiculous results.  For instance, if a professor arranges for some stranger to enter an elevator wherein ten ‘compliance practitioners’ are all standing so that they face the rear of the elevator, the stranger will often turn around and do the same.

Of course, like the other tendencies, Social Proof has an evolutionary basis.  If the crowd was running in one direction, typically your best response was to follow.

But, in today’s world, simply copying others often doesn’t make sense.  Munger:

And in the highest reaches of business, it is not at all uncommon to find leaders who display followership akin to that of teenagers.  If one oil company foolishly buys a mine, other oil companies often quickly join in buying mines.  So also if the purchased company makes fertilizer.  Both of these oil company buying fads actually bloomed, with bad results.

Of course, it is difficult to identify and correctly weigh all the possible ways to deploy the cash flow of an oil company.  So oil company executives, like everyone else, have made many bad decisions that were triggered by discomfort from doubt.  Going along with social proof provided by the action of other oil companies ends this discomfort in a natural way.

Munger points out that Social Proof can sometimes be constructive:

Because both bad and good behavior are made contagious by Social-Proof Tendency, it is highly important that human societies (1) stop any bad behavior before it spreads and (2) foster and display all good behavior.

It’s vital for investors to be able to think independently.  As Ben Graham says:

You are neither right nor wrong because the crowd disagrees with you.  You are right because your data and reasoning are right.

18. Authority

A disturbingly significant portion of copilots will not correct obvious errors made by the pilot during simulation exercises.  There are also real world examples of copilots crashing planes because they followed the pilot mindlessly.  Munger states:

…Such cases are also given attention in the simulator training of copilots who have to learn to ignore certain really foolish orders from boss pilots because boss pilots will sometimes err disastrously.  Even after going through such a training regime, however, copilots in simulator exercises will too often allow the simulated plane to crash because of some extreme and perfectly obvious simulated error of the chief pilot.

Psychologist Stanley Milgram wanted to understand why so many seemingly normal and decent people engaged in horrific, unspeakable acts during World War II.  Munger:

[Milgram] decided to do an experiment to determine exactly how far authority figures could lead ordinary people into gross misbehavior.  In this experiment, a man posing as an authority figure, namely a professor governing a respectable experiment, was able to trick a great many ordinary people into giving what they had every reason to believe were massive electric shocks that inflicted heavy torture on innocent fellow citizens…

Almost any intelligent person with my checklist of psychological tendencies in his hand would, by simply going down the checklist, have seen that Milgram’s experiment involved about six powerful psychological tendencies acting in confluence to bring about his extreme experimental result.  For instance, the person pushing Milgram’s shock lever was given much social proof from presence of inactive bystanders whose silence communicated that his behavior was okay…

Bevelin quotes the British novelist and scientist Charles Percy Snow:

When you think of the long and gloomy history of man, you will find more hideous crimes have been committed in the name of obedience than have ever been committed in the name of rebellion.

19. The Narrative Fallacy (Sensemaking)

(Bevelin uses the term “sensemaking,” but “narrative fallacy” is better, in my view.)  In The Black Swan, Nassim Taleb writes the following about the narrative fallacy:

The narrative fallacy addresses our limited ability to look at sequences of facts without weaving an explanation into them, or, equivalently, forcing a logical link, an arrow of relationship, upon them.  Explanations bind facts together.  They make them all the more easily remembered;  they help them make more sense.  Where this propensity can go wrong is when it increases our impression of understanding.

The narrative fallacy is central to many of the biases and misjudgments mentioned by Charlie Munger.  (In his great book, Thinking, Fast and Slow, Daniel Kahneman discusses the narrative fallacy as a central cognitive bias.)  The human brain, whether using System 1 (intuition) or System 2 (logic), always looks for or creates logical coherence among random data.  Often System 1 is right when it assumes causality; thus, System 1 is generally helpful, thanks to evolution.  Furthermore, System 2, by searching for underlying causes or coherence, has, through careful application of the scientific method over centuries, developed a highly useful set of scientific laws by which to explain and predict various phenomena.

The trouble comes when the data or phenomena in question are highly random—or inherently unpredictable (at least for the time being).  In these areas, System 1 makes predictions that are often very wrong.  And even System 2 assumes necessary logical connections when there may not be any—at least, none that can be discovered for some time.

Note:  The eighteenth century Scottish philosopher (and psychologist) David Hume was one of the first to clearly recognize the human brain’s insistence on always assuming necessary logical connections in any set of data or phenomena.

If our goal is to explain certain phenomena scientifically, then we have to develop a testable hypothesis about what will happen (or what will happen with probability x) under specific, relevant conditions.  If our hypothesis can’t accurately predict what will happen under specific, relevant conditions, then our hypothesis is not a valid scientific explanation.

20. Reason-respecting

We are more likely to comply with a request if people give us a reason—even if we don’t understand the reason or if it’s wrong.  In one experiment, a person approaches people standing in line waiting to use a copy machine and says, “Excuse me, I have 5 pages.  May I use the Xerox machine because I have to make some copies?”  Nearly everyone agreed.

Bevelin notes that often the word “because” is enough to convince someone, even if no actual reason is given.

21. Believe First and Doubt Later

We are not natural skeptics.  We find it easy to believe but difficult to doubt.  Doubting is active and takes effort.

Bevelin continues:

Studies show that in order to understand some information, we must first accept it as true… We first believe all information we understand and only afterwards and with effort do we evaluate, and if necessary, un-believe it.

Distraction, fatigue, and stress tend to make us less likely to think things through and more likely to believe something that we normally might doubt.

When it comes to detecting lies, many (if not most) people are only slightly better than chance.  Bevelin quotes Michel de Montaigne:

If falsehood, like truth, had only one face, we would be in better shape.  For we would take as certain the opposite of what the liar said.  But the reverse of truth has a hundred thousand shapes and a limitless field.

22. Memory Limitations

Bevelin:

Our memory is selective.  We remember certain things and distort or forget others.  Every time we recall an event, we reconstruct our memories.  We only remember fragments of our real past experiences.  Fragments influenced by what we have learned, our experiences, beliefs, mood, expectations, stress, and biases.

We remember things that are dramatic, fearful, emotional, or vivid.  But when it comes to learning in general—as opposed to remembering—we learn better when we’re in a positive mood.

Human memory is flawed to the point that eyewitness identification evidence has been a significant cause of wrongful convictions.  Moreover, leading and suggestive questions can cause misidentification.  Bevelin:

Studies show that it is easy to get a witness to believe they saw something when they didn’t.  Merely let some time pass between their observation and the questioning.  Then give them false or emotional information about the event.

23. Do-something Syndrome

Activity is not the same thing as results.  Most people feel impelled by boredom or hubris to be active.  But many things are not worth doing.

If we’re long-term investors, then nearly all of the time the best thing for us to do is nothing at all (other than learn).  This is especially true if we’re tired, stressed, or emotional.

24. Say-something Syndrome

Many people have a hard time either saying nothing or saying, “I don’t know.”  But it’s better for us to say nothing if we have nothing to say.  It’s better to admit “I don’t know” rather than pretend to know.

25. Emotions

Bevelin writes:

We saw under loss aversion and deprival that we put a higher value on things we already own than on the same things if we don’t own them.  Sadness reverses this effect, making us willing to accept less money to sell something than we would pay to buy it.

It’s also worth repeating: If we feel emotional, it’s best to defer important decisions whenever possible.

26. Stress

A study showed that business executives who are committed to their work and who have a positive attitude towards challenges—viewing them as opportunities for growth—do not get sick from stress.  Business executives who lack such commitment or who lack a positive attitude towards challenges are more likely to get sick from stress.

Stress itself is essential to life.  We need challenges.  What harms us is not stress but distress—unnecessary anxiety and unhelpful trains of thought.  Bevelin quotes the stoic philosopher Epictetus:

Happiness and freedom begin with a clear understanding of one principle: Some things are within our control, and some things are not.  It is only after you have faced up to this fundamental rule and learned to distinguish between what you can and can’t control that inner tranquility and outer effectiveness become possible.

27. Pain and Chemicals

People struggle to think clearly when they are in pain or when they’re drunk or high.

Munger argues that if we want to live a good life, first we should list the things that can ruin a life.  Alcohol and drugs are near the top of the list.  Self-pity and a poor mental attitude will also be on that list.  We can’t control everything that happens, but we can always control our mental attitude.  As the Austrian psychiatrist and Holocaust survivor Viktor Frankl said:

Everything can be taken from a man but one thing: the last of the human freedoms—to choose one’s attitude in any given set of circumstances, to choose one’s own way.

28. Multiple Tendencies

Often multiple psychological tendencies operate at the same time.  Bevelin gives an example where the CEO makes a decision and expects the board of directors to go along without any real questions.  Bevelin explains:

Apart from incentive-caused bias, liking, and social approval, what are some other tendencies that operate here?  Authority—the CEO is the authority figure whom directors tend to trust and obey.  He may also make it difficult for those who question him.  Social proof—the CEO is doing dumb things but no one else is objecting so all directors collectively stay quiet—silence equals consent; illusions of the group as invulnerable and group pressure (loyalty) may also contribute.  Reciprocation—unwelcome information is withheld since the CEO is raising the director fees, giving them perks, taking them on trips or letting them use the corporate jet.  Association and Persian Messenger Syndrome—a single director doesn’t want to be the carrier of bad news.  Self-serving tendencies and optimism—feelings of confidence and optimism: many boards also select new directors who are much like themselves; that share similar ideological viewpoints.  Deprival—directors don’t want to lose income and status.  Respecting reasons no matter how illogical—the CEO gives them reasons.  Believing first and doubting later—believing what the CEO says even if not true, especially when distracted.  Consistency—directors want to be consistent with earlier decisions—dumb or not.

 

Part Three:  The Physics and Mathematics of Misjudgments

SYSTEMS THINKING

  • Failing to consider that actions have both intended and unintended consequences.  Includes failing to consider secondary and higher order consequences and inevitable implications.
  • Failing to consider the whole system in which actions and reactions take place, the important factors that make up the system, their relationships and effects of changes on system outcome.
  • Failing to consider the likely reaction of others—what is best to do may depend on what others do.
  • Failing to consider the implications of winning a bid—overestimating value and paying too much.
  • Overestimating predictive ability or using unknowable factors in making predictions.

 

SCALE AND LIMITS

  • Failing to consider that changes in size or time influence form, function, and behavior.
  • Failing to consider breakpoints, critical thresholds, or limits.
  • Failing to consider constraints—that a system’s performance is constrained by its weakest link.

 

CAUSES

  • Not understanding what causes desired results.
  • Believing cause resembles its effect—that a big effect must have a big or complicated cause.
  • Underestimating the influence of randomness in bad or good outcomes.
  • Mistaking an effect for its cause.  Includes failing to consider that many effects may originate from one common root cause.
  • Attributing outcome to a single cause when there are multiple causes.
  • Mistaking correlation for cause.
  • Failing to consider that an outcome may be consistent with alternative explanations.
  • Drawing conclusions about causes from selective data.  Includes identifying the wrong cause because it seems the obvious one based on a single observed effect.  Also failing to consider information or evidence that is missing.
  • Not comparing the difference in conditions, behavior, and factors between negative and positive outcomes in similar situations when explaining an outcome.

 

NUMBERS AND THEIR MEANING

  • Looking at isolated numbers—failing to consider relationships and magnitudes.  Includes not using basic math to count and quantify.  Also not differentiating between relative and absolute risk.
  • Underestimating the effect of exponential growth.
  • Underestimating the time value of money.

 

PROBABILITIES AND NUMBER OF POSSIBLE OUTCOMES

  • Underestimating risk exposure in situations where relative frequency (or comparable data) and/or magnitude of consequences is unknown or changing over time.
  • Underestimating the number of possible outcomes for unwanted events.  Includes underestimating the probability and severity of rate or extreme events.
  • Overestimating the chance of rare but widely publicized and highly emotional events and underestimating the chance of common but less publicized events.
  • Failing to consider both probabilities and consequences (expected value).
  • Believing events where chance plays a role are self-correcting—that previous outcomes of independent events have predictive value in determining future outcomes.
  • Believing one can control the outcome of events where chance is involved.
  • Judging financial decisions by evaluating gains and losses instead of final state of wealth and personal value.
  • Failing to consider the consequences of being wrong.

 

SCENARIOS

  • Overestimating the probability of scenarios where all of a series of steps must be achieved for a wanted outcome.  Also underestimating opportunities for failure and what normally happens in similar situations.
  • Underestimating the probability of systems failure—scenarios composed of many parts where system failure can happen one way or another.  Includes failing to consider that time horizon changes probabilities.  Also assuming independence when it is not present and/or assuming events are equally likely when they are not.
  • Not adding a factor of safety for known and unknown risks.  Size of factor depends on the consequences of failure, how well the risks are understood, systems characteristics, and degree of control.

 

COINCIDENCES AND MIRACLES

  • Underestimating that surprises and improbable events happen, somewhere, sometime, to someone, if they have enough opportunities (large enough size or time) to happen.
  • Looking for meaning, searching for causes, and making up patterns for chance events, especially events that have emotional implications.
  • Failing to consider cases involving the absence of a cause or effect.

 

RELIABILITY OF CASE EVIDENCE

  • Overweighing individual case evidence and under-weighing the prior probability (probability estimate of an event before considering new evidence that might change it) considering for example, the base rate (relative frequency of an attribute or event in a representative comparison group), or evidence from many similar cases.  Includes failing to consider the probability of a random match, and the probability of a false positive and a false negative.  Also failing to consider a relevant comparison population that bears the characteristic we are seeking.

 

MISREPRESENTATIVE EVIDENCE

  • Failing to consider changes in factors, context, or conditions when using past evidence to predict likely future outcomes.  Includes not searching for explanations to why a past outcome happened, what is required to make the past record continue, and what forces can change it.
  • Overestimating evidence from a single case or small or unrepresentative samples.
  • Underestimating the influence of chance in performance (success and failure)
  • Only seeing positive outcomes—paying little or no attention to negative outcomes and prior probabilities.
  • Failing to consider variability of outcomes and their frequency.
  • Failing to consider regression—in any series of events where chance is involved, unique outcomes tend to regress back to the average outcome.

 

Part Four:  Guidelines to Better Thinking

Bevelin explains: “The purpose of this part is to explore tools that provide a foundation for rational thinking.  Ideas that help us when achieving goals, explaining ‘why,’ preventing and reducing mistakes, solving problems, and evaluating statements.”

Bevelin lists 12 tools that he discusses:

  • Models of reality
  • Meaning
  • Simplification
  • Rules and filters
  • Goals
  • Alternatives
  • Consequences
  • Quantification
  • Evidence
  • Backward thinking
  • Risk
  • Attitudes

 

MODELS OF REALITY

Bevelin:

A model is an idea that helps us better understand how the world works.  Models illustrate consequences and answer questions like ‘why’ and ‘how.’  Take the model of social proof as an example.  What happens?  When people are uncertain they often automatically do what others do without thinking about the correct thing to do.  This idea helps explain ‘why’ and predict ‘how’ people are likely to behave in certain situations.

Bevelin continues:

Ask:  What is the underlying big idea?  Do I understand its application in practical life?  Does it help me understand the world?  How does it work?  Why does it work?  Under what conditions does it work?  How reliable is it?  What are its limitations?  How does it relate to other models?

What models are most reliable?  Bevelin quotes Munger:

“The models that come from hard science and engineering are the most reliable models on this Earth.  And engineering quality control—at least the guts of it that matters to you and me and people who are not professional engineers—is very much based on the elementary mathematics of Fermat and Pascal: It costs so much and you get so much less likelihood of it breaking if you spend this much…

And, of course, the engineering idea of a backup system is a very powerful idea.  The engineering idea of breakpoints—that’s a very powerful model, too.  The notion of a critical mass—that comes out of physics—is a very powerful model.”

Bevelin adds:

A valuable model produces meaningful explanations and predictions of likely future consequences where the cost of being wrong is high.

A model should be easy to use.  If it is complicated, we don’t use it.

It is useful on a nearly daily basis.  If it is not used, we forget it.

Bevelin asks what can help us to see the big picture.  Bevelin quotes Munger again:

“In most messy human problems, you have to be able to use all the big ideas and not just a few of them.”

Bevelin notes that physics does not explain everything, and neither does economics.  In business, writes Bevelin, it is useful to know how scale changes behavior, how systems may break, how supply influences prices, and how incentives cause behavior.

It’s also crucial to know how different ideas interact and combine.  Munger again:

“You get lollapalooza effects when two, three, or four forces are all operating in the same direction.  And, frequently, you don’t get simple addition.  It’s often like a critical mass in physics where you get a nuclear explosion if you get to a certain point of mass—and you don’t get anything much worth seeing if you don’t reach the mass.

Sometimes the forces just add like ordinary quantities and sometimes they combine on a break-point or critical-mass basis… More commonly, the forces coming out of models are conflicting to some extent… So you [must] have the models and you [must] see the relatedness and the effects from the relatedness.”

 

MEANING

Bevelin writes:

Understanding ‘meaning’ requires that we observe and ask basic questions.  Examples of some questions are:

    • Meaning of words:  What do the words mean?  What do they imply?  Do they mean anything?  Can we translate words, ideas, or statements into an ordinary situation that tells us something?  An expression is always relative.  We have to judge and measure it against something.
    • Meaning of an event:  What effect is produced?  What is really happening using ordinary words?  What is it doing?  What is accomplished?  Under what conditions does it happen?  What else does it mean?
    • Causes:  What is happening here and why?  Is this working?  Why or why not?  Why did that happen?  Why does it work here but not there?  How can it happen?  What are the mechanisms behind?  What makes it happen?
    • Implications:  What is the consequence of this observation, event, or experience?  What does that imply?
    • Purpose:  Why should we do that?  Why do I want this to happen?
    • Reason:  Why is this better than that?
    • Usefulness:  What is the applicability of this?  Does it mean anything in relation to what I want to achieve?

Turning to the field of investing, how do we decide how much to pay for a business?  Buying stock is buying a fractional share of a business.  Bevelin quotes Warren Buffett:

What you’re trying to do is to look at all the cash a business will produce between now and judgment day and discount it back to the present using an appropriate discount rate and buy a lot cheaper than that.  Whether the money comes from a bank, an Internet company, a brick company… the money all spends the same.  Why pay more for a telecom business than a brick business?  Money doesn’t know where it comes from.  There’s no sense in paying more for a glamorous business if you’re getting the same amount of money, but paying more for it.  It’s the same money that you can get from a brick company at a lower cost.  The question is what are the economic characteristics of the bank, the Internet company, or the brick company.  That’s going to tell you how much cash they generate over long periods in the future.

 

SIMPLIFICATION

Bevelin quotes Munger:

We have a passion for keeping things simple.

Bevelin then quotes Buffett:

We haven’t succeeded because we have some great, complicated systems or magic formulas we apply or anything of the sort.  What we have is just simplicity itself.

Munger again:

If something is too hard, we move on to something else.  What could be more simple than that?

Munger:

There are things that we stay away from.  We’re like the man who said he had three baskets on his desk: in, out, and too tough.  We have such baskets—mental baskets—in our office.  An awful lot of stuff goes in the ‘too tough’ basket.

Buffett on how he and Charlie Munger do it:

Easy does it.  After 25 years of buying and supervising a great variety of businesses, Charlie and I have not learned how to solve difficult business problems.  What we have learned is to avoid them.  To the extent we have been successful, it is because we concentrated on identifying one-foot hurdles that we could step over rather than because we acquired any ability to clear seven-footers.  The finding may seem unfair, but in both business and investments it is usually far more profitable to simply stick with the easy and obvious than it is to resolve the difficult.

It’s essential that management maintain focus.  Buffett:

A… serious problem occurs when the management of a great company gets sidetracked and neglects its wonderful base business while purchasing other businesses that are so-so or worse… (Would you believe that a few decades back they wee growing shrimp at Coke and exploring for oil at Gillette?)  Loss of focus is what most worries Charlie and me when we contemplate investing in businesses that in general look outstanding.  All too often, we’ve seen value stagnate in the presence of hubris or of boredom that caused the attention of managers to wander.

For an investor considering an investment, it’s crucial to identify what is knowable and what is important.  Buffett:

There are two questions  you ask yourself as you look at the decision you’ll make.  A) is it knowable?  B) is it important?  If it is not knowable, as you know there are all kinds of things that are important but not knowable, we forget about those.  And if it’s unimportant, whether it’s knowable or not, it won’t make any difference.  We don’t care.

 

RULES AND FILTERS

Bevelin writes:

When we know what we want, we need criteria to evaluate alternatives.  Ask: What are the most critical (and knowable) factors that will cause what I want to achieve or avoid?  Criteria must be based on evidence and be reasonably predictive… Try to use as few criteria as necessary to make your judgment.  Then rank them in order of their importance and use them as filters.  Set decision thresholds in a way that minimizes the likelihood of false alarms and misses (in investing, choosing a bad investment or missing a good investment).  Consider the consequences of being wrong.

Bear in mind that in many fields, a relatively simple statistical prediction rule based on a few key variables will perform better than experts over time.  See: https://boolefund.com/simple-quant-models-beat-experts-in-a-wide-variety-of-areas/

Bevelin gives as an example the following: a man is rushed to the hospital while having a heart attack.  Is it high-risk or low-risk?  If the patient’s minimum systolic blood pressure over the initial 24-hour period is less than 91, then it’s high-risk.  If not, then the next question is age.  If the patient is over 62.5 years old, then if he displays sinus tachycardia, he is high-risk.  It turns out that this simple model—developed by Statistics Professor Leo Breiman and colleagues at the University of California, Berkeley—works better than more complex models and also than experts.

In making an investment decision, Buffett has said that he uses a 4-step filter:

    • Can I understand it?
    • Does it look like it has some kind of sustainable competitive advantage?
    • Is the management composed of able and honest people?
    • Is the price right?

If a potential investment passes all four filters, then Buffett writes a check.  By “understanding,” Buffett means having a “reasonable probability” of assessing whether the business will be in 10 years.

 

GOALS

Bevelin puts forth:

Always ask:  What end result do I want?  What causes that?  What factors have a major impact on the outcome?  What single factor has the most impact?  Do I have the variable(s) needed for the goal to be achieved?  What is the best way to achieve my goal?  Have I considered what other effects my actions will have that will influence the final outcome?

When we solve problems and know what we want to achieve, we need to prioritize or focus on the right problems.  What should we do first?  Ask:  How serious are the problems?  Are they fixable?  What is the most important problem?  Are the assumptions behind them correct?  Did we consider the interconnectedness of the problems?  The long-term consequences?

 

ALTERNATIVES

Bevelin writes:

Choices have costs.  Even understanding has an opportunity cost.  If we understand one thing well, we may understand other things better.  The cost of using a limited resource like time, effort, and money for a specific purpose, can be measured as the value or opportunity lost by not using it in its best available alternative use…

Bevelin considers a business:

Should TransCorp take the time, money, and talent to build a market presence in Montana?  The real cost of doing that is the value of the time, money, and talent used in its best alternative use.  Maybe increasing their presence in a state where they already have a market share is creating more value.  Sometimes it is more profitable to marginally increase a cost where a company already has an infrastructure.  Where to they marginally get the most leverage on resources spent?  Always ask: What is the change of value of taking a specific action?  Where is it best to invest resources from a value point of view?

 

CONSEQUENCES

Bevelin writes:

Whenever we install a policy, take an action, or evaluate statements, we must trace the consequences.  When doing so, we must remember four key things:

  • Pay attention to the whole system.  Direct and indirect effects,
  • Consequences have implications or more consequences, some which may be unwanted.  We can’t estimate all possible consequences but there is at least one unwanted consequence we should look out for,
  • Consider the effects of feedback, time, scale, repetition, critical thresholds and limits,
  • Different alternatives have different consequences in terms of costs and benefits.  Estimate the net effects over time and how desirable these are compared to what we want to achieve.

We should heed Buffett’s advice:  Whenever someone makes an assertion in economics, always ask, “And then what?”  Very often, particularly in economics, it’s the consequences of the consequences that matter.

 

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC

The Go-Go Years

August 14, 2022

John Brooks is one of the best business writers of all time.  Business Adventures may be his best book, but Once in GolcondaThe Go-Go Years, and The Games Players are also worth reading.

I wrote about Business Adventures here: https://boolefund.com/business-adventures/

Today’s blog post deals with The Go-Go Years.

Here’s a brief outline:

    • Climax: The Day Henry Ross Perot Lost $450 million
    • Fair Exchange: The Year the Amex Delisted the Old Guard Romans
    • The Last Gatsby: Recessional for Edward M. Gilbert
    • Palmy Days and Low Rumblings: Early Warnings Along Wall Street
    • Northern Exposure: Early Warnings Along Bay Street
    • The Birth of Go-Go: The Rise of a Proper Chinese Bostonian
    • The Conglomerateurs: Corporate Chutzpah and Creative Accounting
    • The Enormous Back Room: Drugs, Fails, and Chaos Among the Clerks
    • Go-Go at High Noon: The View from Trinity Church
    • Confrontation: Steinberg/Leasco vs. Renchard/Chemical Bank

 

CLIMAX

Henry Ross Perot from Dallas, Texas, was one of the top six richest people in America on April 22, 1970.  That day, he suffered a stock market loss of $450 million, still leaving him with a billion dollars worth of stock.  Brooks writes that Perot lost more than the assets of any charitable foundation in the country outside of the top five.  Brooks adds:

It was also quite possibly more in actual purchasing power than any man had ever lost in a single day since the Industrial Revolution brought large private accumulations of money into being.

On May 8, 1970, schools were closed in protest of Vietnam.  One of the antiwar demonstrations by students took place at Wall Street.  This particular demonstration was noticeably nonviolent.  Unfortunately, right before noon, a group of construction workers—carrying construction tools and wearing heavy boots—attacked the student demonstrators.  Fifty (out of a thousand) students needed first-aid treatment, and twenty-three of those were hospitalized.

Brooks explains that workers on Wall Street sided with the students:

Perhaps out of common humanity, or perhaps out of class feeling, the bulls and bears felt more kinship with the doves than with the hawks.  At Exchange Place, Robert A. Bernhard, a partner in the aristocratic firm of Lehman Brothers, was himself assaulted and severely cut in the head by a construction worker’s heavy pliers, after he had tried to protect a youth who was being beaten.  A few blocks north, a young Wall Street lawyer was knocked down, kicked, and beaten when he protested against hardhats who were yelling, ‘Kill the Commie bastards!’

However, many on Wall Street took no part in the struggle.  Brooks continues:

…there is an all too symbolic aspect to professional Wall Street’s role that day as a bystander, sympathizing, unmistakeably, with the underdogs, the unarmed, the peace-lovers, but keeping its hands clean—watching with fascination and horror from its windows…

Brooks asks:

Did it make sense any more to live—and live at the top of the heap—by playing games with paper while children screamed under the window?

Although Perot understood that Wall Street—where a company could be taken public—was the source of his wealth, he nonetheless still believed in the West—and the “frontier”—not the East.  Brooks:

He believed that all things were possible in America for the man of enterprise and that the natural habitat of the man of enterprise was the “frontier.”

Perot graduated from the Naval Academy in 1953, where he was class president.  After four years of active Navy duty, he went to work as a salesman on commission for IBM.

Perot was earning so much money at IBM that the company cut his commissions by 80 percent and they gave him a quota for the year, past which he wouldn’t earn anything.  In 1962, Perot hit his quota on January 19, which made him essentially unemployed for the rest of the year.

Perot’s solution was to start his own company—Electronic Data Systems Corp., designers, installers, and operators of computer systems.  The new company struggled for some time.  Finally in 1965, federal Medicare legislation was passed.  E.D.S. soon had subcontracts to administer Medicare or Medicaid in eleven states.

All told, by 1968 E.D.S. had twenty-three contracts for computer systems, 323 full-time employees, about $10 million in assets, annual net profits of over $1.5 million, and a growth curve so fantastic as to make investment bankers’ mouths water.

By early 1970, having beaten every city slicker he encountered, Perot was worth $1.5 billion.  (This was a few years after E.D.S. went public.)

Perot proceeded to become what Brooks calls a “moral billionaire.”  He pledged to give away nearly all of his fortune to improve people’s lives.  Early on, when he started making charitable donations, he refused to take a tax write-off because he felt he owed tax money to a country that had given him such great opportunities.

Regarding the one-day stock market loss of $450 million, Brooks says:

The way Perot received the news of his monumental setback on April 22 was casual to the point of comedy.

Perot thought, correctly, that the $1.5 billion he had made over eight years wasn’t entirely real because it couldn’t be turned easily into cash.  Moreover, he had plenty of money, including a billion dollars in E.D.S. stock post-crash.  The bottom line, notes Brooks, was that Perot viewed the one-day swing as a non-event.

Brooks writes that E.D.S. was experiencing outstanding financial results at the time.  So the stock swoon wasn’t related to company fundamentals.  Many other stocks had fallen far more on a percentage basis than E.D.S. would fall on April 22, 1970.

At any rate, since the vast majority of stocks had already fallen, whereas E.D.S. stock hadn’t fallen at all, it seemed to make sense that E.D.S. stock would finally experience some downward volatility.  (Brooks notes that University Computing, a stock in E.D.S’s industry, was 80 percent below its peak before E.D.S. even started falling.)  Furthermore, it appeared that there was a bear raid on E.D.S. stock—the stock was vulnerable precisely because it was near its all-time highs, whereas so many other stocks were far lower than their all-time highs.

Brooks concludes:

Nor is it without symbolic importance that the larger market calamity of which E.D.S. was a part resembled in so many respects what had happened forty years before—what wise men had said, for more than a generation, over and over again as if by way of incantation, could never happen again.  It had happened again, as history will; but (as history will) it had happened differently.

 

FAIR EXCHANGE

Brooks tells the stories of two swindlers, Lowell McAfee Birrell and Alexander Guterma.  Brooks writes:

Birrell, like Richard Whitney before him, was apparently a scoundrel as much by choice as by necessity.  The son of a small-town Presbyterian minister, a graduate of Syracuse University and Michigan law school, a handsome, brilliant, and charming man who began his career with the aristocratic Wall Street law firm of Cadwalader, Wickersham and Taft and soon belonged to the Union League and Metropolitan Clubs, Birrell, if he had not been Birrell, might easily have become the modern-day equivalent of a Morgan partner—above the battle and beyond reproach.

Birrell issued himself tons of unauthorized stock in corporations he controlled, and then illegally sold the shares.  The S.E.C. was after Birrell in 1957.  To escape prosecution, Birrell fled to Brazil.

Brooks again:

Guterma was in the mold of the traditional international cheat of spy stories—an elusive man of uncertain national origin whose speech accent sometimes suggested Old Russia, sometimes the Lower East Side of New York, sometimes the American Deep South.

Guterma made his first fortune in the Phillipines during World War II.  He ran a gambling casino that catered to occupying Japanese serviceman.

In 1950, Guterma married an American woman and moved to the United States.  Brooks:

During the succeeding decade he controlled, and systematically looted, more than a dozen substantial American companies…

In September 1959, Guterma was indicted for fraud, stock manipulation, violation of federal banking laws, and failure to register as the agent of a foreign government.

Brooks mentioned Birrell and Guterma as background to a story in 1961 that involved Gerard A. (Jerry) Re and his son, Gerard F. Re.  The Re’s formed the Amex’s largest firm of stock specialists.  (At that time, specialists maintained orderly markets in various stocks.)  One problem was that specialists often have inside information about specific stocks from which they could profit.  Brooks comments:

Pushed in one direction by prudent self-interest, in the other by sense of duty or fear of punishment, a specialist at such times faces a dilemma more appropriate to a hero in Corneille or Racine than to a simple businessman brought up on classic Adam Smith and the comfortable theory of the socially beneficent marketplace.

The S.E.C. finally took notice.  Brooks:

Over a period of at least six years, the S.E.C. charged, the father and son had abused their fiduciary duties in just about every conceivable way, repeating a personal profit of something like $3 million.  They had made special deals with unethical company heads—Lowell Birrell in particular—to distribute unregistered stock to the public in violation of the law.  In order to manipulate the prices of those stocks for their private benefit and that of the executives they were in league with, they had bribed the press, given false tips by word of mouth, paid kickbacks to brokers, generated false public interest by arranging for fictitious trades to be recorded on the tape—the whole, infamous old panoply of sharp stock-jobbing practices.

Ralph S. Saul, the S.E.C.’s young assistant director of the Division of Trading and Exchanges, led the investigation against the Res.  After only two hours of oral arguments, the S.E.C. permanently banned the Res from the securities business.

It turned out that the president of the Amex, Edward T. McCormick, was on the S.E.C.’s list of Re associates.  This implied that the Amex, or at least its chief, knew what was going on all along.

McCormick, who held a master’s degree from the University of California and a PhD from Duke, had started working for the S.E.C. in 1934.  In 1951, he left his post as S.E.C. commissioner to become head of the Amex.  Brooks notes that this sort of talent drain had been the bane of the S.E.C. from its beginnings.  Brooks says:

The scholar and bureaucrat had turned out to be a born salesman.  But with the Amex’s growth, it began to appear toward the end of the decade, a certain laxness of administration had crept in.  Restless at his desk, Ted McCormick was always out selling up-and-coming companies on listing their shares on the Amex, and while he was in Florida or at the Stork Club drumming up trade, sloppy practices were flourishing back at Trinity Place.

Many didn’t notice the Res’ misdeeds.  And it seemed that those who knew didn’t care.  However, a father-and-son-in-law team, David S. Jackson and Andrew Segal, were greatly disturbed.

Jackson had seen McCormick change over the years:

…Jackson had watched McCormick gradually changing from a quiet, reflective man into a wheeler-dealer who loved to be invited by big businessmen to White Sulphur Springs for golf, and the change worried him.  “Ted,” he would say, when they were at dinner at one or the other’s house, “why don’t you read any more?”

“I haven’t got time,” McCormick would reply.

“But you’ll lose your perspective,” Jackson would protest, shaking his head.

Jackson and Segal eventually concluded that McCormick was not fit to be the president of Amex.  Jackson met with McCormick to tell him he should resign.  McCormick reacted violently, picking up a stack of papers and slamming them on to his desk, and then punching a wall of his office.

McCormick told Jackson that he had never done anything dishonest.

“No, I don’t think you have,” Jackson said, his voice shaking.  “But you’ve been indiscreet.”

Roughly a dozen members of Amex, mostly under forty and nicknamed the Young Turks, sided with Jackson and Segal in calling for McCormick’s resignation.  However, they were greatly outnumbered and they were harrassed and threatened.

One Young Turk, for example, was pointedly reminded of a questionable stock transaction in which he had been involved some years earlier, and of how easily the matter could be called to the S.E.C.’s attention; to another it was suggested that certain evidence at hand, if revealed, could make a shambles of his pending suit for divorce; and so on.

Soon Jackson and Segal were practically alone.  Then something strange happened.  The  S.E.C. chose to question Jackson about an incident in which one of Jackson’s assistants, years earlier, had done a bad job of specializing.  The S.E.C. was led by its top investigators, Ralph Saul, David Silver, and Edward Jaegerman.  They questioned Jackson for hours with what seemed to be hostility, scorn, and sarcasm.

Jackson went home and started writing a letter to send to various public officials to complain about his poor treatment by the S.E.C.  Jackson read the letter aloud over the phone to Ralph Saul, who was horrified.  Saul apologized, asked Jackson not to send the letter, and said that amends would be made.

The S.E.C. sent a team to watch the Jackson and Segal operation, trade by trade.  The S.E.C. concluded that Jackson and Segal were honest and asked them to become allies in the reform of the Amex.  Jackson and Segal agreed.

McCormick eventually was forced to resign.

 

THE LAST GATSBY

Brooks tells the story of Edward M. Gilbert:

From the first, he was a bright but lazy student with a particular aptitude for mathematics, a talented and fanatical athlete, and something of a spoiled darling…

Matriculating at Cornell in the early stages of World War II, he made a name for himself in tennis and boxing, won the chess championship of his dormitory, and earned a reputation as a prankster, but went on neglecting his studies.

Gilbert enlisted in the Army Air Force and worked for Army newspapers.  He demonstrated a talent for acquiring foreign languages.

After the war, Gilbert joined his father’s company.

During this period of his business apprenticeship he embarked on a series of personal ventures that were uniformly unsuccessful.  He backed a prizefighter who turned out to be a dud.  He was co-producer of a Broadway play, How Long Till Summer? that starred the black folksinger Josh White’s son… [but the play] got disastrous notices and closed [after a week.]  Gilbert also dabbled in the stock market without any notable success.

Edward Gilbert’s father, Harry Gilbert, became a multi-millionaire when his company, Empire Millwork, sold stock to the public.  Brooks:

He was ever ready to use his money to indulge his son, and over the years he would do so again and again… Never a corporate rainmaker, Harry Gilbert, humanly enough, yearned to appear vital, enterprising, and interesting to his friends and colleagues.  The son’s deals and the electric office atmosphere they created were made possible by the father’s money.  Doubtless the father on occasion did not even understand the intricate transactions his son was forever proposing—debentures and takeovers and the like.  But to admit it would be to lose face… And so, again and again, he put up the money.  Harry Gilbert bought commercial glamour from his son.

Brooks explains that, in 1948, Eddie Gilbert began dreaming of enlarging Empire Millwork through mergers.  In 1951, he asked his father for a directorship.  But Harry Gilbert turned him down.  So Eddie quit and entered the hardwood-floor business on his own.

There were two versions of what happened next.  In one version, Eddie Gilbert was successful and Empire bought him out in 1962.  In the other version, Eddie tried and failed to corner the hardwood-floor market, and Harry bought him out to bury the big mistake.  Brooks writes:

At any rate, in 1955 Eddie returned to Empire with new power and freedom to act.

Eddie wanted to buy E. L. Bruce and Company, the country’s leading hardwood-floor company.

With net sales of around $25 million a year, Bruce was considerably larger than Empire, but it was a staid firm, conservatively managed and in languid family control, of the sort that is the classic prey for an ambitious raider.  In 1955, Eddie Gilbert persuaded his father to commit much of his own and the company’s resources in an attempt to take over Bruce.

Now Eddie came into his own at last.  He began to make important friends in Wall Street—brokers impressed with his dash and daring, and delighted to have the considerable commissions he generated.  Some of his friends came from the highest and most rarified levels of finance.

Brooks continues:

In his early thirties, a short, compact man with pale blue eyes and a sort of ferret face under thinning hair, Gilbert had a direct personal charm that compensated for his vanity and extreme competitiveness.  Sometimes his newfound friends patronized him behind his back, laughing at his social pretensions and his love of ostentation, but they continued going to his parties and, above all, following his market tips.  Some accused him of being a habitual liar; they forgave him because he seemed genuinely to believe his lies, especially those about himself and his past.  He was a compulsive gambler—but, endearingly, a very bad one; on lucky streaks he would double bets until he lost all his winnings, or draw to inside straights for huge sums at poker, or go for broke on losing streaks; yet at all times he seemed to take large losses in the best of humor.

Eddie urged his new friends as well as his family to buy Bruce stock, which was selling around $25 a share.

All that spring, the Gilberts and their relatives and Eddie’s friends accumulated the stock, until in June it had reached the seventies and was bouncing up and down from day to day and hour to hour in an alarming way.  What was in the process of developing in Bruce stock was the classically dangerous, sometimes disastrous situation called a corner.

Bruce family management had realized that a raid was developing, so they were buying as much stock as they could.  At the same time, speculators began shorting the stock on the belief that the stock price would fall.  Shorting involved borrowing shares and selling them, and later buying them back.  The short sellers would profit if they bought it back at a price lower than where they sold it.  However, they would lose money if they bought it back at a price higher than where they sold it.

The problem for short sellers was that Eddie’s friends and family, and Bruce family management, ended up owning all available shares of stock.  Brooks:

The short sellers were squeezed; if called upon to deliver the stock they had borrowed and then sold, they could not do so, and those who owned it were in a position to force them to buy back what they owed at a highly inflated price.

Short sellers bought what little stock was available, sending the price up to 188.

Eddie Gilbert, coming out of the fray in the fall of 1958, seemed to have arrived at last—apparently paper-rich from his huge holdings of high-priced Bruce stock, rich in the esteem of his society backers, nationally famous from the publicity attendant on the corner he had brought about.

Gilbert was self-indulgent with his new wealth, for instance, keeping a regular Monday box at the Metropolitan Opera.  Brooks:

He acquired a huge Fifth Avenue apartment and, when and as he could, filled it with French antiques, a fortune in generally almost-first-rate paintings, and a staff of six.  Sometimes he lived in a mansion at Palm Beach, epitome of Real Society in faded turn-of-the-century photographs.  He took an immense villa at Cap Martin on the French Riviera, where he mingled when he could with Maria Callas and Aristotle Onassis and their like, and gave huge outdoor parties with an orchestra playing beside an Olympic-size swimming pool.

Brooks explains that Gilbert was not genuinely rich:

His paper profits were built on borrowing, and he was always mortgaged right up to the hilt; to be thus mortgaged, and to remain so, was all but an article of faith with him… He was habitually so pressed for cash that on each January first he would draw his entire $50,000 empire salary for the coming year in a lump sum in advance.  By the summer of 1960 he was in bad financial trouble.  Empire National stock was down, Gilbert’s brokers were calling for additional margin, and Gilbert was already in debt all over New York.  He owed large sums to dozens of art dealers… But he hung on gamely; when friends advised him at least to liquidate the art collection, he refused.  To sell it, he explained, would be to lose face.

However, Gilbert was saved when Bruce stock increased sharply.  This led Gilbert to want to have Bruce acquire Celotex Corporation, a large manufacturer of building-insulation materials.  Gilbert acquired as much Celotex stock as he could.  He put his friends and family into Celotex.  Even his old enemies the Bruce family authorized Gilbert’s use of $1.4 million of the company’s money to buy Celotex shares.

But then Gilbert’s fortunes reversed again.  The stock market started to go sour.  Moreover, Gilbert’s marriage was on the rocks.  Gilbert moved to Las Vegas in order to stay there the 6-week period required for a Nevada divorce.

Gilbert kept his residence in Las Vegas as much of a secret as he could.  The few people from Bruce who were allowed to know where Gilbert was were sworn to secrecy.

While in Vegas, Gilbert would be up at dawn, since the markets opened at 7:00 A.M. Nevada time.  In the afternoons, Gilbert went to the casinos to gamble.  He later admitted that his gambling losses were heavy.

Meanwhile, the stock market continued to decline.  Eddie Gilbert was in trouble.  Most of Eddie’s friends—who held Celotex on margin—were also in trouble.

Gilbert himself had all but exhausted his borrowing power.  His debts to brokers, to friends, to Swiss bankers, to New York loan sharks on the fringes of the underworld, all loomed over him, and the market betrayed him daily by dropping even more.

Brooks writes:

The third week of May became for Gilbert a nightmare of thwarted pleas by telephone—pleas to lenders for new loans, pleas to brokers to be patient and not sell him out, pleas with friends to stick with him just a little longer.  But it was all in vain, and in desperation that same week Gilbert took the old, familiar, bad-gambler’s last bad gamble—to avoid the certainty of bankruptcy he risked the possibility of criminal charges.  Gilbert ordered an official of Bruce to make out checks drawn on the Bruce treasury to a couple of companies called Rhodes Enterprises and Empire Hardwood Flooring, which were actually dummies for Gilbert himself, and he used the proceeds to shore up his personal margin calls.  The checks amounted to not quite $2 million; the act amounted to grand larceny.

Gilbert hoped that the prices of Bruce and Celotex would rise, allowing him to repay Bruce for the improper loan.  But Gilbert had a premonition that the stock prices of Bruce and Celotex were about to tumble more.  Gilbert later told The New York Times:

“I suddenly knew that I couldn’t get through this without getting hurt and getting innocent people hurt.”

Gilbert was right, as the prices of Bruce and Celotex collapsed on what turned out to be Blue Monday, the Stock Exchange’s second worse day of the century thus far.  Bruce fell to 23, down 9 3/8, while Celotex fell to 25, down 6.  In total, Gilbert lost $5 million on Blue Monday.  Furthermore, many of Gilbert’s friends who’d followed his advice also had huge losses.

Gilbert realized that if he could find a block buyer for his Celotex shares, that might allow him to repay loans, especially the improper loan from Bruce.  But Gilbert was unable to find such a buyer.  So Eddie did the last thing he felt he could—he fled to Brazil, which had no effective extradition treaty with the United States.

Suddenly Gilbert returned to the United States, despite federal and state charges against him that carried penalties adding up to 194 years in prison.  Gilbert’s father had hired Arnold Bauman, a New York criminal lawyer, who had told Gilbert that he could return to the U.S. if he promised to implicate other wrongdoers.  Gilbert never fulfilled these promises, however, and he ended up spending a bit over two years in prison.

Before going to prison, Gilbert was free on bail for four and a half years.  During that time, with more money form his father, Gilbert started and ran a new business, the Northerlin Company, flooring brokers.  He was successful for a time, allowing him to begin repaying loans.  But again he was too aggressive, and he had to sell the Northerlin Company for a tax loss.

 

PALMY DAYS AND LOW RUMBLINGS

Brooks explains how William Lucius Cary came to be appointed as chairman of the S.E.C.:

A strong Report on Regulatory Agencies to the President Elect, commissioned by the President-elect himself and written late in 1960 by James M. Landis, who had been an S.E.C. chairman in New Deal days, showed that Kennedy was bent on bringing the S.E.C. back to life, and it set the stage for the Cary regime.  Landis called for more funds as well as greater regulatory zeal, and Kennedy and Congress implemented the Landis conclusions with practical backing; between 1960 and 1964, the S.E.C.’s annual appropriation increased from $9.5 million to almost $14 million and its payroll from fewer than one thousand persons to almost fifteen hundred.  But the change was not only quantitative.  Cary concentrated on recruiting talented and enthusiastic lawyers, devoting perhaps a third of his time to the task.  His base supply naturally enough consisted of his former students and their friends; the atmosphere… soon changed from one of bureaucratic somnolence to one of academic liberal activism.

Brooks gives background on Cary:

Cary in 1962 was a lawyer of fifty-one with the gentlemanly manner and the pixyish countenance of a New England professor.  A late-starting family man, he had two children who were still tots; his wife, Katherine, was a great-great-granddaughter of America’s first world-famous novelist, James Fenimore Cooper.  His reputation among his colleagues of the bar was, as one of them put it, for “sweetness of temperament combined with fundamental toughness of fibre.”…He had grown up in and around Columbus, the son of a lawyer and president of a small utility company; he had graduated from Yale and then from Yale Law, practiced law a couple of years in Cleveland, then done a long stretch in federal government—first as a young S.E.C. assistant counsel, later as an assistant attorney general in the tax division of the Justice Department, then as an Office of Strategic Services cloak-and-dagger functionary in wartime Roumania and Yugoslavia.  In 1947 he had entered academic life, teaching law thereafter, first at Northwestern and later at Columbia.  He was in the latter post, taking one day a week off to go downtown to the “real world” of Wall Street and practice law with the firm of Patterson, Belknap and Webb, when John F. Kennedy appointed him S.E.C. chairman soon after assuming the Presidency in January 1961.

Brooks then states:

Two actions during his first year in office gave the financial district an inkling of Cary’s mettle and the S.E.C.’s new mood.

The first case was In the Matter of Cady, Roberts and Co., which related to events that occurred two years before.  A young broker of Cady, Roberts and Co., Robert M. Gintel, had received information that Curtiss-Wright Corporation was about to seriously cut its quarterly dividend.  Gintel immediately sold 7,000 shares for his firm’s customers.  This violated Rule 10B-5 of the S.E.C. against trading based on privileged information.  Gintel was suspended from trading for twenty days.  It seemed like a light sentence.

But so firmly entrenched was the Wall Street tradition of taking unfair advantage of the larger investing public, and so lax the S.E.C.’s administration of that particular part of the law between 1942 and 1961, that not a single stockbroker had ever been prosecuted for improper use of privileged information during those two decades.

The second action led by Cary involved a two-year Special Study of the securities markets.  The study was released in three parts.

Specifically, the first installment said that insider-trading rules should be tightened; standards of character and competence for stockbrokers should be raised; further curbs should be put on the new-issues market; and S.E.C. surveillance should be extended to the thousands of small-company stocks traded over the counter that had previously been free of federal regulation…

The second part of the study… concentrated on stock-exchange operations, recommending that brokers’ commissions on trades be lowered, that the freedom of action of specialists be drastically curtailed, and that floor traders—those exchange members who play the market with their own money on the floor itself, deriving from their membership the unique advantages over nonmembers of being at the scene of action and of paying no commissions to brokers—be legislated right out of existence through the interdiction of their activities.

Brooks continues:

The third and final part… was probably the harshest of the three—and in view of political realities the most quixotic.  Turning its attention to the wildly growing mutual-fund business, the S.E.C. now recommended outlawing of the kind of contract, called “front-end load,” under which mutual-fund buyers agreed (and still agree) to pay large sales commissions off the top of their investment.  It also accused the New York Stock Exchange of leaning toward “tenderness rather than severity” in disciplining those of its members who have broken its rules.

Brooks comments:

All in all, the Special Study was a blueprint for a fair and orderly securities market, certainly the most comprehensive such blueprint ever drawn up, and if all of its recommendations had been promptly put into effect, what follows in this chronicle’s later chapters would be a different tale.  But, of course, they were not.

Brooks explains:

The law that was finally passed—the Securities Acts Amendments of 1964—had two main sections, one extending S.E.C. jurisdiction to include some twenty-five hundred over-the-counter stocks (about as many as were traded on the New York and American exchanges combined), and the other giving the government authority to set standards and qualifications for securities firms and their employees.

As far as it went, it was a good law, a landmark law, a signal achievement for Cary and his egghead crew.  But it fell far short of what the Special Study had asked for.  Not a word, for example, about mutual-fund abuses; no new restrictions on the activities of specialists; and nothing to alter the Stock Exchange’s habit of “tenderness” toward its erring members.  Those items had been edited out in the course of the political compromises that had made passage of the bill possible.

The “bitterest pill of all,” writes Brooks, was that the floor traders continued to be allowed to trade for their own accounts using privileged or inside information.  The Special Study had asked that such trading be outlawed.  But there were very strong objections from the Stock Exchange and then from business in general.  Their arguments referred to the freedom of the marketplace and also the welfare of the investing public.  The Stock Exchange commissioned the management firm of Cresap, McCormick and Paget to study the issue and determine if floor trading served the public or not.

Brooks observes:

Built into this situation was one of those moral absurdities that are so dismayingly common in American business life.  The Stock Exchange, largely run by floor traders and their allies, had a vested interest in finding that floor traders serve a socially useful purpose.  Cresap, McCormick and Paget, being on the Exchange payroll, had a vested interest in pleasing the Exchange…

Cresap, McCormick and Paget labored mightily.  One may imagine the Exchange’s gratification when the report, finished at last, concluded that abolition of floor trading would decrease liquidity and thereby introduce a dangerous new volatility into Stock Exchange trading, doing “irreparable farm” to the free and fair operation of the auction market.  But perhaps the Exchange’s gratification was less than complete.  The magisterial authority of the report was somewhat sullied when James Dowd, head of the Cresap team that had compiled it, stated publicly that his actual finding had been that floor trading was far from an unmixed blessing for the public, and accused the Stock Exchange of having tampered with the report before publishing it… Cary wanted to hold S.E.C. hearings on the matter, but was voted down by his fellow commissioners.

At all events, the report as finally published did not seem to be a triumph of logical thought.

Brooks concludes:

Thus frustrated, Cary’s S.E.C. came to achieve through administration much of what it had failed to achieve through legislation.

In August 1964, the S.E.C. issued strict new rules requiring Stock Exchange members to pass an exam before being permitted to be floor traders.  As well, each floor trader had to submit daily a detailed report of his or her transactions.

Shortly after imposition of the new rules, the number of floor traders on the Stock Exchange dropped from three hundred to thirty.  As an important factor in the market, floor trading was finished.  Cary had won through indirection.

 

NORTHERN EXPOSURE

There were hardly any blacks or women on Wall Street in the 1960’s.  Brooks:

Emancipated, highly competent and successful women in other fields—the arts, publishing, real estate, retail trade—still found it consistent with their self-esteem to affect a coy bewilderment when conversation turned to the stock market or the intricacies of finance.

Brooks continues:

Liberal Democrats, many of them Jewish, were about as common as conservative Republicans in the positions of power; now, one of them, Howard Stein of Dreyfus Corporation, would be the chief fund-raiser for Eugene McCarthy’s 1968 presidential campaign…

Many of the men putting together the stock market’s new darlings, the conglomerates, were liberals—and, of course, it didn’t hurt a Wall Street analyst or salesman to be on close and sympathetic terms with such men.  There were even former Communists high in the financial game.

Between 1930 and 1951, very few young people went to work on Wall Street.  Brooks writes:

Indeed, by 1969, half of Wall Street’s salesmen and analysts would be persons who had come into the business since 1962, and consequently had never seen a bad market break.  Probably the prototypical portfolio hotshot of 1968 entered Wall Street precisely in 1965… Portfolio management had the appeal of sports—that one cleanly wins or loses, the results are measurable in numbers; if one’s portfolio was up 30 or 50 percent for a given year one was a certified winner, so recognized and so compensated regardless of whether he was popular with his colleagues or had come from the right ancestry or the right side of the tracks.

Brooks describes:

It was open season now on Anglo-Saxon Protestants even when they stayed plausibly close to the straight and narrow.  Their sins, or alleged sins, which had once been so sedulously covered up by press and even government, were now good politics for their opponents.  They had become useful as scapegoats—as was perhaps shown in the poignant personal tragedy of Thomas S. Lamont.  Son of Thomas W. Lamont, the Morgan partner who may well have been the most powerful man in the nation in the nineteen twenties, “Tommy” Lamont was an amiable, easygoing man.  He was a high officer of the Morgan Guaranty Trust Company and a director of Texas Gulf Sulphur Company, and on the morning—April 16, 1964—when Texas Gulf publicly announced its great Timmins ore strike, he notified one of his banking colleagues of the good news at a moment when, although he had reason to believe that it was public knowledge, by the S.E.C.’s lights in fact it was not.  The colleague acted quickly and forcefully on Lamont’s tip, on behalf of some of the bank’s clients; then, almost two  hours later, when news of the mine was unquestionably public, Lamont bought Texas Gulf stock for himself and his family.

Lamont had known for several days earlier, and had done nothing.  And when he informed his colleague about the Timmins ore strike, he believed that the information was already public knowledge.  According to the S.E.C., however, the insider trading rule also required one to wait “a reasonable amount of time,” so that the news could be digested.  Brooks:

In so doing, it lumped [Lamont] with flagrant violators, some Texas Gulf geologists and executives who had bought stock on the strength of their knowledge of Timmins days and months earlier, and who made up the bulk of the S.E.C.’s landmark insider case of 1966.

Could it be, then, that the S.E.C. knew well enough that it had a weak case against Lamont, and dragged him into the suit purely for the publicity value of his name?  The outlandishness of the charge against him, and the frequency with which his name appeared in newspaper headlines about the case, suggest such a conclusion.

In the end, all charges against Lamont were dropped, while virtually no charges against the other defendants were dropped.  Unfortunately, before this happened, Lamont’s health had declined and he had passed away.

Brooks continues:

The Texas Gulf ore strike at Timmins in early 1964 had dramatically shown Canada to United States investors as the new Golconda.  Here was a great, undeveloped land with rich veins of dear metals lying almost untouched under its often-frozen soil; with stocks in companies that might soon be worth millions selling for nickels or dimes on Bay Street, the Wall Street of Toronto; and with no inconvenient Securities and Exchange Commission on hand to monitor the impulsiveness of promoters or cool the enthusiasm of investors.  American money flowed to Bay Street in a torrent in 1964 and early in 1965, sending trading volume there to record heights and severely overtaxing the facilities of the Toronto Stock Exchange.  Copies of The Northern Miner, authoritative gossip sheet of the Canadian mining industry, vanished from south-of-the-border newsstands within minutes of their arrival; some Wall Street brokers, unwilling to wait for their copies, had correspondents in Toronto telephone them the Miner‘s juicier items the moment it was off the press.  And why not?  Small fortunes were being made almost every week by quick-acting U.S. investors on new Canadian ore strikes, or even on rumors of strikes.  It was as if the vanished western frontier, with its infinite possibilities both spiritual and material had magically reappeared, with a new orientation ninety degrees to the right of the old one.

The Canadian economy in general was growing fast along with the exploitation of the nation’s mineral resources, and among the Canadian firms that had attracted the favorable attention of U.S. investors, long before 1964, was Atlantic Acceptance Corporation, Ltd., a credit firm, specializing in real-estate and automobile loans, headed by one Campbell Powell Morgan, a former accountant with International Silver Company of Canada, with an affable manner, a vast fund of ambition, and, it would appear later, a marked weakness for shady promoters and a fatal tendency toward compulsive gambling.

In 1955, two years after founding Atlantic, Morgan sought to raise money from Wall Street at a time when some on Wall Street thought they could make profits in Toronto.  Morgan knew Alan T. Christie, another Canadian.  Christie was a partner in “the small but rising Wall Street concern of Lambert and Company.”

At Christie’s recommendation, Lambert and Company in 1954 put $300,000 into Atlantic Acceptance, thereby becoming Atlantic’s principal U.S. investor and chief booster in Wall Street and other points south.

Brooks again:

The years passed and Atlantic seemed to do well, its annual profits steadily mounting along with its volume of loans.  Naturally, it constantly needed new money to finance its continuing expansion.  Lambert and Company undertook to find the money in the coffers of U.S. investing institutions; and Jean Lambert, backed by Christie, had just the air of European elegance and respectability, spiced with a dash of mystery, to make him perfectly adapted for the task of impressing the authorities of such institutions.

Christie first contacted Harvey E. Mole, Jr., head of the U.S. Steel and Carnegie Pension Fund.  Brooks:

Christie made the pitch for Steel to invest in Atlantic.  Mole, born in France but out of Lawrenceville and Princeton, was no ramrod-stiff traditional trustee type; rather, he fancied himself, not without reason, as a money manager with a component of dash and daring.  Atlantic Acceptance was just the kind of relatively far-out, yet apparently intrinsically sound, investment that appealed to Mole’s Continental sporting blood.  The Steel fund took a bundle of Atlantic securities, including subordinate notes, convertible preferred stock, and common stock, amounting to nearly $3 million.

The following year, Lambert and Company convinced the Ford Foundation to invest in Atlantic Acceptance.  Brooks comments:

After that, it was easy.  With the kings of U.S. institutional investing taken into camp, the courtiers could be induced to surrender virtually without a fight.  Now Lambert and Company could say to the fund managers, “If this is good enough for U.S. Steel and the Ford Foundation, how can you lose?”  “We were all sheep,” one of them would admit, sheepishly, years later.  Before the promotion was finished, the list of U.S. investors in Atlantic had become a kind of Burke’s Peerage of American investing institutions: the Morgan Guaranty and First National City Banks; the Chesapeake and Ohio Railway; the General Council of Congregational Churches; Pennsylvania and Princeton Universities (perhaps not coincidentally, the man in charge of Princeton’s investment program was Harvey Mole); and Kuhn, Loeb and Company, which, to the delight of Lambert, gave the enterprise its valuable imprimatur by taking over as agent for the sale of Atlantic securities in the United States.  Perhaps the final turn of the screw, as the matter appears in hindsight, is the fact that the list of Atlantic investors eventually included Moody’s Investors Service, whose function is to produce statistics and reports designed specifically to help people avoid investment pitfalls of the sort of which Atlantic would turn out to be an absolutely classic case.

In the early 1960’s, Atlantic’s sales were increasing nearly 100 percent per year.  It seemed that the company was exceeding what anyone could have expected.  Of course, in the loan business, you can increase volumes significantly by making bad loans that are unlikely to be repaid.  Brooks:

In fact, that was precisely what Atlantic was doing, intentionally and systematically.

However, having such investors as the Steel fund, the Ford Foundation, etc., made it easy to dismiss critics.

Late in 1964, Atlantic, hungry for capital as always, sold more stock; and early in 1965, Kuhn, Loeb helped place $8.5 million more in Atlantic long-term debt with U.S. institutional investors.  By this time, Lambert and Company’s stake in Atlantic amounted to $7.5 million.  The firm’s commitment was a do-or-die matter; it would stand or fall with Atlantic.  Moreover, it is now clear that by this time Morgan and his associates were engaged in conducting a systematic fraud on a pattern not wholly dissimilar to that of Ponzi or Ivar Kreuger.  Atlantic would use the new capital flowing from Wall Street to make new loans that its major officers knew to be unsound; the unsoundness would be deliberately camouflaged in the company’s reports, in order to mislead investors; the spurious growth represented by the ever-increasing loans would lure in new investment money, with which further unsound loans would be made; and so on and on.  Morgan had taken to intervening personally each year in the work of his firm’s accountants—some of whom were willing enough to commit fraud at their client’s request—to ensure that a satisfactory rise in profits was shown through overstatement of assets and understatement of allowances for bad debts.  For 1964, it would come out later, Atlantic’s announced $1.4 million profit, under proper accounting procedure, should have been reported as a loss of $16.6 million.

Brooks continues:

The game, like all such games, could not go on forever.  By early 1965, suspicion of Atlantic’s operations was in the wind.  In April, the New York Hanseatic Corporation, a $12-million investor in Atlantic paper, asked the Toronto-Dominion Bank for a credit check on Atlantic.  The response—which in retrospect appears dumbfounding—was favorable.  In fact, if the bank had been able to penetrate the mystifications of Powell’s accountants, it would have discovered that Atlantic was by that time actually insolvent.  For several years, at the instigation of some of the various international schemers for whom Morgan had a fatal affinity, the firm had been increasingly involved in a desperate and doomed plunge in a shaky venture far from home: between 1963 and early 1965 it had committed more than $11 million to the Lucayan Beach, a hotel with a gambling casino attached, on balmy, distant Grand Bahama Island.  A Royal Commission would later describe the investment as “the last throw of the dice to retrieve all the losses created by years of imprudence and impropriety.”  But the Lucayan Beach venture, managed incompetently and fraudulently, did not flourish, and the losses were not to be retrieved.

On June 15, Atlantic went into default.  It needed $25 million to cure the situation.  “Of course, it neither had not could raise such a sum.”  Brooks comments:

The Old Establishment of U.S. investing had fallen for its own fading mystique.  Believing, with tribal faith that can only be called touching, that no member of the club could make a serious mistake, the members had followed each other blindly into the crudest of traps, and had paid the price for their folly.

Brooks concludes: “…the Atlantic episode neatly divides Wall Street’s drama of the decade, ending the first act, and beginning the second and climactic one.”

 

THE BIRTH OF GO-GO

Brooks defines the term “go-go” as a method of investing:

The method was characterized by rapid in-and-out trading of huge blocks of stock, with an eye to large profits taken very quickly, and the term was used specifically to apply to the operation of certain mutual funds, none of which had previously operated in anything like such a free, fast, or lively manner.

The mood and the method seem to have started, of all places, in Boston, the home of the Yankee trustee.  The handling of other people’s money in the United States began in Boston, the nation’s financial center until after the Civil War.  Trusteeship is by its nature conservative—its primary purpose being to conserve capital—and so indeed was the type of man it attracted in Boston.  Exquisitely memorialized in the novels of John P. Marquand, for a century the Boston trustee was the very height of unassailable probity and sobriety: his white hair neatly but not too neatly combed; his blue Yankee eyes untwinkling, at least during business hours; the lines in his cheeks running from his nose to the corners of his mouth forming a reassuringly geometric isoceles triangle; his lips touching liquor only at precisely set times each day, and then in precise therapeutic dosage; his grooming impeccable (his wildest sartorial extravagance a small, neat bow tie) with a single notable exception—that he wore the same battered gray hat through his entire adult life, which, so life-preserving was his curriculum, seldom ended before he was eighty-five or ninety.

Brooks writes about the Boston-born “prudent man rule.”  In 1830, Justice Samuel Putnam of the Supreme Judicial Court of Massachusetts wrote in a famous opinion:

All that can be required of a Trustee to invest is that he conduct himself faithfully and exercise a sound discretion.  He is to observe how men of prudence, discretion, and intelligence manage their own affairs, not in regard to speculation, but in regard to permanent disposition of their funds, considering the probable income, as well as the probable safety of the capital to be invested.

Brooks then writes that Boston, in 1924, was the location of “another epoch-making innovation in American money management, the founding of the first two mutual funds, Massachusetts Investors Trust and State Street Investing Company.”  Later, after World War II, “the go-go cult quietly originated hard by Beacon Hill under the unlikely sponsorship of a Boston Yankee named Edward Crosby Johnson II.”  Brooks describes Johnson:

Although never a trustee by profession, Johnson was almost the Boston-trustee type personified.

Brooks adds:

The market bug first bit him in 1924 when he read a serialization in the old Saturday Evening Post of Edwin Lefevre’s “Reminiscences of a Stock Market Operator,” the story of the career of the famous speculator Jesse Livermore.  “I’ll never forget the thrill,” he told a friend almost a half century later.  “Everything was there, or else implied.  Here was the picture of a world in which it was every man for himself, no favors asked or given.  You were what you were, not because you were a friend of somebody, but for yourself.  And Livermore—what a man, always betting his whole wad!  A sure system for losing, of course, but the point was how much he loved it.  Operating in the market, he was like Drake sitting on the poop of his vessel in a cannonade.  Glorious!”

Eventually Johnson was asked to take over Fidelity Fund, a mutual fund with only $3 million under management.  Brooks comments:

Edward Crosby Johnson II, for all of his trustee-like ways, clearly had a speculative background and temperament; after all, his stock-market idol was one of the master speculators.

What this meant in practice was that Fidelity was willing to trade in and out of stocks, often fairly rapidly, rather than buy and hold.

Then in 1952, Johnson met Gerald Tsai, Jr.  Johnson, liking the young man’s looks, first hired Tsai as a junior stock analyst.  Tsai was born in Shanghai in 1928 to Westernized Chinese parents.  Tsai’s father had been educated at the University of Michigan, and was Shanghai district manager for the Ford Motor Company.  Tsai himself got a BA and MA in economics from Boston University.  Brooks:

“I liked the market,” he would explain years later.  “I felt that being a foreigner I didn’t have a competitive disadvantage there, when I might somewhere else.  If you buy GM at forty and it goes to fifty, whether you are an Oriental, a Korean, or a Buddhist doesn’t make any difference.”

Tsai’s reasons for liking the market were similar to Johnson’s reasons: “you were what you were not because you were a friend of somebody, but for yourself.”  Brooks continues:

At Fidelity, Tsai was not long in making his mark.  Always impeccably groomed, his moon face as impassive as a Buddha, he showed himself to be a shrewd and decisive picker of stocks for shot-term appreciation…

Tsai explained later that Johnson gave you your head—a chance to work on your own rather than as part of a committee—but he simultaneously gave you your rope, saying “God ahead and hang yourself with it.”  Tsai also quoted Johnson as saying, “Do it by yourself.  Two men can’t play a violin.”

Soon Tsai asked Johnson if he could launch a speculative growth fund.  Johnson said yes in the space of an hour, saying, “Go ahead.  Here’s your rope.”

Tsai’s rope was called Fidelity Capital Fund, and it was the company’s first frankly speculative public growth fund.  Right from the start, he operated it in a way that was at the time considered almost out-and-out gambling.  He concentrated Fidelity Capital’s money in a few stock that were then thought to be outrageously speculative and unseasoned for a mutual fund (Polaroid, Xerox, and Litton Industries among them).

Brooks notes:

As once “Jesse Livermore is buying it!” had been the signal for a general stampede into any stock, so now it was “Gerry Tsai is buying it!”  Like Livermore’s, his prophecies by force of his reputation came to be to a certain extent self-fulfilling.

Brooks also writes about the invention of the “hedge fund,” so named because, unlike a mutual fund, a hedge fund could operate on margin and make short sales.  Brooks describes the man who invented the hedge fund:

He was Alfred Winslow Jones, no sideburned gunslinger but a rather shy, scholarly journalist trained in sociology and devoted to good works.  Born in Australia at the turn of the century to American parents posted there by General Electric, he graduated from Harvard in 1923, got a Ph.D. in sociology from Columbia, served in the foreign service in Berlin during the thirties, and became a writer for Time-Life in the forties.

In 1949, Winslow got the idea for a hedge fund.  He raised $100,000 in investment capital, $40,000 of it his.  The first hedge fund did well, even in the bad market of 1962 because of its capacity to sell short.  Its clients were mainly writers, teachers, scholars, social workers.  (One client was Sam Stayman, the bridge expert.)  Winslow’s fund showed a five-year gain of 325 percent and a ten-year gain of more than twice that amount.  The fund took 20 percent of profits as an annual fee.

Brooks adds:

Alfred Jones, in his own middle sixties, had made so much money out of A. W. Jones and Company’s annual 20 percents that he could well afford to indulge his predilections.  Spending less and less time at his office on Broad Street, he devoted himself more and more to a personal dream of ending all poverty.  Considering material deprivation in the land of affluence to be a national disgrace, he set up a personal foundation devoted to mobilizing available social skills against it… Jones could afford to go the way of the aristocrat, treating money-making as something too simple to be taken very seriously, and putting his most profound efforts into work not in the cause of profit but in that of humanity.

In late 1965, Tsai left Fidelity and formed his own mutual fund, the Manhattan Fund.  Tsai set out to raise $25 million, but he ended up raising $247 million.  The only problem for Tsai was that the bull market had peaked.  Investors expected Tsai to make 50 percent a year, but he could only do so if the bull market continued.  Brooks comments:

But if Tsai no longer seemed to know when to cash in the investments he made for others, he knew when to cash in his own.  In August, 1968, he sold Tsai Management and Research to C.N.A. Financial Corporation, an insurance holding company, in exchange for a high executive post with C.N.A. and C.N.A. stock worth in the neighborhood of $30 million.

 

THE CONGLOMERATEURS

Brooks defines the term:

Derived from the Latin word glomus, meaning wax, the word suggests a sort of apotheosis of the old Madison Avenue cliche “a big ball of wax,” and is no doubt apt enough; but right from the start, the heads of conglomerate companies objected to it.  Each of them felt that his company was a mesh of corporate and managerial genius in which diverse lines of endeavor—producing, say, ice cream, cement, and flagpoles—were subtly welded together by some abstruse metaphysical principle so refined as to be invisible to the vulgar eye.  Other diversified companies, each such genius acknowledged, were conglomerates; but not his own.

In 1968, 26 of the country’s 500 biggest companies disappeared through conglomerate merger.  Some of the largest targets were acquired by companies much smaller than themselves.  Moreover, enthusiasts were saying that eventually 200 super-conglomerates would be doing most of the national business.  There would only be a handful of non-conglomerates left.  Brooks observes:

The movement was new and yet old.  In the nineteenth century, few companies diversified their activities very widely by acquiring other companies or by any other means.  There is, on the face of it, no basic reason for believing that a man who can successfully run an ice cream business should not be able to successfully run an ice-cream-and-cement business, or even an ice-cream-cement-and-flagpole business.  On the other hand, there is no reason for believing that he should be able to do so.  In the Puritan and craft ethic that for the most part ruled nineteenth-century America, one of the cardinal precepts was that the shoemaker should stick to his last.

Brooks notes that it was during the 1950’s that “really uninhibited diversification first appeared.  Brooks:

During that decade, National Power and Light, as a result of its purchase of another company, found itself chiefly engaged in peddling soft drinks; Borg-Warner, formerly a maker of automotive parts, got into refrigerators, other consumer products; and companies like Penn-Texas and Merritt Chapman and Scott, under the leadership of corporate wild men like David Carr and Louis E. Wolfson, took to ingesting whatever companies swam within reach.  Among the first companies to be called conglomerates were Litton, which in 1958 began to augment its established electronics business with office calculators and computers and later branched out into typewriters, cash registers, packaged foods, conveyor belts, oceangoing ships, solder, teaching aids, and aircraft guidance systems, and Textron, once a placid and single-minded New England textile company, and eventually a purveyor of zippers, pens, snowmobiles, eyeglass frames, silverware, golf carts, metalwork machinery, helicopters, rocket engines, ball bearings, and gas meters.

Brooks lists the factors involved in the conglomerate explosion:

    • corporate affluence
    • a decline of the stick-to-your-last philosophy among businessmen
    • a decline of the stick-to-anything philosophy among almost everyone else
    • a rise in the influence of graduate business schools, led by Harvard, which in the 1960’s were trying to enshrine business as a profession, and often taught that managerial ability was an absolute quality, not limited by the type of business being managed
    • federal antitrust laws, which forbade most mergers between large companies in the same line of business

One additional factor was that many investors focused on just one metric: the price-to-earnings (P/E) ratio.  Brooks clarifies why this isn’t a reliable guide when investing in a conglomerate: when a company with a high P/E buys a company with a low P/E, earnings per share increases.  Brooks:

There is an apparent growth in earnings that is entirely an optical illusion.  Moreover, under accounting procedures of the late nineteen sixties, a merger could generally be recorded in either of two ways—as a purchase of one company by another, or as a simple pooling of the combined resources.  In many cases, the current earnings of the combined company came out quite differently under the two methods, and it was understandable that the company’s accountants were inclined to choose arbitrarily the method that gave the more cheerful result.  Indeed, the accountant, through this choice and others as his disposal, was often able to write for the surviving company practically any current earnings figure he chose—a situation that impelled one leading investment-advisory service to issue a derisive bulletin entitled, “Accounting as a Creative Art.”

Brooks continues:

The conglomerate game tended to become a form of pyramiding… The accountant evaluating the results of a conglomerate merger would apply his creative resources by writing an earnings figure that looked good to investors; they, reacting to the artistry, would buy the company’s stock, thereby forcing its market price up to a high multiple again; the company would then make the new merger, write new higher earnings, and so on.

 

THE ENORMOUS BACKROOM

Brooks writes:

Nineteen sixty-eight was to be the year when speculation spread like a prairie fire—when the nation, sick and disgusted with itself, seemed to try to drown its guilt in a frenetic quest for quick and easy money.  “The great garbage market,” Richard Jenrette called it—a market in which the “leaders” were neither old blue chips like General Motors and American Telephone nor newer solid starts like Polaroid and Xerox, but stock with names like Four Seasons Nursing Centers, Kentucky Fried Chicken, United Convalescent Homes, and Applied Logic.  The fad, as in 1961, was for taking short, profitable rides on hot new issues.

As trading volume increased, back-office troubles erupted.  Brooks:

The main barometric measuring-device for the seriousness of back-office trouble was the amount of what Wall Street calls “fails.”  A fail, which might more bluntly be called a default, occurs when on the normal settlement date for any stock trade–five days after the transaction–the seller’s broker for some reason does not physically deliver the actual sold stock certificates to the buyer’s broker, or the buyer’s broker for some reason fails to receive it.

The reasons for fails typically are that either the selling broker can’t find the certificates being sold, the buying broker misplaces them, or one side or the other makes a mistake in record-keeping by saying that the stock certificates have not been delivered when in fact they have been.

Lehman Brothers, in particular, was experiencing a high level of fails.

Stock discrepancies at the firm, by the end of May, ran into hundreds of millions of dollars.  Lehman reacted by eliminating a few accounts, ceasing the make markets in over-the-counter stocks, and refusing further orders for low-priced securities; it did not augment these comparatively mild measures with drastic ones—the institution of a crash program costing half a million dollars to eliminate stock record errors—until August, when the S.E.C. threatened to suspend Lehman’s registration as a broker-dealer and thus effectively put it out of business.  Lehman’s reluctance to act promptly to save its customers’ skins, and ultimately its own, was all too characteristic of Wall Street’s attitude toward its troubles in 1968.

Brooks comments:

Where were the counsels of restraint, not to say common sense, in both Washington and on Wall Street?  The answer seems to lie in the conclusion that in America, with its deeply imprinted business ethic, no inherent stabilizer, moral or practical, is sufficiently strong in and of itself to support the turning away of new business when competitors are taking it on.  As a people, we would rather face chaos making potsfull of short-term money than maintain long-term order and sanity by profiting less.

 

GO-GO AT HIGH NOON

Brooks on New York City in 1968:

Almost all of the great cultural centers of history have first been financial centers.  This generalization, for which New York City provides a classic example, is one to be used for purposes of point-proving only with the greatest caution.  To conclude from it that financial centers naturally engender culture would be to fall into the most celebrated of logical fallacies.  It is nonetheless a suggestive fact, and particularly so in the light of 1968 Wall Street, standing as it was on the toe of the same rock that supported Broadway, off-Broadway, Lincoln Center, the Metropolitan Museum, the Museum of Modern Art, and Greenwich Village.

Brooks then writes about changes on Wall Street:

Begin with the old social edifices that survived more or less intact.  In many instances they were Wall Street’s worst and most dispensable; for example, its long-held prejudices, mitigated only by tokenism, against women and blacks.

A few women—but not many—were reaching important positions.  Meanwhile, for blacks, Wall Street “had advanced the miniscule distance from the no-tokenism of 1965 to tokenism at the end of the decade.”

 

CONFRONTATION

Brooks writes:

Spring of 1969—a time that now seems in some ways part of another, and a more romantic, era—was in the business world a time of Davids and Goliaths: of threatened takeovers of venerable Pan American World Airways by upstart Resorts International, for example, and of venerable Goodrich Tire and Rubber by upstart Northwest Industries… Undoubtedly, though, the David-and-Goliath act of early 1969 that most caught the popular imagination was an attempt upon the century-and-a-half-old Chemical Bank New York Trust Company (assets a grand $9 billion) by the eight-year-old Leasco Data Processing Equipment Corporation of Great Neck, Long Island (assets a mere $400 million), a company entirely unknown to almost everyone in the larger business community without a special interest in either computer leasing, Leasco’s principal business until 1968, or in the securities market, in which its stock was a star performer.  In that takeover contest, the roles of Goliath and David were played, with exceptional spirit, by William Shryock Renchard of the Chemical and Saul Phillip Steinberg of Leasco.

Renchard was from Trenton, New Jersey, and although he attended Princeton, he probably was not expected to amount to much.  That is, he didn’t stand out at all at Princeton and his senior yearbook said, “Renchard is undecided as to his future occupation.”

By 1946, at the age of thirty-eight, Renchard was a vice president of Chemical Bank and Trust Company.  In 1955, he became executive vice president; in 1960, he was made president; and in 1966, he was made chairman of the board of what was now called Chemical Bank New York Trust Company.  By that time, the bank had $9 billion in assets and was the country’s six largest commercial bank.

Saul Phillip Steinberg was from Brooklyn and was a full generation younger than Renchard.  Steinberg was unexceptional, although he did develop an early habit of reading The Wall Street Journal.  He attended the Wharton School of Finance and Commerce at the University of Pennsylvania.

Steinberg researched I.B.M., expecting to find negative things.  Instead, he learned that I.B.M. was a brilliantly run company.  He also concluded that I.B.M.’s industrial computers would last longer than was assumed.  So he started a business whereby he purchased I.B.M. computers and then leased them out for longer terms and for lower rates than I.B.M. itself was offering.

In 1964, two years after launching his business—Ideal Leasing Company—earnings were $255,000 and revenues were $8 million.  Steinberg then decided to go public, and the company’s name was changed to Leasco Data Processing Equipment Corporation.  Public sale of Leasco stock brought in $750,000.  Leasco’s profits skyrocketed: 1967 profits were more than eight times 1966 profits.  Brooks:

As might be expected of a young company with ambition, a voracious need for cash, and a high price-to-earnings multiple, Leasco became acquisition-minded… In 1966 and 1967, Leasco increased its corporate muscle by buying several small companies in fields more or less related to computers or to leasing… These acquisitions left the company with $74 million in assets, more than eight hundred employees, larger new headquarters in Great Neck, Long Island, and a vast appetite for further growth through mergers.

Diversified companies learned that if they acquired or merged with a fire-and-casualty company, then the otherwise restricted cash reserves—”redundant capital”—of the fire-and-casualty company could be put to use.  Thus, Leasco got the idea of acquiring Reliance Insurance Company, a Philadelphia-based fire-and-casualty underwriter “with more than five thousand employees, almost $350 million in annual revenues, and a fund of more than $100 million in redundant capital.”  Brooks writes:

Truly—to change the metaphor—it was a case of the minnow swallowing the whale; Reliance was nearly ten times Leasco’s size, and Leasco, as the surviving company, found itself suddenly more than 80 percent in the insurance business and less than 20 percent in the computer-leasing business.

Brooks adds:

[Leasco] suddenly had assets of $400 million instead of $74 million, net annual income of $27 million instead of $1.4 million, and 8,500 employees doing business in fifty countries instead of 800 doing business in only one.

Brooks notes that Leasco’s stock had, over the previous five years, increased 5,410 percent making Leasco “the undisputed king of all the go-go stocks.”  Now comes the story of Leasco and Chemical Bank.

Leasco had gotten interested in acquiring a bank.  Banks often sold at low price-to-earnings ratio’s, giving Leasco leverage in a takeover.  Also, Steinberg thought “that it would be advantageous to anchor Leasco’s diversified financial services to a New York money-center bank with international connections.”  By the fall of 1968, Leasco was zeroing in on Renchard’s $9-billion Chemical Bank.

Leasco had begun buying shares in Chemical and had prepared a hypothetical tender offer involving warrants and convertible debentures when Chemical learned of Leasco’s intended takeover.  Brooks:

…Renchard was in no doubt as to Chemical’s response.  He and his bank were going to fight Leasco with all their strength.  True enough, a merger, as in the Reliance case, would result in immediate financial benefit to the stockholders of both companies.  But it seemed to Renchard and his colleagues that more than immediate stockholder profit was involved.  The century-and-a-half-old Chemical Bank a mere division of an unseasoned upstart called Leasco?

Renchard organized an eleven-man task force to come up with a strategy for fighting off any takeover attempt.  Renchard commented later:

“We were guessing that they would offer stuff with a market value of around $110 for each share of our stock, which was then selling at $72.  So we knew well enough it would be tough going persuading our stockholders not to accept.”

First, Renchard leaked the story to The New York Times.  The Times published a piece that included the following:

Can a Johnny-come-lately on the business scene move in on the Establishment and knock off one of the biggest prizes in sight?

[…]

Is Chemical in the bag?  Hardly.  William S. Renchard, chairman of the Chemical Bank, sounded like a Marine Corps colonel in presenting his battle plan…

One strategy Chemical came up with was to attack the value of Leasco stock by selling it or shorting it.  This approach was discussed at a February 6 strategy meeting, but no one afterwards was ever willing to admit it.  Brooks:

The striking and undeniable fact is, however, that on that very day, Leasco stock, which had been hovering in the stratosphere at around 140, abruptly began to fall in price on large trading volume.  By the close the following day Leasco was down almost seven points, and over the following three weeks it would drop inexorably below 100.

Chemical planned a full-scale strategy meeting:

At the Chemical strategy meeting—which was attended, this time, not only by Chemical’s in-house task force, but by invitees from other powerful Wall Street institutions sympathetic to the Chemical cause, including First Boston, Kuhn Loeb, and Hornblower Weeks—a whole array of defensive measures were taken up and thrashed out, among them the organizing of telephone teams to contact Chemical stockholders; the retaining of the leading proxy-soliciting firms solely to deny their services to Leasco; and the possibility of getting state and federal legislation introduced through the bankers’ friends in Albany and Washington in order to make a Leasco takeover of Chemical illegal.  Despite the availability of such weapons, the opinion of those present seemed to be that Leasco’s venture had an excellent chance of success.

Finally, Renchard and Steinberg met for lunch.  Brooks writes:

One may imagine the first reactions of the antagonists to each other.  One was lean, iron-gray, of distinctly military bearing; a North Shore estate owner, very conscious of the entrenched power of the nation standing behind him, very much a man of few and incisive words.  The other was round-faced, easy-smiling, a man of many words who looked preposterously younger than his already preposterous twenty-nine years, and given, as he talked, to making windmill gestures with his arms and suddenly jumping galvanically up from his chair; a South Shore estate owner…; a young man bubbling with energy and joy in living.

During the meeting, Steinberg said he wanted it to be a friendly takeover.  Renchard seemed to be open to that possibility.  At the same time, Renchard said that he was “a pretty good gutter fighter,” to which Steinberg replied that his own record as a gutter fighter “was considered to be pretty good, too.”

A second meeting was held.  This time, Renchard and Steinberg brought their chief aides.  Steinberg put more emphasis on his friendly intentions, and he conceded that he would be willing to not be the chief executive of the merged entity.  Renchard said they had lots to consider and would get back in touch shortly.

Then Chemical held another full-scale battle meeting at which they considered several possible options.  They thought about changing their company’s charter to make a Leasco takeover legally difficult if not impossible.  They floated the idea of buying a fire-and-casualty company to create an antitrust conflict with Leasco’s ownership of Reliance.  They even talked about arranging to have a giant insurance company take over Chemical.  Brooks notes:

Probably the most effective of Chemical’s various salvos was on the legislative front… Richard Simmons of the Cravath law firm, on retainer from Chemical, began devoting full time to the Leasco affair, concentrating his attention on the drafting of laws specifically designed to prevent or make difficult the takeover of banks similar to Chemical by companies that resembled Leasco, and getting these drafts introduced as bills in the State Legislature in Albany and the Congress in Washington.

Simmons’ anti-bank-takeover bill was introduced in Albany and was passed.  Moreover, a Wall Street Journal article questioned Leasco’s earnings prospects.  As well, the Department of Justice sent a letter to Leasco raising the possibility that the proposed takeover might violate antitrust laws.  In truth, the proposed takeover did not violate antitrust laws.  How the Justice Department came to send such a letter has never been explained, observes Brooks.

At this point, Steinberg decided to abandon the effort to merge with Chemical.  Brooks quotes Steinberg:

“Nobody was objective… bankers and businessmen I’d never met kept calling up out of the blue and attacking us for merely thinking about taking over a big bank.  Some of the attacks were pretty funny—responsible investment bankers taking as if we were using Mafia tactics.. Months after we’d abandoned our plans, executives of major corporations were still calling up and ranting, ‘I feel it was so wrong, what you tried to do—’  And yet they could never say why… I still don’t know exactly what it was.”

 

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

Kahneman and Tversky

August 7, 2022

If we’re more aware of cognitive biases today than a decade or two ago, that’s thanks in large part to the research of the Israeli psychologists Daniel Kahneman and Amos Tversky.  I’ve written about cognitive biases before, including:

I’ve seen few books that do a good job covering the work of Kahneman and Tversky.  The Undoing Project: A Friendship That Changed Our Minds, by Michael Lewis, is one such book.  (Lewis also writes well about the personal stories of Kahneman and Tversky.)

Why are cognitive biases important?  Economists, decision theorists, and others used to assume that people are rational.  Sure, people make mistakes.  But many scientists believed that mistakes are random: if some people happen to make mistakes in one direction—estimates that are too high—other people will (on average) make mistakes in the other direction—estimates that are too low.  Since the mistakes are random, they cancel out, and so the aggregate results in a given market will nevertheless be rational.  Markets are efficient.

For some markets, this is still true.  Francis Galton, the English Victorian-era polymath, wrote about a contest in which 787 people guessed at the weight of a large ox.  Most participants in the contest were not experts by any means, but ordinary people.  The ox actually weighed 1,198 pounds.  The average guess of the 787 guessers was 1,197 pounds, which was more accurate than the guesses made by the smartest and the most expert guessers.   The errors are completely random, and so they cancel out.

This type of experiment can easily be repeated.  For example, take a jar filled with pennies, where only you know how many pennies are in the jar.  Pass the jar around in a group of people and ask each person—independently (with no discussion)—to write down their guess of how many pennies are in the jar.  In a group that is large enough, you will nearly always discover that the average guess is better than any individual guess.  (That’s been the result when I’ve performed this experiment in classes I’ve taught.)

However, in other areas, people do not make random errors, but systematic errors.  This is what Kahneman and Tversky proved using carefully constructed experiments that have been repeated countless times.  In certain situations, many people will tend to make mistakes in the same direction—these mistakes do not cancel out.  This means that the aggregate results in a given market can sometimes be much less than fully rational.  Markets can be inefficient.

Outline (based on chapters from Lewis’s book):

  • Introduction
  • Man Boobs
  • The Outsider
  • The Insider
  • Errors
  • The Collision
  • The Mind’s Rules
  • The Rules of Prediction
  • Going Viral
  • Birth of the Warrior Psychologist
  • The Isolation Effect
  • This Cloud of Possibility

(Illustration by Alain Lacroix)

 

INTRODUCTION

In his 2003 book, Moneyball, Lewis writes about the Oakland Athletic’s efforts to find betters methods for valuing players and evaluating strategies.  By using statistical techniques, the team was able to perform better than many others teams even though the A’s had less money.  Lewis says:

A lot of people saw in Oakland’s approach to building a baseball team a more general lesson: If the highly paid, publicly scrutinized employees of a business that had existed since the 1860s could be misunderstood by their market, what couldn’t be?  If the market for baseball players was inefficient, what market couldn’t be?  If a fresh analytical approach had led to the discovery of new knowledge in baseball, was there any sphere of human activity in which it might not do the same?

After the publication of Moneyball, people started applying statistical techniques to other areas, such as education, movies, golf, farming, book publishing, presidential campaigns, and government.  However, Lewis hadn’t asked the question of what it was about the human mind that led experts to be wrong so often.  Why were simple statistical techniques so often better than experts?

The answer had to do with the structure of the human mind.  Lewis:

Where do the biases come from?  Why do people have them?  I’d set out to tell a story about the way markets worked, or failed to work, especially when they were valuing people.  But buried somewhere inside it was another story, one that I’d left unexplored and untold, about the way the human mind worked, or failed to work, when it was forming judgments and making decisions.  When faced with uncertainty—about investments or people or anything else—how did it arrive at its conclusions?  How did it process evidence—from a baseball game, an earnings report, a trial, a medical examination, or a speed date?  What were people’s minds doing—even the minds of supposed experts—that led them to the misjudgments that could be exploited for profit by others, who ignored the experts and relied on data?

 

MAN BOOBS

Daryl Morey, the general manager of the Houston Rockets, used statistical methods to make decisions, especially when it came to picking players for the team.  Lewis:

His job was to replace one form of decision making, which relied upon the intuition of basketball experts,  with another, which relied mainly on the analysis of data.  He had no serious basketball-playing experience and no interest in passing himself off as a jock or basketball insider.  He’d always been just the way he was, a person who was happier counting than feeling his way through life.  As a kid he’d cultivated an interest in using data to make predictions until it became a ruling obsession.

Lewis continues:

If he could predict the future performance of professional athletes, he could build winning sports teams… well, that’s where Daryl Morey’s mind came to rest.  All he wanted to do in life was build winning sports teams.

Morey found it difficult to get a job for a professional sports franchise.  He concluded that he’d have to get rich so that he could buy a team and run it.  Morey got an MBA, and then got a job consulting.  One important lesson Morey picked up was that part of a consultant’s job was to pretend to be totally certain about uncertain things.

There were a great many interesting questions in the world to which the only honest answer was, ‘It’s impossible to know for sure.’… That didn’t mean you gave up trying to find an answer; you just couched that answer in probabilistic terms.

Leslie Alexander, the owner of the Houston Rockets, had gotten disillusioned with the gut instincts of the team’s basketball experts.  That’s what led him to hire Morey.

Morey built a statistical model for predicting the future performance of basketball players.

A model allowed you to explore the attributes in an amateur basketball player that led to professional success, and determine how much weight should be given to each.

The central idea was that the model would usually give you a “better” answer than relying only on expert intuition.  That said, the model had to be monitored closely because sometimes it wouldn’t have important information.  For instance, a player might have had a serious injury right before the NBA draft.

(Illustration by fotomek)

Statistical and algorithmic approaches to decision making are more widespread now.  But back in 2006 when Morey got started, such an approach was not at all obvious.

In 2008, when the Rocket’s had the 33rd pick, Morey’s model led him to select Joey Dorsey.  Dorsey ended up not doing well at all.  Meanwhile, Morey’s model had passed over DeAndre Jordan, who ended up being chosen 35th by the Los Angeles Clippers.  DeAndre Jordan ended up being the second best player in the entire draft, after Russell Westbrook.  What had gone wrong?  Lewis comments:

This sort of thing happened every year to some NBA team, and usually to all of them.  Every year there were great players the scouts missed, and every year highly regarded players went bust.  Morey didn’t think his model was perfect, but he also couldn’t believe that it could be so drastically wrong.

Morey went back to the data and ended up improving his model.  For example, the improved model assigned greater weight to games played against strong opponents than against weak ones.  Lewis adds:

In the end, he decided that the Rockets needed to reduce to data, and subject to analysis, a lot of stuff that had never before been seriously analyzed: physical traits.  They needed to know not just how high a player jumped but how quickly he left the earth—how fast his muscles took him into the air.  They needed to measure not just the speed of the player but the quickness of his first two steps.

At the same time, Morey realized he had to listen to his basketball experts.  Morey focused on developing a process that relied both on the model and on human experts.  It was a matter of learning the strengths and weaknesses of the model, as well as the strengths and weaknesses of human experts.

But it wasn’t easy.  By letting human intuition play a role, that opened the door to more human mistakes.  In 2007, Morey’s model highly valued the player Marc Gasol.  But the scouts had seen a photo of Gasol without a shirt.  Gasol was pudgy with jiggly pecs.  The Rockets staff nicknamed Gasol “Man Boobs.”  Morey allowed this ridicule of Gasol’s body to cause him to ignore his statistical model.  The Rockets didn’t select Gasol.  The Los Angeles Lakers picked him 48th.  Gasol went on to be a two-time NBA All-Star.  From that point forward, Morey banned nicknames because they could interfere with good decision making.

Over time, Morey developed a list of biases that could distort human judgment: confirmation bias, the endowment effect, present bias, hindsight bias, et cetera.

 

THE OUTSIDER

Although Danny Kahneman had frequently delivered a semester of lectures from his head, without any notes, he nonetheless always doubted his own memory.  This tendency to doubt his own mind may have been central to his scientific discoveries in psychology.

But there was one experience he had while a kid that he clearly remembered.  In Paris, about a year after the Germans occupied the city, new laws required Jews to wear the Star of David.  Danny didn’t like this, so he wore his sweater inside out.  One evening while going home, he saw a German soldier with a black SS uniform.  The soldier had noticed Danny and picked him up and hugged him.  The soldier spoke in German, with great emotion.  Then he put Danny down, showed him a picture of a boy, and gave him some money.  Danny remarks:

I went home more certain than ever that my mother was right: people were endlessly complicated and interesting.

Another thing Danny remembers is when his father came home after being in  a concentration camp.  Danny and his mother had gone shopping, and his father was there when they returned.  Despite the fact that he was extremely thin—only ninety-nine pounds—Danny’s father had waited for them to arrive home before eating anything.  This impressed Danny.  A few years later, his father got sick and died.  Danny was angry.

Over time, Danny grew even more fascinated by people—why they thought and behaved as they did.

When Danny was thirteen years old, he moved with his mother and sister to Jerusalem.  Although it was dangerous—a bullet went through Danny’s bedroom—it seemed better because they felt they were fighting rather than being hunted.

On May 14, 1948, Israel declared itself a sovereign state.  The British soldiers immediately left.  The armies from Jordan, Syria, and Egypt—along with soldiers from Iraq and Lebanon—attacked.  The war of independence took ten months.

Because he was identified as intellectually gifted, Danny was permitted to go to university at age seventeen to study psychology.  Most of his professors were European refugees, people with interesting stories.

Danny wasn’t interested in Freud or in behaviorism.  He wanted objectivity.

The school of psychological thought that most charmed him was Gestalt psychology.  Led by German Jews—its origins were in the early twentieth century Berlin—it sought to explore, scientifically, the mysteries of the human mind.  The Gestalt psychologists had made careers uncovering interesting phenomena and demonstrating them with great flair: a light appeared brighter when it appeared from total darkness; the color gray looked green when it was surrounded by violet and yellow if surrounded by blue; if you said to a person, “Don’t step on the banana eel!,” he’d be sure that you had said not “eel” but “peel.”  The Gestalists showed that there was no obvious relationship between any external stimulus and the sensation it created in people, as the mind intervened in many curious ways.

(Two faces or a vase?  Illustration by Peter Hermes Furian)

Lewis continues:

The central question posed by Gestalt psychologists was the question behaviorists had elected to ignore: How does the brain create meaning?  How does it turn the fragments collected by the senses into a coherent picture of reality?  Why does the picture so often seem to be imposed by the mind upon the world around it, rather than by the world upon the mind?  How does a person turn the shards of memory into a coherent life story?  Why does a person’s understanding of what he sees change with the context in which he sees it?

In his second year at Hebrew Univeristy, Danny heard a fascinating talk by a German neurosurgeon.  This led Danny to abandon psychology in order to pursue a medical degree.   He wanted to study the brain.  But one of his professors convinced him it was only worth getting a medical degree if he wanted to be a doctor.

After getting a degree in psychology, Danny had to serve in the Israeli military.  The army assigned him to the psychology unit, since he wasn’t really cut out for combat.  The head of the unit at that time was a chemist.  Danny was the first psychologist to join.

Danny was put in charge of evaluating conscripts and assigning them to various roles in the army.  Those applying to become officers had to perform a task: to move themselves over a wall without touching it using only a log that could not touch the wall or the ground.  Danny and his coworkers thought that they could see “each man’s true nature.”  However, when Danny checked how the various soldiers later performed, he learned that his unit’s evaluations—with associated predictions—were worthless.

Danny compared his unit’s delusions to the Müller-Lyer optical illusion.  Are these two lines the same length?

(Müller-Lyer optical illusion by Gwestheimer, Wikimedia Commons)

The eye automatically sees one line as longer than the other even though the lines have equal length.  Even after you use a ruler to show the lines are equal, the illusion persists.  If we’re automatically fooled in such a simple case, what about in more complex cases?

Danny thought up a list of traits that seemed correlated with fitness for combat.  However, Danny was concerned about how to get an accurate measure of these traits from an interview.  One problem was the halo effect: If people see that a person is strong, they tend to see him as impressive in other ways.  Or if people see a person as good in certain areas, then they tend to assume that he must be good in other areas.  More on the halo effect: https://boolefund.com/youre-deluding-yourself/

Danny developed special instructions for the interviewers.  They had to ask specific questions not about how subjects thought of themselves, but rather about how they actually had behaved in the past.  Using this information, before moving to the next question, the interviewers would rate the subject from 1 to 5.  Danny’s essential process is still used in Israel today.

 

THE INSIDER

To his fellow Israelis, Amos Tversky somehow was, at once, the most extraordinary person they had ever met and the quintessential Israeli.  His parents were among the pioneers who had fled Russian anti-Semitism in the early 1920s to build a Zionist nation.  His mother, Genia Tversky, was a social force and political operator who became a member of the first Israeli Parliament, and the next four after that.  She sacrificed her private life for public service and didn’t agonize greatly about the choice…

Amos was raised by his father, a veterinarian who hated religion and loved Russian literature, and who was amused by things people say:

…His father had turned away from an early career in medicine, Amos explained to friends, because “he thought animals had more real pain than people and complained a lot less.”  Yosef Tversky was a serious man.  At the same time, when he talked about his life and work, he brought his son to his knees with laughter about his experiences, and about the mysteries of existence.

Although Amos had a gift for math and science—he may have been more gifted than any other boy—he chose to study the humanities because he was fascinated by a teacher, Baruch Kurzweil.  Amos loved Kurzweil’s classes in Hebrew literature and philosophy.  Amos told others he was going to be a poet or literary critic.

Amos was small but athletic.  During his final year in high school, he volunteered to become an elite soldier, a paratrooper.  Amos made over fifty jumps.  Soon he was made a platoon commander.

By late 1956, Amos was not merely a platoon commander but a recipient of one of the Israeli army’s highest awards for bravery.  During a training exercise in front of the General Staff of the Israeli Defense Forces, one of his soldiers was assigned to clear a barbed wire fence with a bangalore torpedo.  From the moment he pulled the string to activate the fuse, the soldier had twenty seconds to run for cover.  The soldier pushed the torpedo under the fence, yanked the string, fainted, and collapsed on top of the explosive.  Amos’s commanding officer shouted for everyone to stay put—to leave the unconscious soldier to die.  Amos ignored him and sprinted from behind the wall that served as cover for his unit, grabbed the soldier, picked him up, hauled him ten yards, tossed him on the ground, and threw himself on top of him.  The shrapnel from the explosion remained in Amos for the rest of his life.  The Israeli army did not bestow honors for bravery lightly.  As he handed Amos his award, Moshe Dayan, who had watched the entire episode, said, “You did a very stupid and brave thing and you won’t get away with it again.”

Amos was a great storyteller and also a true genius.  Lewis writes about one time when Tel Aviv University threw a party for a physicist who had just won the Wolf Prize.  Most of the leading physicists came to the party.  But the prizewinner, by chance, ended up in a corner talking with Amos.  (Amos had recently gotten interested in black holes.)  The following day, the prizewinner called his hosts to find out the name of the “physicist” with whom he had been talking.  They realized he had been talking with Amos, and told him that Amos was a psychologist rather than a physicist.  The physicist replied:

“It’s not possible, he was the smartest of all the physicists.”

Most people who knew Amos thought that Amos was the smartest person they’d ever met.  Moreover, he kept strange hours and had other unusual habits.  When he wanted to go for a run, he’d just sprint out his front door and run until he could run no more.  He didn’t pretend to be interested in whatever others expected him to be interested in.  Rather, he excelled at doing exactly what he wanted to do and nothing else.  He loved people, but didn’t like social norms and he would skip family vacation if he didn’t like the place.  Most of his mail he left unopened.

People competed for Amos’s attention.  As Lewis explains, many of Amos’s friends would ask themselves: “I know why I like him, but why does he like me?”

While at Hebrew University, Amos was studying both philosophy and psychology.  But he decided a couple of years later that he would focus on psychology.  He thought that philosophy had too many smart people studying too few problems, and some of the problems couldn’t be solved.

Many wondered how someone as bright, optimistic, logical, and clear-minded as Amos could end up in psychology.  In an interview when he was in his mid-forties, Amos commented:

“It’s hard to know how people select a course in life.  The big choices we make are practically random.  The small choices probably tell us more about who we are.  Which field we go into may depend upon which high school teacher we happen to meet.  Who we marry may depend on who happens to be around at the right time of life.  On the other hand, the small decisions are very systematic.  That I became a psychologist is probably not very revealing.  What kind of psychologist I am may depend upon deep traits.”

Amos became interested in decision making.  While pursuing a PhD at the University of Michigan, Amos ran experiments on people making decisions involving small gambles.  Economists had always assumed that people are rational.  There were axioms of rationality that people were thought to follow, such as transitivity:  if a person prefers A to B and B to C, then he must prefer A to C.  However, Amos found that many people preferred A to B when considering A and B, B to C when considering B and C, and C to A when considering A and C.  Many people violated transitivity.  Amos didn’t generalize his findings at that point, however.

(Transitivity illustration by Thuluviel, Wikimedia Commons)

Next Amos studied how people compare things.  He had read papers by the Berkeley psychologist Eleanor Rosch, who explored how people classified objects.

People said some strange things.  For instance, they said that magenta was similar to red, but that red wasn’t similar to magenta.  Amos spotted the contradiction and set out to generalize it.  He asked people if they thought North Korea was like Red China.  They said yes.  He asked them if Red China was like North Korea—and they said no.  People thought Tel Aviv was like New York but that New York was not like Tel Aviv.  People thought that the number 103 was sort of like the number 100, but that 100 wasn’t like 103.  People thought a toy train was a lot like a real train but that a real train was not like a toy train.

Amos came up with a theory, “features of similarity.”  When people compare two things, they make a list of noticeable features.  The more features two things have in common, the more similar they are.  However, not all objects have the same number of noticeable features.  New York has more than Tel Aviv.

This line of thinking led to some interesting insights:

When people picked coffee over tea, and tea over hot chocolate, and then turned around and picked hot chocolate over coffee—they weren’t comparing two drinks in some holistic manner.  Hot drinks didn’t exist as points on some mental map at fixed distances from some ideal.  They were collections of features.  Those features might become more or less noticeable; their prominence in the mind depended on the context in which they were perceived.  And the choice created its own context: Different features might assume greater prominence in the mind when the coffee was being compared to tea (caffeine) than when it was being compared to hot chocolate (sugar).  And what was true of drinks might also be true of people, and ideas, and emotions.

 

ERRORS

Amos returned to Israel after marrying Barbara Gans, who was a fellow graduate student in psychology at the University of Michigan.  Amos was now an assistant professor at Hebrew University.

Israel felt like a dangerous place because there was a sense that if the Arabs ever united instead of fighting each other, they could overrun Israel.  Israel was unusual in how it treated its professors: as relevant.  Amos gave talks about the latest theories in decision-making to Israeli generals.

Furthermore, everyone who was in Israel was in the army, including professors.  On May 22, 1967, the Egyptian president Gamal Abdel Nasser announced that he was closing the Straits of Tiran to Israeli ships.  Since most Israeli ships passed through the straits, Israel viewed the announcement as an act of war.  Amos was given an infantry unit to command.

By June 7, Israel was in a war on three fronts against Egypt, Jordan, and Syria.  In the span of a week, Israel had won the war and the country was now twice as big.  679 had died.  But because Israel was a small country, virtually everyone knew someone who had died.

Meanwhile, Danny was helping the Israeli Air Force to train fighter pilots.  He noticed that the instructors viewed criticism as more useful than praise.  After a good performance, the instructors would praise the pilot and then the pilot would usually perform worse on the next run.  After a poor performance, the instructors would criticize the pilot and the pilot would usually perform better on the next run.

Danny explained that pilot performance regressed to the mean.  An above average performance would usually be followed by worse performance—closer to the average.  A below average performance would usually be followed by better performance—again closer to the average.  Praise and criticism had little to do with it.

Illustration by intheskies

Danny was brilliant, though insecure and moody.  He became interested in several different areas in psychology.  Lewis adds:

That was another thing colleagues and students noticed about Danny: how quickly he moved on from his enthusiasms, how easily he accepted failure.  It was as if he expected it.  But he wasn’t afraid of it.  He’d try anything.  He thought of himself as someone who enjoyed, more than most, changing his mind.

Danny read about research by Eckhart Hess focused on measuring the dilation and contraction of the pupil in response to various stimuli.  People’s pupils expanded when they saw pictures of good-looking people of the opposite sex.  Their pupils contracted if shown a picture of a shark.  If given a sweet drink, their pupils expanded.  An unpleasant drink caused their pupils to contract.  If you gave people five slightly differently flavored drinks, their pupils would faithfully record the relative degree of pleasure.

People reacted incredibly quickly, before they were entirely conscious of which one they liked best.  “The essential sensitivity of the pupil response,” wrote Hess, “suggests that it can reveal preferences in some cases in which the actual taste differences are so slight that the subject cannot even articulate them.”

Danny tested how the pupil responded to a series of tasks requiring mental effort.  Does intense mental activity hinder perception?  Danny found that mental effort also caused the pupil to dilate.

 

THE COLLISION

Danny invited Amos to come to his seminar, Applications in Psychology, and talk about whatever he wanted.

Amos was now what people referred to, a bit confusingly, as a “mathematical psychologist.”  Nonmathematical psychologists, like Danny, quietly viewed much of mathematical psychology as a series of pointless exercises conducted by people who were using their ability to do math as camouflage for how little of psychological interest they had to say.  Mathematical psychologists, for their part, tended to view nonmathematical psychologists as simply too stupid to understand the importance of what they were saying.  Amos was then at work with a team of mathematically gifted American academics on what would become a three-volume, molasses-dense, axiom-filled textbook called Foundations of Measurement—more than a thousand pages of arguments and proofs of how to measure stuff.

Instead of talking about his own research, Amos talked about a specific study of decision making and how people respond to new information.  In the experiment, the psychologists presented people with two bags full of poker chips.  Each bag contained both red poker chips and white poker chips.  In one bag, 75 percent of the poker chips were white and 25 percent red.  In the other bag, 75 percent red and 25 percent white.  The subject would pick a bag randomly and, without looking in the bag, begin pulling poker chips out one at a time.  After each draw, the subject had to give her best guess about whether the chosen bag contained mostly red or mostly white chips.

There was a correct answer to the question, and it was provided by Bayes’s theorem:

Bayes’s rule allowed you to calculate the true odds, after each new chip was pulled from it, that the book bag in question was the one with majority white, or majority red, chips.  Before any chips had been withdrawn, those odds were 50:50—the bag in your hands was equally likely to be either majority red or majority white.  But how did the odds shift after each new chip was revealed?

That depended, in a big way, on the so-called base rate: the percentage of red versus white chips in the bag… If you know that one bag contains 99 percent red chips and the other, 99 percent white chips, the color of the first chip drawn from the bag tells you a lot more than if you know that each bag contains only 51 percent red or white… In the case of the two bags known to be 75 percent-25 percent majority red or white, the odds that you are holding the bag containing mostly red chips rise by three times every time you draw a red chip, and are divided by three every time you draw a white chip.  If the first chip you draw is red, there is a 3:1 (or 75 percent) chance that the bag you are holding is majority red.  If the second chip you draw is also red, the odds rise to 9:1, or 90 percent.  If the third chip you draw is white, they fall back to 3:1.  And so on.

Were human beings good intuitive statisticians?

(Image by Honina, Wikimedia Commons)

Lewis notes that these experiments were radical and exciting at the time.  Psychologists thought that they could gain insight into a number of real-world problems: investors reacting to an earnings report, political strategists responding to polls, doctors making a diagnosis, patients reacting to a diagnosis, coaches responding to a score, et cetera.  A common example is when a woman is diagnosed with breast cancer from a single test.  If the woman is in her twenties, it’s far more likely to be a misdiagnosis than if the woman is in her forties.  That’s because the base rates are different:  there’s a higher percentage of women in their forties than women in their twenties who have breast cancer.

Amos concluded that people do move in the right direction, however they usually don’t move nearly far enough.  Danny didn’t think people were good intuitive statisticians at all.  Although Danny was the best teacher of statistics at Hebrew University, he knew that he himself was not a good intuitive statistician because he frequently made simple mistakes like not accounting for the base rate.

Danny let Amos know that people are not good intuitive statisticians.  Uncharacteristically, Amos didn’t argue much, except he wasn’t inclined to jettison the assumption of rationality:

Until you could replace a theory with a better theory—a theory that better predicted what actually happened—you didn’t chuck a theory out.  Theories ordered knowledge, and allowed for better prediction.  The best working theory in social science just then was that people were rational—or, at the very least, decent intuitive statisticians.  They were good at interpreting new information, and at judging probabilities.  They of course made mistakes, but their mistakes were a product of emotions, and the emotions were random, and so could be safely ignored.

Note: To say that the mistakes are random means that mistakes in one direction will be cancelled out by mistakes in the other direction.  This implies that the aggregate market can still be rational and efficient.

Amos left Danny’s class feeling doubtful about the assumption of rationality.  By the fall of 1969, Amos and Danny were together nearly all the time.  Many others wondered at how two extremely different personalities could wind up so close.  Lewis:

Danny was a Holocaust kid; Amos was a swaggering Sabra—the slang term for a native Israeli.  Danny was always sure he was wrong.  Amos was always sure he was right.  Amos was the life of every party; Danny didn’t go to parties.  Amos was loose and informal; even when he made a stab at informality, Danny felt as if he had descended from some formal place.  With Amos you always just picked up where you left off, no matter how long it had been since you last saw him.  With Danny there was always a sense you were starting over, even if you had been with him just yesterday.  Amos was tone-deaf but would nevertheless sing Hebrew folk songs with great gusto.  Danny was the sort of person who might be in possession of a lovely singing voice that he would never discover.  Amos was a one-man wrecking ball for illogical arguments; when Danny heard an illogical argument, he asked, What might that be true of?  Danny was a pessimist.  Amos was not merely an optimist; Amos willed himself to be optimistic, because he had decided pessimism was stupid.

Lewis later writes:

But there was another story to be told, about how much Danny and Amos had in common.  Both were grandsons of Eastern European rabbis, for a start.  Both were explicitly interested in how people functioned when there were in a normal “unemotional” state.  Both wanted to do science.  Both wanted to search for simple, powerful truths.  As complicated as Danny might have been, he still longed to do “the psychology of single questions,” and as complicated as Amos’s work might have seemed, his instinct was to cut through endless bullshit to the simple nub of any matter.  Both men were blessed with shockingly fertile minds.

After testing scientists with statistical questions, Amos and Danny found that even most scientists are not good intuitive statisticians.  Amos and Danny wrote a paper about their findings, “A Belief in the Law of Small Numbers.”  Essentially, scientists—including statisticians—tended to assume that any given sample of a large population was more representative of that population than it actually was.

Amos and Danny had suspected that many scientists would make the mistake of relying too much on a small sample.  Why did they suspect this?  Because Danny himself had made the mistake many times.  Soon Amos and Danny realized that everyone was prone to the same mistakes that Danny would make.  In this way, Amos and Danny developed a series of hypotheses to test.

 

THE MIND’S RULES

The Oregon Research Institute is dedicated to studying human behavior.  It was started in 1960 by psychologist Paul Hoffman.  Lewis observes that many of the psychologists who joined the institute shared an interest in Paul Meehl’s book, Clinical vs. Statistical Prediction.  The book showed how algorithms usually perform better than psychologists when trying to diagnose patients or predict their behavior.

In 1986, thirty two years after publishing his book, Meehl argued that algorithms outperform human experts in a wide variety of areas.  That’s what the vast majority of studies had demonstrated by then.  Here’s a more recent meta-analysis: https://boolefund.com/simple-quant-models-beat-experts-in-a-wide-variety-of-areas/

In the 1960s, researchers at the institute wanted to build a model of how experts make decisions.  One study they did was to ask radiologists how they determined if a stomach ulcer was benign or malignant.  Lewis explains:

The Oregon researchers began by creating, as a starting point, a very simple algorithm, in which the likelihood that an ulcer was malignant depended on the seven factors the doctors had mentioned, equally weighted.  The researchers then asked the doctors to judge the probability of cancer in ninety-six different individual stomach ulcers, on a seven-point scale from “definitely malignant” to “definitely benign.”  Without telling the doctors what they were up to, they showed them each ulcer twice, mixing up the duplicates randomly in the pile so the doctors wouldn’t notice they were being asked to diagnose the exact same ulcer they had already diagnosed.

Initially the researchers planned to start with a simple model and then gradually build a more complex model.  But then they got the results of the first round of questions.  It turned out that the simple statistical model often seemed as good or better than experts at diagnosing cancer.  Moreover, the experts didn’t agree with each other and frequently even contradicted themselves when viewing the same image a second time.

Next, the Oregon experimenters explicitly tested a simple algorithm against human experts:  Was a simple algorithm better than human experts?  Yes.

If you wanted to know whether you had cancer or not, you were better off using the algorithm that the researchers had created than you were asking the radiologist to study the X-ray.  The simple algorithm had outperformed not merely the group of doctors; it had outperformed even the single best doctor.

(Algorithm illustration by Blankstock)

The strange thing was that the simple model was built on the factors that the doctors themselves had suggested as important.  While the algorithm was absolutely consistent, it appeared that human experts were rather inconsistent, most likely due to things like boredom, fatigue, illness, or other distractions.

Amos and Danny continued asking people questions where the odds were hard or impossible to know.  Lewis:

…Danny made the mistakes, noticed that he had made the mistakes, and theorized about why he had made the mistakes, and Amos became so engrossed by both Danny’s mistakes and his perceptions of those mistakes that he at least pretended to have been tempted to make the same ones.

Once again, Amos and Danny spent hour after hour after hour together talking, laughing, and developing hypotheses to test.  Occasionally Danny would say that he was out of ideas.  Amos would always laugh at this—he remarked later, “Danny has more ideas in one minute than a hundred people have in a hundred years.”  When they wrote, Amos and Danny would sit right next to each other at the typewriter.  Danny explained:

“We were sharing a mind.”

The second paper Amos and Danny did—as a follow-up on their first paper, “Belief in the Law of Small Numbers”—focused  on how people actually make decisions.  The mind typically doesn’t calculate probabilities.  What does it do?  It uses rules of thumb, or heuristics, said Amos and Danny.  In other words, people develop mental models, and then compare whatever they are judging to their mental models.  Amos and Danny wrote:

“Our thesis is that, in many situations, an event A is judged to be more probable than an event B whenever A appears more representative than B.”

What’s a bit tricky is that often the mind’s rules of thumb lead to correct decisions and judgments.  If that weren’t the case, the mind would not have evolved this ability.  For the same reason, however, when the mind makes mistakes because it relies on rules of thumb, those mistakes are not random, but systematic.

(Image by Argus)

When does the mind’s heuristics lead to serious mistakes?  When the mind is trying to judge something that has a random component.  That was one answer.  What’s interesting is that the mind can be taught the correct rule about how sample size impacts sampling variance; however, the mind rarely follows the correct statistical rule, even when it knows it.

For their third paper, Amos and Danny focused on the availability heuristic.  (The second paper had been about the representativeness heuristic.)  In one question, Amos and Danny asked their subjects to judge whether the letter “k” is more frequently the first letter of a word or the third letter of a word.  Most people thought “k” was more frequently the first letter because they could more easily recall examples where “k” was the first letter.

The more easily people can call some scenario to mind—the more available it is to them—the more probable they find it to be.  An fact or incident that was especially vivid, or recent, or common—or anything that happened to preoccupy a person—was likely to be recalled with special ease and so be disproportionately weighted in any judgment.  Danny and Amos had noticed how oddly, and often unreliably, their own minds recalculated the odds, in light of some recent or memorable experience.  For instance, after they drove past a gruesome car crash on the highway, they slowed down: Their sense of the odds of being in a crash had changed.  After seeing a movie that dramatizes nuclear war, they worried more about nuclear war; indeed, they felt that it was more likely to happen.

Amos and Danny ran similar experiments and found similar results.  The mind’s rules of thumb, although often useful, consistently made the same mistakes in certain situations.  It was similar to how the eye consistently falls for certain optical illusions.

Another rule of thumb Amos and Danny identified was the anchoring and adjustment heuristic.  One famous experiment they did was to ask people to spin a wheel of fortune, which would stop on a number between 0 and 100, and then guess the percentage of African nations in the United Nations.  The people who spun higher numbers tended to guess a higher percentage than those who spun lower numbers, even though the number spun was purely random and was irrelevant to the question.

 

THE RULES OF PREDICTION

For Amos and Danny, a prediction is a judgment under uncertainty.  They observed:

“In making predictions and judgments under uncertainty, people do not appear to follow the calculus of chance or the statistical theory of prediction.  Instead, they rely on a limited number of heuristics which sometimes yield reasonable judgments and sometimes lead to severe and systematic error.”

In 1972, Amos gave talks on the heuristics he and Danny had uncovered.  In the fifth and final talk, Amos spoke about historical judgment, saying:

“In the course of our personal and professional lives, we often run into situations that appear puzzling at first blush.  We cannot see for the life of us why Mr. X acted in a particular way, we cannot understand how the experimental results came out the way they did, etc.  Typically, however, within a very short time we come up with an explanation, a hypothesis, or an interpretation of the facts that renders them understandable, coherent, or natural.  The same phenomenon is observed in perception.  People are very good at detecting patterns and trends even in random data.  In contrast to our skill in inventing scenarios, explanations, and interpretations, our ability to assess their likelihood, or to evaluate them critically, is grossly inadequate.  Once we have adopted a particular hypothesis or interpretation, we grossly exaggerate the likelihood of that hypothesis, and find it very difficult to see things in any other way.”

In one experiment, Amos and Danny asked students to predict various future events that would result from Nixon’s upcoming visit to China and Russia.  What was intriguing was what happened later: If a predicted event had occurred, people overestimated the likelihood they had previously assigned to that event.  Similarly, if a predicted event had not occurred, people tended to claim that they always thought it was unlikely.  This came to be called hindsight bias.

  • A possible event that had occurred was seen in hindsight to be more predictable than it actually was.
  • A possible event that had not occurred was seen in hindsight to be less likely that it actually was.

As Amos said:

All too often, we find ourselves unable to predict what will happen; yet after the fact we explain what did happen with a great deal of confidence.  This “ability” to explain that which we cannot predict, even in the absence of any additional information, represents an important, though subtle, flaw in our reasoning.  It leads us to believe that there is a less uncertain world than there actually is…

Experts from many walks of life—from political pundits to historians—tend to impose an imagined order on random events from the past.  They change their stories to “explain”—and by implication, “predict” (in hindsight)—whatever random set of events occurred.  This is hindsight bias, or “creeping determinism.”

Hindsight bias can create serious problems: If you believe that random events in the past are more predictable than they actually were, you will tend to see the future as more predictable than it actually is.  You will be surprised much more often than you should be.

Image by Zerophoto

 

GOING VIRAL

Part of Don Redelmeier’s job at Sunnybrook Hospital (located in a Toronto suburb) was to check the thinking of specialists for mental mistakes.  In North America, more people died every year as a result of preventable accidents in hospitals than died in car accidents.  Redelmeier focused especially on clinical misjudgment.  Lewis:

Doctors tended to pay attention mainly to what they were asked to pay attention to, and to miss some bigger picture.  They sometimes failed to notice what they were not directly assigned to notice.

[…]

Doctors tended to see only what they were trained to see… A patient received treatment for something that was obviously wrong with him, from a specialist oblivious to the possibility that some less obvious thing might also be wrong with him.  The less obvious thing, on occasion, could kill a person.

When he was only seventeen years old, Redelmeier had read an article by Kahneman and Tversky, “Judgment Under Uncertainty: Heuristics and Biases.”  Lewis writes:

What struck Redelmeier wasn’t the idea that people make mistakes.  Of course people made mistakes!  What was so compelling is that the mistakes were predictable and systematic.  They seemed ingrained in human nature.

One major problem in medicine is that the culture does not like uncertainty.

To acknowledge uncertainty was to admit the possibility of error.  The entire profession had arranged itself as if to confirm the wisdom of its decisions.  Whenever a patient recovered, for instance, the doctor typically attributed the recovery to the treatment he had prescribed, without any solid evidence the treatment was responsible… [As Redelmeier said:]  “So many diseases are self-limiting.  They will cure themselves.  People who are in distress seek care.  When they seek care, physicians feel the need to do something.  You put leeches on; the condition improves.  And that can propel a lifetime of leeches.  A lifetime of overprescribing antibiotics.  A lifetime of giving tonsillectomies to people with ear infections.  You try it and they get better the next day and it is so compelling…”

Photo by airdone

One day, Redelmeier was going to have lunch with Amos Tversky.  Hal Sox, Redelmeier’s superior, told him just to sit quietly and listen, because Tversky was like Einstein, “one for the ages.”  Sox had coauthored a paper Amos had done about medicine.  They explored how doctors and patients thought about gains and losses based upon how the choices were framed.

An example was lung cancer.  You could treat it with surgery or radiation.  Surgery was more likely to extend your life, but there was a 10 percent chance of dying.  If you told people that surgery had a 90 percent chance of success, 82 percent of patients elected to have surgery.  But if you told people that surgery had a 10 percent chance of killing them, only 54 percent chose surgery.  In a life-and-death decision, people made different choices based not on the odds, but on how the odds were framed.

Amos and Redelmeier ended up doing a paper:

[Their paper] showed that, in treating individual patients, the doctors behaved differently than they did when they designed ideal treatments for groups of patients with the same symptoms.  They were likely to order additional tests to avoid raising troubling issues, and less likely to ask if patients wished to donate their organs if they died.  In treating individual patients, doctors often did things they would disapprove of if they were creating a public policy to treat groups of patients with the exact same illness…

The point was not that the doctor was incorrectly or inadequately treating individual patients.  The point was that he could not treat his patient one way, and groups of patients suffering from precisely the same problem in another way, and be doing his best in both cases.  Both could not be right.

Redelmeier pointed out that the facade of rationality and science and logic is “a partial lie.”

In late 1988 or early 1989, Amos introduced Redelmeier to Danny.  One of the recent things Danny had been studying was people’s experience of happiness versus their memories of happiness.  Danny also looked at how people experienced pain versus how they remembered it.

One experiment involved sticking the subject’s arms into a bucket of ice water.

[People’s] memory of pain was different from their experience of it.  They remembered moments of maximum pain, and they remembered, especially, how they felt the moment the pain ended.  But they didn’t particularly remember the length of the painful experience.  If you stuck people’s arms in ice buckets for three minutes but warmed the water just a bit for another minute or so before allowing them to flee the lab, they remembered the experience more fondly than if you stuck their arms in the bucket for three minutes and removed them at a moment of maximum misery.  If you asked them to choose one experience to repeat, they’d take the first session.  That is, people preferred to endure more total pain so long as the experience ended on a more pleasant note.

Redelmeier tested this hypothesis on seven hundred people who underwent a colonoscopy.  The results supported Danny’s finding.

 

BIRTH OF THE WARRIOR PSYCHOLOGIST

In 1973, the armies of Egypt and Syria surprised Israel on Yom Kippur.  Amos and Danny left California for Israeli.  Egyptian President Anwar Sadat had promised to shoot down any commercial airliners entering Israel.  That was because, as usual, Israelis in other parts of the world would return to Israel during a war.  Amos and Danny managed to land in Tel Aviv on an El Al flight.  The plane had descended in total darkness.  Amos and Danny were to join the psychology field unit.

Amos and Danny set out in a jeep and went to the battlefield in order to study how to improve the morale of the troops.  Their fellow psychologists thought they were crazy.  It wasn’t just enemy tanks and planes.  Land mines were everywhere.  And it was easy to get lost.  People were more concerned about Danny than Amos because Amos was more of a fighter.  But Danny proved to be more useful because he had a gift for finding solutions to problems where others hadn’t even noticed the problem.

Soon after the war, Amos and Danny studied public decision making.

Both Amos and Danny thought that voters and shareholders and all the other people who lived with the consequences of high-level decisions might come to develop a better understanding of the nature of decision making.  They would learn to evaluate a decision not by its outcomes—whether it turned out to be right or wrong—but by the process that led to it.  The job of the decision maker wasn’t to be right but to figure out the odds in any decision and play them well.

It turned out that Israeli leaders often agreed about probabilities, but didn’t pay much attention to them when making decisions on whether to negotiate for peace or fight instead.  The director-general of the Israeli Foreign Ministry wasn’t even interested in the best estimates of probabilities.  Instead, he made it clear that he preferred to trust his gut.  Lewis quotes Danny:

“That was the moment I gave up on decision analysis.  No one ever made a decision because of a number.  They need a story.”

Some time later, Amos introduced Danny to the field of decision making under uncertainty.  Many students of the field studied subjects in labs making hypothetical gambles.

The central theory in decision making under uncertainty had been published in the 1730s by the Swiss mathematician Daniel Bernoulli.  Bernoulli argued that people make probabilistic decisions so as to maximize their expected utility.  Bernoulli also argued that people are “risk averse”: each new dollar has less utility than the one before.  This theory seemed to describe some human behavior.

(Utility as a function of outcomes, Global Water Forum, Wikimedia Commons)

The utility function above illustrates risk aversion: Each additional dollar—between $10 and $50—has less utility than the one before.

In 1944, John von Neumann and Oskar Morgenstern published the axioms of rational decision making.  One axiom was “transitivity”: if you preferred A to B, and B to C, then you preferred A to C.  Another axiom was “independence”:  if you preferred A to B, your preference between A and B wouldn’t change if some other alternative (say D) was introduced.

Many people, including nearly all economists, accepted von Neumann and Morgenstern’s axioms of rationality as a fair description for how people actually made choices.  Danny recalls that Amos regarded the axioms as a “sacred thing.”

By the summer of 1973, Amos was searching for ways to undo the reigning theory of decision making, just as he and Danny had undone the idea that human judgment followed the precepts of statistical theory.

Lewis records that by the end of 1973, Amos and Danny were spending six hours a day together.  One insight Danny had about utility was that it wasn’t levels of wealth that represented utility (or happiness); it was changes in wealth—gains and losses—that mattered.

 

THE ISOLATION EFFECT

Many of the ideas Amos and Danny had could not be attributed to either one of them individually, but seemed to come from their interaction.  That’s why they always shared credit equally—they switched the order of their names for each new paper, and the order for their very first paper had been determined by a coin toss.

In this case, though, it was clear that Danny had the insight that gains and losses are more important than levels of utility.  However, Amos then asked a question with profound implications: “What if we flipped the signs?”  Instead of asking whether someone preferred a 50-50 gamble for $1,000 or $500 for sure, they asked this instead:

Which of the following do you prefer?

  • Gift A: A lottery ticket that offers a 50 percent chance of losing $1,000
  • Gift B: A certain loss of $500

When the question was put in terms of possible gains, people preferred the sure thing.  But when the question was put in terms of possible losses, people preferred to gamble.  Lewis elaborates:

The desire to avoid loss ran deep, and expressed itself most clearly when the gamble came with the possibility of both loss and gain.  That is, when it was like most gambles in life.  To get most people to flip a coin for a hundred bucks, you had to offer them far better than even odds.  If they were going to loss $100 if the coin landed on heads, they would need to win $200 if it landed on tails.  To get them to flip a coin for ten thousand bucks, you had to offer them even better odds than you offered them for flipping it for a hundred.

It was easy to see that loss aversion had evolutionary advantages.  People who weren’t sensitive to pain or loss probably wouldn’t survive very long.

A loss is when you end up worse than your status quo.  Yet determining the status quo can be tricky because often it’s a state of mind.  Amos and Danny gave this example:

Problem A.  In addition to whatever you own, you have been given $1,000.  You are now required to choose between the following options:

  • Option 1.  A 50 percent chance to win $1,000
  • Option 2.  A gift of $500

Problem B.  In addition to whatever you own, you have been given $2,000.  You are now required to choose between the following options:

  • Option 3.  A 50 percent chance to lose $1,000
  • Option 4.  A sure loss of $500

In Problem A, most people picked Option 2, the sure thing.  In Problem B, most people chose Option 3, the gamble.  However, the two problems are logically identical:  Overall, you’re choosing between $1,500 for sure versus a 50-50 chance of either $2,000 or $1,000.

What Amos and Danny had discovered was framing.  The way a choice is framed can impact the way people choose, even if two different frames both refer to exactly the same choice, logically speaking.  Consider the Asian Disease Problem, invented by Amos and Danny.  People were randomly divided into two groups.  The first group was given this question:

Problem 1.  Imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people.  Two alternative problems to combat the disease have been proposed.  Assume that the exact scientific estimate of the consequence of the programs is as follows:

  • If Program A is adopted, 200 people will be saved.
  • If Program B is adopted, there is a 1/3 probability that 600 people will be saved, and a 2/3 probability that no one will be saved.

Which of the two programs would you favor?

People overwhelming chose Program A, saving 200 people for sure.

The second group was given the same problem, but was offered these two choices:

  • If Program C is adopted, 400 people will die.
  • If Program D is adopted, there is a 1/3 probability that nobody will die and a 2/3 probability that 600 people will die.

People overwhelmingly chose Program D.  Once again, the underlying choice in each problem is logically identical.  If you save 200 for sure, then 400 will die for sure.  Because of framing, however, people make inconsistent choices.

 

THIS CLOUD OF POSSIBILITY

In 1984, Amos learned he had been given a MacArthur “genius” grant.  He was upset, as Lewis explains:

Amos disliked prizes.  He thought that they exaggerated the differences between people, did more harm than good, and created more misery than joy, as for every winner there were many others who deserved to win, or felt they did.

Amos was angry because he thought that being given the award, and Danny not being given the award, was “a death blow” for the collaboration between him and Danny.  Nonetheless, Amos kept on receiving prizes and honors, and Danny kept on not receiving them.  Furthermore, ever more books and articles came forth praising Amos for the work he had done with Danny, as if he had done it alone.

Amos continued to be invited to lectures, seminars, and conferences.  Also, many groups asked him for his advice:

United States congressmen called him for advice on bills their were drafting.  The National Basketball Association called to hear his argument about statistical fallacies in basketball.  The United States Secret Service flew him to Washington so that he could advise them on how to predict and deter threats to the political leaders under their protection.  The North Atlantic Treaty Organization flew him to the French Alps to teach them about how people made decisions in conditions of uncertainty.  Amos seemed able to walk into any problem, and make the people dealing with it feel as if he grasped its essence better than they did.

Despite the work of Amos and Danny, many economists and decision theorists continued to believe in rationality.  These scientists argued that Amos and Danny had overstated human fallibility.  So Amos looked for new ways to convince others.  For instance, Amos asked people: Which is more likely to happen in the next year, that a thousand Americans will die in a flood, or that an earthquake in California will trigger a massive flood that will drown a thousand Americans?  Most people thought the second scenario was more likely; however, the second scenario is a special case of the first scenario, and therefore the first scenario is automatically more likely.

Amos and Danny came up with an even more stark example.  They presented people with the following:

Linda is 31 years old, single, outspoken, and very bright.  She majored in philosophy.  As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

Which of the two alternatives is more probable?

  • Linda is a bank teller.
  • Linda is a bank teller and is active in the feminist movement.

Eighty-five percent of the subjects thought that the second scenario is more likely than the first scenario.  However, just like the previous problem, the second scenario is a special case of the first scenario, and so the first scenario is automatically more likely than the second scenario.

Say there are 50 people who fit the description, are named Linda, and are bank tellers.  Of those 50, how many are also active in the feminist movement?  Perhaps quite a few, but certainly not all 50.

Amos and Danny constructed a similar problem for doctors.  But the majority of doctors made the same error.

Lewis:

The paper Amos and Danny set out to write about what they were now calling “the conjunction fallacy” must have felt to Amos like an argument ender—that is, if the argument was about whether the human mind reasoned probabilistically, instead of the ways Danny and Amos had suggested.  They walked the reader through how and why people violated “perhaps the simplest and the most basic qualitative law of probability.”  They explained that people chose the more detailed description, even though it was less probable, because it was more “representative.”  They pointed out some places in the real world where this kink in the mind might have serious consequences.  Any prediction, for instance, could be made to seem more believable, even as it became less likely, if it was filled with internally consistent details.  And any lawyer could at once make a case seem more persuasive, even as he made the truth of it less likely, by adding “representative” details to his description of people and events.

Around the time Amos and Danny published work with these examples, their collaboration had come to be nothing like it was before.  Lewis writes:

It had taken Danny the longest time to understand his own value.  Now he could see that the work Amos had done alone was not as good as the work they had done together.  The joint work always attracted more interest and higher praise than anything Amos had done alone.

Danny pointed out to Amos that Amos that been a member of the National Academy of Sciences for a decade, but Danny still wasn’t a member.  Danny asked Amos why he hadn’t put Danny’s name forward.

A bit later, Danny told Amos they were no longer friends.  Three days after that, Amos called Danny.  Amos learned that his body was riddled with cancer and that he had at most six months to live.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

Think Twice

July 31, 2022

In today’s blog post, I review some lessons from Michael Mauboussin’s excellent book Think Twice: Harnessing the Power of Counterintuition.   Each chapter is based on a common mistake in decision-making:

  • RQ vs. IQ
  • The Outside View
  • Open to Options
  • The Expert Squeeze
  • Situational Awareness
  • More Is Different
  • Evidence of Circumstance
  • Phase Transitions—”Grand Ah-Whooms”
  • Sorting Luck From Skill
  • Time to Think Twice
Illustration by Kheng Guan Toh

 

RQ vs IQ

Given a proper investment framework or system, obviously IQ can help a great deal over time.  Warren Buffett and Charlie Munger are seriously smart.  But they wouldn’t have become great investors without a lifelong process of learning and improvement, including learning how to be rational.  The ability to be rational may be partly innate, but it can be improved—sometimes significantly—with work.

Illustration by hafakot

An investor dedicated to lifelong improvements in knowledge and rationality can do well in value investing even without being brilliant.  A part of rationality is focusing on the knowable and remembering the obvious.

“We try more to profit from always remembering the obvious than from grasping the esoteric. It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent.” — Charlie Munger

Quite often, the best approach for a value investor is to invest in an index fund or in a quantitative value fund.  Lifelong improvements are still helpful in these cases.  Many value investors, including the father of value investing Ben Graham, have advocated and used a quantitative approach.

 

THE OUTSIDE VIEW

Mauboussin discusses why Big Brown was a bad bet to win the Triple Crown in 2008.  Big Brown had won the Kentucky Derby by four-and-three-quarters lengths, and he won the Preakness by five-and-one-quarter lengths.  The horse’s trainer, Rick Dutrow, said, “He looks as good as he can possibly look.  I can’t find any flaws whatsoever in Big Brown.  I see the prettiest picture.  I’m so confident, it’s unbelievable.”  UPS (after whom Big Brown was named) signed a marketing deal.  And enthusiasm for Big Brown’s chances in the Belmont Stakes grew.

(Photo of Big Brown by Naoki Nakashima, via Wikimedia Commons)

What happened?  Big Brown trailed the field during the race, so his jockey eased him out of the race.  This was a shocking result.  But the result of not winning could have been much more widely anticipated if people had used the outside view.

The outside view means identifying similar situations and finding the statistics on how things worked out.  Renowned handicapper Steven Crist developed an outside view, as Mauboussin summarizes:

Of the twenty-nine horses with a chance to capture the Triple Crown after winning the Kentucky Derby and the Preakness Stakes, only eleven triumphed, a success rate less than 40 percent.  But a closer examination of those statistics yielded a stark difference before and after 1950.  Before 1950, eight of the nine horses attempting to win the Triple Crown succeeded.  After 1950, only three of twenty horses won.  It’s hard to know why the achievement rate dropped from nearly 90 percent to just 15 percent, but logical factors include better breeding (leading to more quality foals) and bigger starting fields.

Most people naturally use the inside view.  This essentially means looking at more subjective factors that are close at hand, like how tall and strong the horse looks and the fact that Big Brown had handily won the Kentucky Derby and the Preakness.

Why do people naturally adopt the inside view?  Mauboussin gives three reasons:

  • the illusion of superiority
  • the illusion of optimism
  • the illusion of control

First is the illusion of superiority.  Most people say they are above average in many areas, such as looks, driving, judging humor, investing.  Most people have an unrealistically positive view of themselves.  In many areas of life, this does not cause problems.  In fact, unrealistic positivity may often be an advantage that helps people to persevere.  But in zero-sum games—like investing—where winning requires clearly being above average, the illusion of superiority is harmful.

Illustration by OptureDesign

Munger calls it the Excessive Self-Regard Tendency.  Munger also notes that humans tend to way overvalue the things they possess—the endowment effect.  This often causes someone already overconfident about a bet he is considering to become even more overconfident after making the bet.

The illusion of optimism, which is similar to the illusion of superiority, causes most people to see their future as brighter than that of others.

The illusion of control causes people to behave as if chance events are somehow subject to their control.  People throwing dice throw softly when they want low numbers and hard for high numbers.  A similar phenomenon is seen when people choose which lottery card to take, as opposed to getting one by chance.

Mauboussin observes that a vast range of professionals tends to use the inside view to make important decisions, with predictably poor results.

Encouraged by the three illusions, most believe they are making the right decision and have faith that the outcomes will be satisfactory.

In the world of investing, many investors believe that they will outperform the market over time.  However, after several decades, there are very few investors who have done better than the market.

Another area where people fall prey to the three illusions is mergers and acquisitions.  Two-thirds of acquisitions fail to create value, but most executives, relying on the inside view, believe that they can beat the odds.

The planning fallacy is yet another example of how most people rely on the inside view instead of the outside view.  Mauboussin gives one common example of students estimating when they’d finish an assignment:

…when the deadline arrived for which the students had given themselves a 50 percent chance of finishing, only 13 percent actually turned in their work.  At the point when the students thought there was 75 percent chance they’d be done, just 19 percent had completed the project.  All the students were virtually sure they’d be done by the final date.  But only 45 percent turned out to be right.

Illustration by OpturaDesign

Daniel Kahneman gives his own example of the planning fallacy.  He was part of a group assembled to write a curriculum to teach judgment and decision-making to high school students.  Kahneman asked everyone in the group to write down their opinion of when they thought the group would complete the task.  Kahneman found that the average was around two years, and everyone, including the dean, estimated between eighteen and thirty months.

Kahneman then realized that the dean had participated in similar projects in the past.  Kahneman asked the dean how long it took them to finish.

The dean blushed and then answered that 40 percent of the groups that had started similar programs had never finished, and that none of the groups completed it in less than seven years.  Kahneman then asked how good this group was compared to past groups.  The dean thought and then replied: ‘Below average, but not by much.’

 

OPEN TO OPTIONS

In making decisions, people often fail to consider a wide enough range of alternatives.  People tend to have “tunnel vision.”

Anchoring is an important example of this mistake.  Mauboussin:

Kahneman and Amos Tversky asked people what percentage of the UN countries is made up of African nations.  A wheel of fortune with the numbers 1 to 100 was spun in front of the participants before they answered.  The wheel was rigged so it gave either 10 or 65 as the result of a spin.  The subjects were then asked—before giving their specific prediction—if the answer was higher or lower than the number on the wheel.  The median response from the group that saw the wheel stop at 10 was 25%, and the median response from the group that saw 65 was 45%.

(Illustration by Olga Vainshtein)

Behavioral finance expert James Montier has run his own experiment on anchoring.  People are asked to write down the last four digits of their phone number.  Then they are asked whether the number of doctors in their capital city is higher or lower than the last four digits of their phone number.  Results: Those whose last four digits were greater than 7000 on average report 6762 doctors, while those with telephone numbers below 2000 arrived at an average 2270 doctors.

Stock prices often have a large component of randomness, but investors tend to anchor on various past stock prices.  The rational way to avoid such anchoring is to carefully develop different possible scenarios for the intrinsic value of a stock.  For instance, you could ask:

  • What is the business worth if things go better than expected?
  • What is the business worth if things go as expected?  Or: What is the business worth under normal conditions?
  • What is the business worth if things go worse than expected?

Ideally, you would not want to know about past stock prices—or even the current stock price—before developing the intrinsic value scenarios.

The Representativeness Heuristic

The representativeness heuristic is another bias that leads many people not to consider a wide range of possibilities.  Daniel Kahneman and Amos Tversky defined representativeness as “the degree to which [an event] (i) is similar in essential characteristics to its parent population, and (ii) reflects the salient features of the process by which it is generated.”

People naturally tend to believe that something that is more representative is more likely.  But frequently that’s not the case.  Here is an example Kahneman and Tversky have used:

“Steve is very shy and withdrawn, invariably helpful but with very little interest in people or in the world of reality.  A meek and tidy soul, he has a need for order and structure, and a passion for detail.  Question: Is Steve more likely to be a librarian or a farmer?”

Most people say “a librarian.”  But the fact that the description seems more representative of librarians than of farmers does not mean that it is more likely that Steve is a librarian.  Instead, one must look at the base rate: there are twenty times as many farmers as librarians, so it is far more likely that Steve is a farmer.

Another example Kahneman gives:

“Linda is 31 years old, single, outspoken, and very bright.  She majored in philosophy.  As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.  Question: Which is more probable?

  1.  Linda is a bank teller.
  2.  Linda is a bank teller and is active in the feminist movement.”

Most people say the second option is more likely.  But just using simple logic, we know that the second option is a subset of the first option, so the first option is more likely.  Most people get this wrong because they use the representativeness heuristic.

Availability Bias, Vividness Bias, Recency Bias

If a fact is easily available—which often happens if a fact is vivid or recent—people generally far overestimate its probability.

A good example is a recent and vivid plane crash.  The odds of dying in a plane crash are one in 11 million—astronomically low.  The odds of dying in a car crash are one in five thousand.  But many people, after seeing recent and vivid photos of a plane crash, decide that taking a car is much safer than taking a plane.

Extrapolating the Recent Past

Most people automatically extrapolate the recent past into the future without considering various alternative scenarios.  To understand why, consider Kahneman’s definitions of two systems in the mind, System 1 and System 2:

System 1:   Operates automatically and quickly;  makes instinctual decisions based on heuristics.

System 2:   Allocates attention (which has a limited budget) to the effortful mental activities that demand it, including logic, statistics, and complex computations.

In Thinking, Fast and Slow, Kahneman writes that System 1 and System 2 work quite well on the whole:

The division of labor between System 1 and System 2 is highly efficient:  it minimizes effort and optimizes performance.  The arrangement works well most of the time because System 1 is generally very good at what it does: its models of familiar situations are accurate, its short-term predictions are usually accurate as well, and its initial reactions to challenges are swift and generally appropriate.  System 1 has biases, however, systematic errors that it is prone to make in specified circumstances.  As we shall see, it sometimes answers easier questions than the one it was asked, and it has little understanding of logic and statistics.

System 1 is automatic and quick, and it works remarkably well much of the time.  Throughout most of our evolutionary history, System 1 has been instrumental in keeping us alive.  However, when we were hunter-gatherers, the recent past was usually the best guide to the future.

  • If there was a rustling in the grass or any other sign of a predator, the brain automatically went on high alert, which was useful because otherwise you weren’t likely to survive long.  A statistical calculation wasn’t needed.
  • There were certain signs indicating the potential presence of animals to hunt or wild plants to collect.  You learned to recognize those signs.  You foraged or you died.  You didn’t need to know any statistics.
  • Absent any potential threats, and assuming enough to eat, then things were fine and you could relax for a spell.

In today’s world—unlike when we were hunter-gatherers—the recent past is often a terrible guide to the future.  For instance, when it comes to investing, extrapolating the recent past is one of the biggest mistakes that investors make.  In a highly random environment, you should expect reversion to the mean, rather than a continuation of the recent past.  Investors must learn to think counterintuitively.  That includes thinking probabilistically—in terms of possible scenarios and reversion to the mean.

Illustration by intheskies

Doubt Avoidance

Charlie Munger—see Poor Charlie’s Almanack, Expanded Third Edition—explains what he calls Doubt Avoidance Tendency as follows:

“The brain of man is programmed with a tendency to quickly remove doubt by reaching some decision.”

System 1 is designed (by evolution) to jump to conclusions.  In the past, when things were simpler and less probabilistic, the ability to make a quick decision was beneficial.  In today’s complex world, you must train yourself to slow down when facing an important decision under uncertainty—a decision that depends on possible scenarios and their associated probabilities.

The trouble is that our mind—due to System 1—wants to jump immediately to a conclusion, even more so if we feel pressured, puzzled, or stressed.  Munger explains:

What triggers Doubt-Avoidance Tendency?  Well, an unthreatened man, thinking of nothing in particular, is not being prompted to remove doubt through rushing to some decision.  As we shall see later when we get to Social-Proof Tendency and Stress-Influence Tendency, what usually triggers Doubt-Avoidance Tendency is some combination of (1) puzzlement and (2) stress…

The fact that social pressure and stress trigger the Doubt-Avoidance Tendency supports the notion that System 1 excelled at keeping us alive when we lived in a much more primitive world.  In that type of environment where things usually were what they seemed to be, the speed of System 1 in making decisions was vital.  If the group was running in one direction, the immediate, automatic decision to follow was what kept you alive over time.

Inconsistency Avoidance and Confirmation Bias

Munger on the Inconsistency-Avoidance Tendency:

The brain of man conserves programming space by being reluctant to change, which is a form of inconsistency avoidance.  We see this in all human habits, constructive and destructive.  Few people can list a lot of bad habits that they have eliminated, and some people cannot identify even one of these.  Instead, practically everyone has a great many bad habits he has long maintained despite their being known as bad…. chains of habit that were too light to be felt before they became too heavy to be broken.

The rare life that is wisely lived has in it many good habits maintained and many bad habits avoided or cured.

Photo by Marek

Munger continues:

It is easy to see that a quickly reached conclusion, triggered by Doubt-Avoidance Tendency, when combined with a tendency to resist any change in that conclusion, will naturally cause a lot of errors in cognition for modern man.  And so it observably works out…

And so, people tend to accumulate large mental holdings of fixed conclusions and attitudes that are not often reexamined or changed, even though there is plenty of good evidence that they are wrong.

Our brain will jump quickly to a conclusion and then resist any change in that conclusion.  How do we combat this tendency?  One great way to overcome first conclusion bias is to train our brains to emulate Charles Darwin:

One of the most successful users of an antidote to first conclusion bias was Charles Darwin.  He trained himself, early, to intensively consider any evidence tending to disconfirm any hypothesis of his, more so if he thought his hypothesis was a particularly good one.  The opposite of what Darwin did is now called confirmation bias, a term of opprobrium.  Darwin’s practice came from his acute recognition of man’s natural cognitive faults arising from Inconsistency-Avoidance Tendency.  He provides a great example of psychological insight correctly used to advance some of the finest mental work ever done.  (my emphasis)

Selective Attention and Inattentional Blindness

We tend to be very selective about what we hear and see, and this is partly a function of what we already believe.  We often see and hear only what we want, and tune out everything else.

On a purely visual level, there is something called inattentional blindness.  When we focus on certain aspects of our environment, this causes many of us to miss other aspects that are plainly visible.  There is a well-known experiment related to inattentional blindness.  People watch a thirty-second video that shows two teams, one wearing white and the wearing black.  Each team is passing a basketball back and forth.  In the middle of the video, a woman wearing a gorilla suit walks into the middle of the scene, thumps her chest, and walks off.  Roughly half of the people watching the video have no recollection of the gorilla.

Struggles and Stresses

Stress or fatigue causes many of us to make poorer decisions than we otherwise would.  Thus, we must take care.  With the right attitude, however, stress can slowly be turned into an advantage over a long period of time.

As Ray Dalio and Charlie Munger have pointed out, mental strength is one of life’s greatest gifts.  With a high degree of focus and discipline, a human being can become surprisingly strong and resilient.  But this typically only happens gradually, over the course of years or decades, as the result of an endless series of struggles, stresses, and problems.

A part of strength that can be learned over time is inner peace or total calm in the face of seemingly overwhelming difficulties.  The practice of transcendental meditation is an excellent way to achieve inner peace and total calm in the face of any adversity.  But there are other ways, too.

Wise men such as Munger or Lincoln are of the view that total calm in the face of any challenge is simply an aspect of mental strength that can be developed over time.  Consider Rudyard Kipling’s poem “If”:

If you can keep your head when all about you
    Are losing theirs and blaming it on you,
If you can trust yourself when all men doubt you,
    But make allowance for their doubting too;
If you can wait and not be tired by waiting,
    Or being lied about, don’t deal in lies,
Or being hated, don’t give way to hating,
    And yet don’t look too good, nor talk too wise…
(Image by nickolae)
In the 2016 Daily Journal Annual Meeting, Charlie Munger made the following remarks:

…So, maybe in that sense I think a tougher hand has been good for us.  My answer to that question reminds me of my old Harvard law professor who used to say, ‘Charlie, let me know what your problem is and I’ll try to make it harder for you.’  I’m afraid that’s what I’ve done to you.

As for how do I understand a new industry: the answer is barely.  I just barely have enough cognitive ability to do what I do.  And that’s because the world promoted me to the place where I’m stressed.  And you’re lucky if it happens to you, because that’s what you want to end up: stressed.  You want to have your full powers called for.  Believe you me, I’ve had that happen all my life.  I’ve just barely been able to think through to the right answer, time after time.  And sometimes I’ve failed…

Link to 2016 Daily Journal Meeting Notes (recorded courtesy of Whitney Tilson): https://www.scribd.com/doc/308879985/MungerDJ-2-16

Incentives

Mauboussin writes about the credit crisis of 2007-2008.  People without credit could buy nice homes.  Lenders earned fees and usually did not hold on to the mortgages.  Investment banks bought mortgages and bundled them for resale, earning a fee.  Rating agencies were paid to rate the mortgage-backed securities, and they rated many of them AAA (based partly on the fact that home prices had never declined nationwide).  Investors worldwide in AAA-rated mortgage-backed securities earned higher returns than they did on other AAA issues.  Some of these investors were paid based on portfolio performance and thus earned higher fees this way.

Incentives are extremely important:

Never, ever think about something else when you should be thinking about incentives.” – Charlie Munger

Under a certain set of incentives, many people who normally are good people will behave badly.  Often this bad behavior is not only due to the incentives at play, but also involves other psychological pressures like social proof, stress, and doubt-avoidance.  A bad actor could manipulate basically good people to do bad things using social proof and propaganda.  If that fails, he could use bribery or blackmail.

Finally, Mauboussin offers advice about how to deal with “tunnel vision,” or the insufficient consideration of alternatives:

  • Explicitly consider alternatives.
  • Seek dissent. (This is very difficult, but highly effective.  Think of Lincoln’s team of rivals.)
  • Keep track of previous decisions. (A decision journal does not cost much, but it can help one over time to make better decisions.)
  • Avoid making decisions while at emotional extremes. (One benefit to meditation—in addition to total calm and rationality—is that it can give you much greater self-awareness.  You can learn to accurately assess your emotional state, and you can learn to postpone important decisions if you’re too emotional or tired.)
  • Understand incentives.

 

THE EXPERT SQUEEZE

In business today, there are many areas where you can get better insights or predictions than what traditional experts can offer.

Mauboussin gives the example of Best Buy forecasting holiday sales.  In the past, Best Buy depended on specialists to make these forecasts.  James Surowiecki, author of The Wisdom of Crowds, went to Best Buy’s headquarters and told them that a crowd could predict better than their specialists could.

Jeff Severts, a Best Buy executive, decided to test Surowiecki’s suggestion.  Late in 2005, Severts set up a location for employees to submit and update their estimates of sales from Thanksgiving to year-end.  In early 2006, Severts revealed that the internal experts had been 93 percent accurate, while the “amateur crowd” was off only one-tenth of one percent.  Best Buy then allocated more resources to its prediction market, and benefited.

Another example of traditional experts being supplanted:  Orley Ashenfelter, wine lover and economist, figured out a simple regression equation that predicts the quality of red wines from France’s Bordeaux region better than most wine experts.  Mauboussin:

With the equation in hand, the computer can deliver appraisals that are quicker, cheaper, more reliable, and without a whiff of snobbishness.

Mauboussin mentions four categories over which we can judge experts versus computers:

Rule based; limited range of outcomes—experts are generally worse than computers. Examples include credit scoring and simple medical diagnosis.

Rule based; wide range of outcomes—experts are generally better than computers.  But this may be changing.  For example, humans used to be better at chess and Go, but now computers are far better than humans.

Probabilistic; limited range of outcomes—experts are equal or worse than collectives.  Examples include admissions officers and poker.

Probabilistic; wide range of outcomes—experts are worse than collectives.  Examples include forecasting any of the following: stock prices, the stock market, interest rates, or the economy.

Regarding areas that are probabilistic, with a wide range of outcomes (the fourth category), Mauboussin comments on economic and political forecasts:

The evidence shows that collectives outperform experts in solving these problems.  For instance, economists are extremely poor forecasters of interest rates, often failing to accurately guess the direction of rate moves, much less their correct level.  Note, too, that not only are experts poor at predicting actual outcomes, they rarely agree with one another.  Two equally credentialed experts may make opposite predictions and, hence, decisions from one another.

Mauboussin notes that experts do relatively well with rule-based problems with a wide range of outcomes because they can be better than computers at eliminating bad choices and making creative connections between bits of information.  A fascinating example: Eric Bonabeau, a physicist, has developed programs that generate alternative designs for packaging using the principles of evolution (recombination and mutation).  But the experts select the best designs at the end of the process, since the computers have no taste.

Yet computers will continue to make big improvements in this category (rule-based problems with a wide range of outcomes).  For instance, many chess programs today can beat any human, whereas there was only one program (IBM’s Deep Blue) that could do this in the late 1990’s.  Also, in October 2015, Google DeepMind’s program AlphaGo beat Fan Hui, the European Go champion.

Note:  We still need experts to make the systems that replace them.  Severts had to set up the prediction market.  Ashenfelter had to find the regression equation.  And experts need to stay on top of the systems, making improvements when needed.

Also, experts are still needed for many areas in strategy, including innovation and creativity.  And people are needed to deal with people.  (Although many jobs will soon be done by robots.)

I’ve written before about how simple quant models outperform experts in a wide variety of areas: https://boolefund.com/simple-quant-models-beat-experts-in-a-wide-variety-of-areas/

 

SITUATIONAL AWARENESS

Mauboussin writes about the famous experiment by Solomon Asch.  The subject is shown lines of obviously different lengths.  But in the same room with the subject are shills, who unbeknownst to the subject have already been instructed to say that two lines of obviously different lengths actually have the same length.  So the subject of the experiment has to decide between the obvious evidence of his eyes—the two lines are clearly different lengths—and the opinion of the crowd.  A significant number (36.8 percent) ignored their own eyes and went with the crowd, saying that the two lines had equal length, despite the obvious fact that they didn’t.

(Photo by D-janous, via Wikimedia Commons)

Mauboussin notes that the interesting question about the Solomon Asch experiment is: what’s going on in the heads of people who conform?  Asch himself suggested three possibilities:

Distortion of judgment.  The subjects conclude that their perceptions are wrong and that the group is right.

Distortion of action.  These individuals suppress their own knowledge in order to go with the majority.

Distortion of perception.  This group is not aware that the majority opinion distorts their estimates.

Unfortunately, Asch didn’t have the tools to try to test these possibilities.  Gregory Berns, a neuroscientist, five decades after Asch, used functional magnetic resonance imaging (fMRI) in the lab at Emory University.

For the conforming subjects, the scientists found activity in the areas of the brain that were related to perception of the object.  Also, the scientists did not find a meaningful change in activity in the frontal lobe—an area associated with activities like judgment.  Thus, for conforming subjects, it is a distortion of perception: what the majority claims to see, the subject actually does see.  Remarkable.

What about the people who remained independent when faced with the group’s wrong responses?  Those subjects showed increased activity in the amygdala, a region that signals to prepare for immediate action (fight or flight).  Mauboussin comments: “…while standing alone is commendable, it is unpleasant.”

Priming

Mauboussin:

How do you feel when you read the word ‘treasure’? … If you are like most people, just ruminating on ‘treasure’ gives you a little lift.  Our minds naturally make connections and associate ideas.  So if someone introduces a cue to you—a word, a smell, a symbol—your mind often starts down an associative path.  And you can be sure the initial cue will color a decision that waits at the path’s end.  All this happens outside your perception.

(Subconscious as brain under water, Illustration by Agawa288)

Scientists did the following experiment:

In this test, the researchers placed the French and German wines next to each other, along with small national flags.  Over two weeks, the scientists alternated playing French accordion music and German Bierkeller pieces and watched the results.  When French music played, French wines represented 77 percent of the sales.  When German music played, consumers selected German wines 73 percent of the time… The music made a huge difference in shaping purchases.  But that’s not what the shoppers thought…

While the customers acknowledged that the music made them think of either France or Germany, 86 percent denied that the tunes had any influence on their choice.  This experiment is an example of priming, which psychologists formally define as ‘the incidental activation of knowledge structures by the current situational context.’  In other words, what comes in through our senses influences how we make decisions, even when it seems completely irrelevant in a logical sense.  Priming is by no means limited to music.  Researchers have manipulated behavior through exposure to words, smells, and visual backgrounds.

Mauboussin gives some examples of priming:

  • Immediately after being exposed to words associated with the elderly, primed subjects walked 13 percent slower than subjects seeing neutral words.
  • Exposure to the scent of an all-purpose cleaner prompted study participants to keep their environment tidier while eating a crumbly biscuit.
  • Subjects reviewing Web pages describing two sofa models preferred the more comfortable model when they saw a background with puffy clouds, and favored the cheaper sofa when they saw a background with coins.

The Fault of the Default

While virtually 100 percent of Austrians have consented to be an organ donor, only 12 percent of Germans have.  The difference is due entirely to how the choice is presented.  In Austria, you must opt-out of being an organ donor—being an organ donor is the default choice.  In Germany, you must opt-in to being an organ donor—not being a donor is the default choice.  But this directly translates into many more saved lives in Austria than in Germany.

Illustration by hafakot

Mauboussin makes an important larger point.  We tend to assume that people decide what is best for them independent of how the choice is framed, but in reality, “many people simply go with the default options.”  This includes consequential areas (in addition to organ donation) like savings, educational choice, medical alternatives, etc.

The Power of Inertia

To overcome inertia, Peter Drucker suggested asking: “If we did not do this already, would we, knowing what we now know, go into it?”

Dr. Atul Gawande, author of The Checklist Manifesto, tells the story of Dr. Peter Pronovost, an anesthesiologist and critical-care specialist at the Johns Hopkins Hospital.  Pronovost’s father died due to a medical error, which led Pronovost to dedicate his career to ensuring the safety of patients.  Mauboussin explains:

In the United States, medical professionals put roughly 5 million lines into patients each year, and about 4 percent of those patients become infected within a week and a half.  The added cost of treating those patients is roughly $3 billion per year, and the complications result in twenty to thirty thousand annual preventable deaths.

Pronovost came up with a simple checklist because he observed that physicians in a hurry would often overlook some simple routine that is normally done as a part of safety.  It saved numerous lives and millions of dollars in the first few years at Johns Hopkins Hospital, so Pronovost got the Michigan Health & Hospital Association to try the checklist.  After just three months, the rate of infection dropped by two-thirds.  After eighteen months, the checklist saved 1,500 lives and nearly $200 million.

 

MORE IS DIFFERENT

Mauboussin covers complex adaptive systems such as the stock market or the economy.  His advice, when dealing with a complex adaptive system, is:

  • Consider the system at the correct level.  An individual agent in the system can be very different from one outside the system.
  • Watch for tightly coupled systems.  A system is tightly coupled when there is no slack between items, allowing a process to go from one stage to the next without any opportunity to intervene.  (Examples include space missions and nuclear power plants.)  Most complex adaptive systems are loosely coupled, where removing or incapacitating one or a few agents has little impact on the system’s performance.
  • Use simulations to create virtual worlds.  Simulation is a tool that can help our learning process.  Simulations are low cost, provide feedback, and have proved their value in other domains like military planning and pilot training.

Mauboussin notes that complex adaptive systems often perform well at the system level, despite dumb agents (consider ants or bees).  Moreover, there are often unintended consequences that can lead to failure when well-meaning humans try to manage a complex system towards a particular goal.

 

EVIDENCE OF CIRCUMSTANCE

Decisions that work well in one context can often fail miserably in a different context.  The right answer to many questions that professionals face is: “It depends.”

Mauboussin writes about how most people make decisions based on a theory, even though often they are not aware of it.  Two business professors, Paul Carlile and Clayton Christensen, describe three stages of theory building:

  • The first stage is observation, which includes carefully measuring a phenomenon and documenting the results.  The goal is to set common standards so that subsequent researchers can agree on the subject and the terms to describe it.
  • The second stage is classification, where researchers simplify and organize the world into categories to clarify the differences among phenomena.  Early in theory development, these categories are based predominantly on attributes.
  • The final stage is definition, or describing the relationship between the categories and the outcomes.  Often, these relationships start as simple correlations.

What’s especially important, writes Mauboussin:

Theories improve when researchers test predictions against real-world data, identify anomalies, and subsequently reshape the theory.  Two crucial improvements occur during this refining process.  In the classification stage, researchers evolve the categories to reflect circumstances, not just attributes.  In other words, the categories go beyond what works to when it works.  In the definition stage, the theory advances beyond simple correlations and sharpens to define causes—why it works.  This pair of improvements allows people to go beyond crude estimates and to tailor their choices to the situation they face.

Here is what is often done:  Some successes are observed, some common attributes are identified, and it is proclaimed that these attributes can lead others to success.  This doesn’t work.

By the same logic, a company should not adopt a strategy without understanding the conditions under which it succeeds or fails.  Mauboussin gives the example of Boeing outsourcing both the design and the building of sections of the Dreamliner to its suppliers.  This was a disaster.  Boeing had to pull the design work back in-house.

The Colonel Blotto Game

Each player gets a hundred soldiers (resources) to distribute across three battlefields (dimensions).  The players make their allocations in secret.  Then the players’ choices are simultaneously revealed, and the winner of each battle is whichever army has more soldiers in that battlefield.  The overall winner is whichever player wins the most battles.  What’s interesting is how the game changes as you adjust one of the two parameters (resources, dimensions).

Mauboussin observes that it’s not intuitive how much advantage additional points give to one side in a three-battlefield game:

In a three-battlefield game, a player with 25 percent more resources has a 60 percent expected payoff (the proportion of battles the player wins), and a player with twice the resources has a 78 percent expected payoff.  So some randomness exists, even in contests with fairly asymmetric resources, but the resource-rich side has a decisive advantage.  Further, with low dimensions, the game is largely transitive: if A can beat B and B can beat C, then A can beat C.  Colonel Blotto helps us to understand games with few dimensions, such as tennis.

Things can change even more unexpectedly when the number of dimensions is increased:

But to get the whole picture of the payoffs, we must introduce the second parameter, the number of dimensions or battlefields.  The more dimensions the game has, the less certain the outcome (unless the players have identical resources).  For example, a weak player’s expected payoff is nearly three times higher in a game with fifteen dimensions than in a nine-dimension game.  For this reason, the outcome is harder to predict in a high-dimension game than in a low-dimension game, and as a result there are more upsets.  Baseball is a good example of a high-dimension game…

What may be most surprising is that the Colonel Blotto game is highly nontransitive (except for largely asymmetric, low-dimension situations).  This means that tournaments often fail to reveal the best team.  Mauboussin gives an example where A beats B, B beats C, C beats A, and all of them beat D.  Because there is no best player, the winner of a tournament is simply “the player who got to play D first.”  Mauboussin:

Because of nontransitivity and randomness, the attribute of resources does not always prevail over the circumstance of dimensionality.

Bottom Line on Attributes vs. Circumstances

Mauboussin sums up the  main lesson on attributes versus circumstances:

Most of us look forward to leveraging our favorable experiences by applying the same approach to the next situation.  We also have a thirst for success formulas—key steps to enrich ourselves.  Sometimes our experience and nostrums work, but more often they fail us.  The reason usually boils down to the simple reality that the theories guiding our decisions are based on attributes, not circumstances.  Attribute-based theories come very naturally to us and often appear compelling… However, once you realize the answer to most questions is, ‘It depends,’ you are ready to embark on the quest to figure out what it depends on.

 

PHASE TRANSITIONS—“GRAND AH-WHOOMS”

Just a small incremental change in temperature leads to a change from solid to liquid or from liquid to gas.  Philip Ball, a physicist and author of Critical Mass: How One Thing Leads to Another, calls it a grand ah-whoom.

(Illustration by Designua)

Critical Points, Extremes, and Surprise

In part due to the writings of Nassim Taleb, people are more aware of black swans, or extreme outcomes within a power law distribution.  According to Mauboussin, however, what most people do not yet appreciate is how black swans are caused:

Here’s where critical points and phase transitions come in.  Positive feedback leads to outcomes that are outliers.  And critical points help explain our perpetual surprise at black swan events because we have a hard time understanding how such small incremental perturbations can lead to such large outcomes.

Mauboussin explains critical points in social systems.  Consider the wisdom of crowds: Crowds tend to make accurate predictions when three conditions prevail—diversity, aggregation, and incentives.

Diversity is about people having different ideas and different views of things.  Aggregation means you can bring the group’s information together.  Incentives are rewards for being right and penalties for being wrong that are often, but not necessarily, monetary.

Mauboussin continues:

For a host of psychological and sociological reasons, diversity is the most likely condition to fail when humans are involved.  But what’s essential is that the crowd doesn’t go from smart to dumb gradually.  As you slowly remove diversity, nothing happens initially.  Additional reductions may also have no effect.  But at a certain critical point, a small incremental reduction causes the system to change qualitatively.

Blake LeBaron, an economist at Brandeis University, has done an experiment.  LaBaron created a thousand investors within the computer and gave them money, guidelines on allocating their portfolios, and diverse trading rules.  Then he let the system play out.  As Mauboussin describes:

His model was able to replicate many of the empirical features we see in the real world, including cycles of booms and crashes.  But perhaps his most important finding is that a stock price can continue to rise even while the diversity of decision rules falls.  Invisible vulnerability grows.  But then, ah-whoom, the stock price tumbles as diversity rises again.  Writes LaBaron, ‘During the run-up to a crash, population diversity falls.  Agents begin using very similar trading strategies as their common good performance is reinforced.  This makes the population very brittle, in that a small reduction in the demand for shares could have a strong destabilizing impact on the market.’

The Problem of Induction, Reductive Bias, and Bad Predictions

Extrapolating from what we see or have seen, to what will happen next, is a common decision-making mistake.  Nassim Taleb retells Bertrand Russell’s story of a turkey (Taleb said turkey instead of chicken to suit his American audience).  The turkey is fed a thousand days in a row.  The turkey feels increasingly good until the day before Thanksgiving, when an unexpected event occurs.  None of the previous one thousand days has given the turkey any clue about what’s next.  Mauboussin explains:

The equivalent of the turkey’s plight—sharp losses following a period of prosperity—has occurred repeatedly in business.  For example, Merrill Lynch (which was acquired by Bank of America) suffered losses over a two-year period from 2007 to 2008 that were in excess of one-third of the profits it had earned cumulatively in its thirty-six years as a public company….

The term black swan reflects the criticism of induction by the philosopher Karl Popper.  Popper argued that seeing lots of white swans doesn’t prove the theory that all swans are white, but seeing one black swan does disprove it.  So Popper’s point is that to understand a phenomenon, we’re better off focusing on falsification than on verification.  But we’re not naturally inclined to falsify something.

Black swan, Photo by Dr. Jürgen Tenckhoff

Not only does System 1 naturally look for confirming evidence.  But even System 2 uses a positive test strategy, looking for confirming evidence for any hypothesis, rather than looking for disconfirming evidence.

People have a propensity to stick to whatever they currently believe.  Most people rarely examine or test their beliefs (hypotheses).  As Bertrand Russell pointed out:

Most people would rather die than think;  many do.

People are generally overconfident.  Reductive bias means that people tend to believe that reality is much simpler and more predictable than it actually is.  This causes people to oversimplify complex phenomena.  Instead of properly addressing the real questions—however complex and difficult—System 1 naturally substitutes an easier question.  The shortcuts used by System 1 work quite well in simple environments.  But these same shortcuts lead to predictable errors in complex and random environments.

System 2—which can be trained to do logic, statistics, and complex computations—is naturally lazy.  It requires conscious effort to activate System 2 .  If System 1 recognizes a serious threat, then System 2 can be activated if needed.

The problem is that System 1 does not recognize the dangers associated with complex and random environments.  Absent an obvious threat, System 1 will nearly always oversimplify complex phenomena.  This creates overconfidence along with comforting illusions—”everything makes sense” and “everything is fine.”  But complex systems frequently undergo phase transitions, and some of these new phases have sharply negative consequences, especially when people are completely unprepared.

Even very smart people routinely oversimplify and are inclined to trust overly simple mathematical models—for instance, models that assume a normal distribution even when the distribution is far from normal.  Mauboussin argues that Long-Term Capital Management, which blew up in the late 1990’s, had oversimplified reality by relying too heavily on its financial models.  According to their models, the odds of LTCM blowing up—as it did—were astronomically low (1 out of billions).  Clearly their models were very wrong.

Mauboussin spoke with Benoit Mandelbrot, the French mathematician and father of fractal geometry.  Mauboussin asked about the reductive bias.  Mandelbrot replied that the wild randomness of stock markets was clearly visible for all to see, but economists continued to assume mild randomness, largely because it simplified reality and made the math more tractable.  If you assume a normal distribution, the math is much easier than if you tried to capture the wildness and complexity of  reality:

Mandelbrot emphasized that while he didn’t know what extreme event was going to happen in the future, he was sure that the simple models of the economists would not anticipate it.

Mauboussin gives the example of David Li’s formula, which measures the correlation of default between assets.  (The formula is known as a Gaussian copula function.)  Li’s equation could measure the likelihood that two or more assets within a portfolio would default at the same time.  This “opened the floodgates” for financial engineers to create new products, including collateralized debt obligations (bundles of corporate bonds), and summarize the default correlation using Li’s equation “rather than worry about the details of how each corporate bond within the pool would behave.”

Unfortunately, Li’s equation oversimplified a complex world: Li’s equation did not make any adjustments for the fact that many correlations can change significantly.

The failure of Long-Term Capital Management illustrates how changing correlations can wreak havoc.  LTCM observed that the correlation between its diverse investments was less than 10 percent over the prior five years.  To stress test its portfolio, LTCM assumed that correlations could rise to 30 percent, well in excess of anything the historical data showed.  But when the financial crisis hit in 1998, the correlations soared to 70 percent.  Diversification went out the window, and the fund suffered mortal losses.  ‘Anything that relies on correlation is charlatanism,’ scoffed Taleb.  Or, as I’ve heard traders say, ‘The only thing that goes up in a bear market is correlation.’

Music Lab

Duncan Watts, a sociologist, led a trio of researchers at Columbia University in doing a social experiment.  Subjects went to a web site—Music Lab—and were invited to participate in a survey.  Upon entering the site, 20 percent of the subjects were assigned to an independent world and 10 percent each to eight worlds where people could see what other people were doing.

In the independent world, subjects were free to listen to songs, rated them, and download them, but they had no information about what other subjects were doing.  In each of the other eight worlds, the subjects could see how many times other people had downloaded each song.

The subjects in the independent world collectively gave a reasonable indication of the quality of each of the songs.  Thus, you could see for the other eight worlds whether social influence made a difference or not.

Song quality did play a role in the ranking, writes Mauboussin.  A top-five song in the independent world had about a 50 percent chance of finishing in the top five in a social influence world.  And the worst songs rarely topped the charts.  But how would you guess the average song did in the social worlds?

The scientists found that social influence played a huge part in success and failure.  One song, ‘Lockdown’ by the band 52metro, ranked twenty-sixth in the independent world, effectively average.  Yet it was the number one song in one of the social influence worlds, and number forty in another.  Social influence catapulted an average song to hit status in one world—ah-whoom—and relegated it to the cellar in another.  Call it Lockdown’s lesson.

In the eight social worlds, the songs the subjects downloaded early in the experiment had a huge influence on the songs subjects downloaded later.  Since the patterns of download were different in each social world, so were the outcomes.

(Illustration by Mindscanner)

Mauboussin summarizes the lessons:

  • Study the distribution of outcomes for the system you are dealing with.  Taleb defines gray swans as “modelable extreme events,” which are events you can at least prepare for, as opposed to black swans, which are by definition exceedingly difficult to prepare for.
  • Look for ah-whoom moments.  In social systems, you must be mindful of the level of diversity.
  • Beware of forecasters.  Especially for phase transitions, forecasts are generally dismal.
  • Mitigate the downside, capture the upside.  One of the Kelly criterion’s central lessons is that betting too much in a system with extreme outcomes leads to ruin.

 

SORTING LUCK FROM SKILL

In areas such as business, investing, and sports, people make predictable and natural mistakes when it comes to distinguishing skill from luck.  Consider reversion to the mean:

The idea is that for many types of systems, an outcome that is not average will be followed by an outcome that has an expected value closer to the average.  While most people recognize the idea of reversion to the mean, they often ignore or misunderstand the concept, leading to a slew of mistakes in their analysis.

Reversion to the mean was discovered by the Victorian polymath Francis Galton, a cousin of Charles Darwin.  For instance, Dalton found that tall parents tend to have children that are tall, but not as talltheir heights are closer to the mean.  Similarly, short parents tend to have children that are short, but not as shorttheir heights are closer to the mean.

Yet it’s equally true that tall people have parents that are tall, but not as tallthe parents’ heights are closer to the mean.  Similarly, short people have parents that are short, but not as shorttheir heights are closer to the mean.  Thus, Dalton’s crucial insight was that the overall distribution of heights remains stable over time: the proportions of the population in every height category was stable as one looks forward or backward in time.

Skill, Luck, and Outcomes

Mauboussin writes that Daniel Kahneman was asked to offer a formula for the twenty-first century.  Kahneman gave two formulas:

Success = Some talent + luck

Great success = Some talent + a lot of luck

Consider an excellent golfer who scores well below her handicap during the first round.  What do you predict will happen in the second round?  We expect the golfer to have a score closer to her handicap for the second round because we expect there to be less luck compared to the first round.

Illustration by iQoncept

When you think about great streaks in sports like baseball, the record streak always belongs to a very talented player.  So a record streak is a lot of talent plus a lot of luck.

 

TIME TO THINK TWICE

You don’t need to think twice before every decision.  The stakes for most decisions are low.  And even when the stakes are high, the best decision is often obvious enough.

The value of Think Twice is in situations with high stakes where your natural decision-making process will typically lead to a suboptimal choice.  Some final thoughts:

Raise Your Awareness

As Kahneman has written, it is much easier to notice decision-making mistakes in others than in ourselves.  So pay careful attention not only to others, but also to yourself.

It is difficult to think clearly about many problems.  Furthermore, after outcomes have occurred, hindsight bias causes many of us to erroneously recall that we assigned the outcome a much higher probability than we actually did ex ante.

Put Yourself in the Shoes of Others

Embracing the outside view is typically essential when making an important probabilistic decision.  Although the situation may be new for us, there are many others who have gone through similar things.

When it comes to understanding the behavior of individuals, often the situationor specific, powerful incentivescan overwhelm otherwise good people.

Also, be careful when trying to understand or to manage a complex adaptive system, whether an ecosystem or the economy.

Finally, leaders must develop empathy for people.

Recognize the Role of Skill and Luck

When luck plays a significant role, anticipate reversion to the mean: extreme outcomes are followed by more average outcomes.

Short-term investment results reflect a great deal of randomness.

Get Feedback

Timely, accurate, and clear feedback is central to deliberate practice, which is the path to gaining expertise.  The challenge is that in some fields, like long-term investing, most of the feedback comes with a fairly large time lag.

For investors, it is quite helpful to keep a journal detailing the reasons for every investment decision.  (If you have the time, you can also write down how you feel physically and mentally at the time of each decision.)

 

(Photo by Vinay_Mathew)

A well-kept journal allows you to clearly audit your investment decisions.  Otherwise, most of us will lose any ability to recall accurately why we made the decisions we did.  This predictable memory lossin the absence of careful written recordsis often associated with hindsight bias.

It’s essential to identifyregardless of the outcomewhen you have made a good decision and when you have made a bad decision.  A good decision means that you faithfully followed a solid, proven process.

Another benefit of a well-kept investment journal is that you will start to notice other factors or patterns associated with bad investment decisions.  For instance, too much stress or too much fatigue is often associated with poorer decisions.  On the other hand, a good mood is often associated with overconfident decisions.

Mauboussin mentions a story told by Josh Waitzkin about Tigran Petrosian, a former World Chess Champion:

“When playing matches lasting days or weeks, Petrosian would wake up and sit quietly in his room, carefully assessing his own mood.  He then built his game plan for the day based on that mood, with great success.  A journal can provide a structured tool for similar introspection.”

Create a Checklist

Mauboussin:

When you face a tough decision, you want to be able to think clearly about what you might inadvertently overlook.  That’s where a decision checklist can be beneficial.

Photo by Andrey Popov

Mauboussin again:

A good checklist balances two opposing objectives.  It should be general enough to allow for varying conditions, yet specific enough to guide action.  Finding this balance means a checklist should not be too long; ideally, you should be able to fit it on one or two pages.

If you have yet to create a checklist, try it and see which issues surface.  Concentrate on steps or procedures, and ask where decisions have gone off track before.  And recognize that errors are often the result of neglecting a step, not from executing the other steps poorly.

Perform a Premortem

Mauboussin explains:

You assume you are in the future and the decision you made has failed.  You then provide plausible reasons for that failure.  In effect, you try to identify why your decision might lead to a poor outcome before you make the decision.  Klein’s research shows that premortems help people identify a greater number of potential problems than other techniques and encourage more open exchange, because no one individual or group has invested in a decision yet.

…You can track your individual or group premortems in your decision journal.  Watching for the possible sources of failure may also reveal early signs of trouble.

Know What You Can’t Know

  • In decisions that involve a system with many interacting parts, causal links are frequently unclear…. Remember what Warren Buffet said: ‘Virtually all surprises are unpleasant.’  So considering the worst-case scenarios is vital and generally overlooked in prosperous times.
  • Also, resist the temptation to treat a complex system as if it’s simpler than it is…. We can trace most of the large financial disasters to a model that failed to capture the richness of outcomes inherent in a complex system like the stock market.

Mauboussin notes a paradox with decision making: Nearly everyone realizes its importance, but hardly anyone practices (or keeps a journal).  Mauboussin concludes:

There are common and identifiable mistakes that you can understand, see in your daily affairs, and manage effectively.  In those cases, the correct approach to deciding well often conflicts with what your mind naturally does.  But now that you know when to think twice, better decisions will follow.  So prepare your mind, recognize the context, apply the right techniqueand practice.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

Shoe Dog

July 24, 2022

Shoe Dog is the autobiography of Phil Knight, the creator of Nike.  Bill Gates mentioned this book as one of his favorites in 2016, saying it was “a refreshingly honest reminder of what the path to business success really looks like:  messy, precarious, and riddled with mistakes.”

After the introduction, Knight has a chapter for each year, starting in 1962 and going through 1980.

 

DAWN

Knight introduces his story:

On paper, I thought, I’m an adult.  Graduated from a good college – University of Oregon.  Earned a master’s from a top business school – Stanford.  Survived a yearlong hitch in the U.S. Army – Fort Lewis and Fort Eustis.  My resume said I was a learned, accomplished soldier, a twenty-four-year-old man in full… So why, I wondered, why do I still feel like a kid?

Worse, like the same shy, pale, rail-thin kid I’d always been.

Maybe because I still hadn’t experienced anything of life.  Least of all its many temptations and excitements.  I hadn’t smoked a cigarette, hadn’t tried a drug.  I hadn’t broken a rule, let alone a law.  The 1960s were just underway, the age of rebellion, and I was the only person in America who hadn’t yet rebelled.  I couldn’t think of one time I’d cut loose, done the unexpected.

I’d never even been with a girl.

If I tended to dwell on all the things I wasn’t, the reason was simple.  Those were the things I knew best.  I’d have found it difficult to see who or what exactly I was, or might become.  Like all my friends I wanted to be successful.  Unlike my friends I didn’t know what that meant.  Money?  Maybe.  Wife?  Kids?  House?  Sure, if I was lucky.  These were the goals I was taught to aspire to, and part of me did aspire to them, instinctively.  But deep down I was searching for something else, something more.  I had an aching sense that our time is short, shorter than we ever know, short as a morning run, and I wanted mine to be meaningful.  And purposeful.  And creative.  And important.  Above all… different.

I wanted to leave a mark on the world…

And then it happened.  As my young heart began to thump, as my pink lungs expanded like the wings of a bird, as the trees turned to greenish blurs, I saw it all before me, exactly what I wanted my life to be.  Play.

Yes, I thought.  That’s it.  That’s the word.  The secret of happiness, I’d always suspected, the essence of beauty or truth, or all we ever need to know of either, lay somewhere in that moment when the ball is in midair, when both boxers sense that approach of the bell, when the runners near the finish line and the crowd rises as one.  There’s a kind of exuberant clarity in that pulsing half second before winning and losing are decided.  I wanted that, whatever that was, to be my life, my daily life.

(Sweet Sixteen Syracuse vs. Gonzaga, March 25, 2016, Photo by Ryan Dickey, Wikimedia Commons)

Knight continues:

At different times, I’d fantasized about becoming a great novelist, a great journalist, a great statesman.  But the ultimate dream was always to be a great athlete.  Sadly, fate had made me good, not great.  At twenty-four, I was finally resigned to that fact.  I’d run track at Oregon, and I’d distinguished myself, lettering three of four years.  But that was that, the end.  Now, as I began to clip off one brisk six-minute mile after another, as the rising sun set fire to the lowest needles of the pines, I asked myself:  What if there were a way, without being an athlete, to feel what athletes feel?  To play all the time, instead of working?  Or else to enjoy work so much that it becomes essentially the same thing.

I was suddenly smiling.  Almost laughing.  Drenched in sweat, moving as gracefully and effortlessly as I ever did, I saw my Crazy Idea shining up ahead, and it didn’t look all that crazy.  It didn’t even look like an idea.  It looked like a place.  It looked like a person, or some life force that existed long before I did, separate from me, but also part of me.  Waiting for me, but also hiding from me.  That might sound a little high-flown, a little crazy.  But that’s how I felt back then.

…At twenty-four, I did have a crazy idea, and somehow, despite being dizzy with existential angst, and fears about the future, and doubts about myself, as all young men and women in their midtwenties are, I did decide that the world is made up of crazy ideas.  History is one long processional of crazy ideas.  The things I loved most – books, sports, democracy, free enterprise – started as crazy ideas.

For that matter, few ideas are as crazy as my favorite thing, running.  It’s hard.  It’s painful.  It’s risky.  The rewards are few and far from guaranteed… Whatever pleasures or gains you drive from the act of running, you must find them within.  It’s all in how you frame it, how you sell it to yourself.

(Runner silhouette, Illustration by Msanca)

Knight:

So that morning in 1962 I told myself:  Let everyone else call your idea crazy… just keep going.  Don’t stop.  Don’t even think about stopping until you get there, and don’t give much thought to where ‘there’ is.  Whatever comes, just don’t stop.

That’s the precocious, prescient, urgent advice I managed to give myself, out of the blue, and somehow managed to take.  Half a century later, I believe it’s the best advice – maybe the only advice – any of us should ever give.

 

1962

Knight explains that his crazy idea started as a research paper for a seminar on entrepreneurship at Stanford.  He became obsessed with the project.  As a runner, he knew about shoes.  He also knew that some Japanese products, such as cameras, had recently gained much market share.  Perhaps Japanese running shoes might do the same thing.

When Knight presented his idea to his classmates, everyone was bored.  No one asked any questions.  But Knight held on to his idea.  He imagined pitching it to a Japanese shoe company.  Knight also conceived of the idea of seeing the world on his way to Japan.  He wanted to see “the world’s most beautiful and wondrous places.”

And its most sacred.  Of course I wanted to taste other foods, hear other languages, dive into other cultures, but what I really craved was connection with a capital C.  I wanted to experience what the Chinese call Tao, the Greeks call Logos, the Hindus call Jnana, the Buddhists call Dharma.  What the Christians call Spirit.  Before setting out on my own personal life voyage, I thought, let me first understand the greater voyage of humankind.  Let me explore the grandest temples and churches and shrines, the holiest rivers and mountaintops.  Let me feel the presence of… God?

Yes, I told myself, yes.  For lack of a better word, God.

But Knight needed his father’s blessing and cash in order to make the trip around the world.

At the time, most people had never been on an airplane.  Also, Knight’s father’s father had died in an air crash.  As for the shoe company idea, Knight was keenly aware that twenty-six out of twenty-seven new companies failed.  Knight then notes that his father, besides being a conventional Episcopalian, also liked respectability.  Traveling around the world just wasn’t done except by beatniks and hipsters.

Knight then adds:

Possibly, the main reason for my father’s respectability fixation was a fear of his inner chaos.  I felt this, viscerally, because every now and then that chaos would burst forth.

Knight tells about having to pick his father up from his club.  On these evenings, Knight’s father had had too much to drink.  But father and son would pretend nothing was wrong.  They would talk sports.

Knight’s mom’s mom, “Mom Hatfield” – from Roseburg, Oregon – warned “Buck” (Knight’s nickname) that the Japanese would take him prisoner and gouge out his eyeballs.  Knight’s sisters, four years younger (twins), Jeanne and Joanne, had no reaction.  His mom didn’t say anything, as usual, but seemed proud of his decision.

Knight asked a Stanford classmate, Carter, a college hoops star, to come with him.  Carter loved to read good books.  And he liked Buck’s idea.

The first stop was Honolulu.  After seeing Hawaiian girls, then diving into the warm ocean, Buck told Carter they should stay.  What about the plan?  Plans change.  Carter liked the new idea and grinned.

They got jobs selling Encyclopedias door-to-door.  But their main mission was learning how to surf.  “Life was heaven.”  Except that Buck couldn’t sell encyclopedias.  He thought he was getting shier as he got older.

So he tried a job selling securities.  Specifically, Dreyfus funds for Investors Overseas Services, Bernard Cornfeld’s firm.  Knight had better luck with this.

Eventually, the time came for Buck and Carter to continue on their trip around the world.  However, Carter wasn’t sure.

He’d met a girl.  A beautiful Hawaiian teenager with long brown legs and jet-black eyes, the kind of girl who’d greeted our airplane, the kind I dreamed of having and never would.  He wanted to stick around, and how could I argue?

Buck hesitated, not sure he wanted to continue on alone.  But he decided not to stop his journey.  He bought a plane ticket that was good for one year on any airline going anywhere.

When Knight got to Tokyo, much of the city was black because it still hadn’t been rebuilt after the bombing.

American B-29s.  Superfortresses.  Over a span of several nights in the summer of 1944, waves of them dropped 750,000 pounds of bombs, most filled with gasoline and flammable jelly.  One of the world’s oldest cities, Tokyo was made largely of wood, so the bombs set off a hurricane of fire.  Some three hundred thousand people were burned alive, instantly, four times the number who died in Hiroshima.  More than a million were gruesomely injured.  And nearly 80 percent of the buildings were vaporized.  For long, solemn stretches the cab driver and I said nothing.  There was nothing to say.

Fortunately, Buck’s father knew some people in Tokyo at United Press International.  They advised Buck to talk to two ex-GI’s who ran a monthly magazine, the Importer.

First, Knight spent long periods of time in walled gardens reading about Buddhism and Shinto.  He liked the concept of kensho, or sartori – a flash of enlightenment.

But according to Zen, reality is nonlinear.  No past, no present.  All is now.  That required Knight to change his thinking.  There is no self.  Even in competition, all is one.

Knight decided to mix it up and visited the Tokyo Stock Exchange – Tosho.  All was madness and yelling.  Is this what it’s all about?

Knight sought peace and enlightenment again.  He visited the garden of the nineteenth century emperor Meiji and his empress.  This particular place was thought to possess great spiritual power.  Buck sat beneath the ginkgo trees, beside the gorgeous torii gate, which was thought of as a portal to the sacred.

Next it was Tsukiji, the world’s largest fish market.  Tosho all over again.

Then to the lakes region in the Northern Hakone mountains.  An area that inspired many of the great Zen poets.

Knight went to see the two ex-GI’s.  They told him how they’d fallen in love with Japan during the Occupation.  So they stayed.  They had managed to keep the import magazine going for seventeen years thus far.

Knight told them he liked the Tiger shoes produced by Onitsuka Co. in Kobe, Japan.  The ex-GI’s gave him tips on negotiating with the Japanese:

‘No one ever turns you down, flat.  No one ever says, straight out, no.  But they don’t say yes, either.  They speak in circles, sentences with no clear subject or object.  Don’t be discouraged, but don’t be cocky.  You might leave a man’s office thinking you’ve blown it, when in fact he’s ready to do a deal.  You might leave thinking you’ve closed a deal, when in fact you’ve just been rejected.  You never know.’

Knight decided to visit Onitsuka right away, with the advice fresh in his mind.  He managed to get an appointment, but got lost and arrived late.

When he did arrive, several executives met him.  Ken Miyazaki showed him the factory.  Then they went to a conference room.

Knight had rehearsed this scene his head, just like he used to visualize his races.  But one thing he hadn’t prepared for was the recent history of World War II hanging over everything.  The Japanese had heroically rebuilt, putting the war behind them.  And these Japanese executives were young.  Still, Knight thought, their fathers and uncles had tried to kill his.  In brief, Knight hesitated and coughed, then finally said, “Gentlemen.”

Mr. Miyazaki interrupted, “Mr. Knight.  What company are you with?”

Knight replied, “Ah, yes.  Good question.”  Knight experienced fight or flight for a moment.  A random jumble of thoughts flickered in his mind until he visualized his wall of blue ribbons from track.  “Blue Ribbon… Gentleman, I represent Blue Ribbon Sports of Portland, Oregon.”

Knight presented his basic argument, which was that the American shoe market was huge and largely untapped.  If Onitsuka could produce good shoes and price them below Adidas, it could be highly profitable.  Knight had spent so much time on his research paper at Stanford that he could simply quote it and come across as eloquent.

The Japanese executives started talking excitedly together, then suddenly stood up and left the room.  Knight didn’t know if he had been rejected.  Perhaps he should leave.  He waited.

Then they came back into the room with sketches of different Tiger shoes.  They told him they had been thinking about the American market for some time.  They asked Knight how big he thought the market could be.  Knight tossed out, “$1 billion.”  He doesn’t know where the number came from.

They asked him if Blue Ribbon would be interested in selling Tigers in the United States.  Yes, please send samples to this address, Knight said, and I’ll send a money order for fifty dollars.

Knight considered returning home to get a jump on the new business.  But then he decided to finish his trek around the world.

Hong Kong, then the Phillipines.

I was fascinated by all the great generals, from Alexander the Great to George Patton.  I hated war, but I loved the warrior spirit.  I hated the sword, but loved the samurai.  And of all the great fighting men in history I found MacArthur the most compelling.  Those Ray-Bans, that corncob pipe – the man didn’t lack for confidence.  Brilliant tactician, master motivator, he also went on to head the U.S. Olympic Committee.  How could I not love him?

Of course, he was deeply flawed.  But he knew that…

Bangkok.  He made his way to Wat Phra Kaew, a huge 600-year-old Buddha carved from one hunk of jade.  One of the most sacred statues in Asia.

(Emerald Buddha at Wat Phra Kaew, Image by J. P. Swimmer, Wikimedia Commons)

Vietnam, where U.S. soldiers filled the streets.  Everyone knew a very ugly and different war was coming.

Calcutta.  Knight got sick immediately.  He thinks food poisoning.  He was sure, for one whole day, that he was going to die.  He rallied.  He ended up at the Ganges.  There was a funeral.  Others were bathing.  Others were drinking the same water.

“The Upanishads say, Lead me from the unreal to the real.”  So Knight went to Kathmandu and hiked up the Himalayas.

Back to India.  Bombay.

Kenya.  Giant ostriches tried to outrun the bus, records Knight.  When Masai warriors boarded the bus, a baboon or two would also try to board.

Cairo.  The Giza plateau.  Standing besides desert nomads with their silk-draped camels.  At the foot of the Great Sphinx.

…The sun hammered down on my head, the same sun that hammered down on the thousands of men who built these pyramids, and the millions of visitors who came after.  Not one of them was remembered, I thought.  All is vanity, says the Bible.  All is now, says Zen.  All is dust, says the desert.

(Great Sphinx of Giza, Photo by Johnny 201, Wikimedia Commons)

Then Jerusalem.

…the first century rabbi Eleazar ben Azariah said our work is the holiest part of us.  All are proud of their craft.  God speaks of his work;  how much more should man.

Istanbul.  Turkish coffee.  Lost on the confusing streets of the Bosphorus.  Glowing minarets.  Then the golden labyrinths of Topkapi Palace.

Rome.  Tons of pasta.  And the most beautiful women and shoes he’d ever seen, says Knight.  The Coliseum.  The Vatican.  The Sistine Chapel.

Florence.  Reading Dante.  Milan.  Da Vinci:  One of his obsessions was the human foot, which he called a masterpiece of engineering.

Venice.  Marco Polo.  The palazzo of Robert Browning:  “If you get simply beauty and naught else, you get about the best thing God invents.”

Paris.  The Pantheon.  Rousseau.  Voltaire:  “Love truth, but pardon error.”  Praying at Notre Dame.  Lost in the Louvre.

(The Louvre, Photo by Pipiten, Wikimedia Commons)

Then to where Joyce slept, and F. Scott Fitzgerald.  Walking down the Seine, and stopping where Hemingway and Dos Passos read the New Testament aloud to each other.

Next, up the Champs-Elysees, along the liberators’ path, thinking of Patton:  “Don’t tell people how to do things, tell them what to do and let them surprise you with their results.”

Munich.  Berlin.  East Berlin:

…I looked around, all directions.  Nothing.  No trees, no stores, no life.  I thought of all the poverty I’d seen in every corner of Asia.  This was a different kind of poverty, more willful, somehow, more preventable.  I saw three children playing in the street.  I walked over, took their picture.  Two boys and a girl, eight years old.  The girl – red wool hat, pink coat – smiled directly at me.  Will I ever forget her?  Or her shoes?  They were made of cardboard.

Vienna.  Stalin, Trotsky, Tito, Hitler, Jung, Freud.  All at the same location in the same time period.  A “coffee-scented crossroads.”  Where Mozart walked.  Crossing the Danube.  The spires of St. Stephen’s Church, where Beethoven realized he was deaf.

London.  Buckingham Palace, Speakers’ Corner, Harrods.

Knight asked himself what the highlight of his trip was.

Greece, I thought.  No question.  Greece.

…I meditated on that moment, looking up at those astonishing columns, experiencing that bracing shock, the kind you receive from all great beauty, but mixed with a powerful sense of – recognition?

Was it only my imagination?  After all, I was standing at the birthplace of Western civilization.  Maybe I merely wanted it to be familiar.  But I don’t think so.  I had the clearest thought:  I’ve been here before.

Then, walking up those bleached steps, another thought:  This is where it all begins.

On my left was the Parthenon, which Plato had watched the teams of architects and workmen build.  On my right was the Temple of Athena Nike.  Twenty-five centuries ago, per my guidebook, it had housed a beautiful frieze of the goddess Athena, thought to be the bringer of “nike,” or victory.

It was one of many blessings Athena bestowed.  She also rewarded the dealmakers.  In the Oresteia she says:  ‘I admire… the eyes of persuasion.’  She was, in a sense, the patron saint of negotiators.

(Temple of Athena Nike, Photo by Steve Swayne, Wikimedia Commons)

 

1963

When Buck got home, his hair was to his shoulders and his beard three inches long.  It had been four months since meeting with Onitsuka.  But they hadn’t sent the sample shoes.  Knight wrote to them to ask why.  They wrote back, “Shoes coming… In a little more days.”

Knight got a haircut and shaved.  He was back.  His father suggested he speak with his old friend, Don Frisbee, CEO of Pacific Power & Light.  Frisbee had an MBA from Harvard.  Frisbee told Buck to get his CPA while he was young, a relatively conservative way to put a floor under his earnings.  Knight liked that idea.  He had to take three more courses in accounting, first, which he promptly did at Portland State.

Then Knight worked at Lybrand, Ross Bros. & Montgomery.  It was a Big Eight national firm, but its Portland office was small.  $500 a month and some solid experience.  But pretty boring.

 

1964

Finally, twelve pairs of shoes arrived from Onitsuka.  They were beautiful, writes Knight.  He sent two pairs immediately to his old track coach at Oregon, Bill Bowerman.

Bowerman was a genius coach, a master motivator, a natural leader of young men, and there was one piece of gear he deemed crucial to their development.  Shoes.

Bowerman was obsessed with shoes.  He constantly took his runners’ shoes and experimented on them.  He especially wanted to make the shoes lighter.  One ounce over a mile is fifty pounds.

Bowerman would try anything.  Kangaroo.  Cod.  Knight says four or five runners on the team were Bowerman’s guinea pigs.  But Knight was his “pet project.”

It’s possible that everything I did in those days was motivated by some deep yearning to impress, to please, Bowerman.  Besides my father there was no man whose approval I craved more, and besides my father there was no man who gave it less often.  Frugality carried over to every part of the coach’s makeup.  He weighed and hoarded words of praise, like uncut diamonds.

After you’d won a race, if you were lucky, Bowerman might say:  ‘Nice race.’  (In fact, that’s precisely what he said to one of his milers after the young man became one of the very first to crack the mythical four-minute mark in the United States.)  More likely Bowerman would say nothing.  He’d stand before you in his tweed blazer and ratty sweater vest, his string tie blowing in the wind, his battered ball cap pulled low, and nod once.  Maybe stare.  Those ice-blue eyes, which missed nothing, gave nothing.  Everyone talked about Bowerman’s dashing good looks, his retro crew cut, his ramrod posture and planed jawline, but what always got me was that gaze of pure violet blue.

(Statue of Bill Bowerman, Photo by Diane Lee Jackson, Wikimedia Commons)

For his service in World War II, Bowerman received the Silver Star and four Bronze Stars.  Bowerman eventually became the most famous track coach in America.  But he hated being called “coach,” writes Knight.  He called himself, “Professor of Competitive Responses” because he viewed himself as preparing his athletes for the many struggles and competitions that lay ahead in life.

Knight did his best to please Bowerman.  Even so, Bowerman would often lose patience with Knight.  On one occasion, Knight told Bowerman he was coming down with the flu and wouldn’t be able to practice.  Bowerman told him to get his ass out there.  The team had a time trial that day.  Knight was close to tears.  But he kept his composure and ran one of his best times of the year.  Bowerman gave him a nod afterward.

Bowerman suggested meeting for lunch shortly after seeing the Tiger shoes from Onitsuka.  At lunch, Bowerman told Knight the shoes were pretty good and suggested they become business partners.  Knight was shocked.

Had God himself spoken from the whirlwind and asked to be my partner, I wouldn’t have been more surprised.

Knight and Bowerman signed an agreement soon thereafter.  Knight found himself thinking again about his coach’s eccentricities.

…He always went against the grain.  Always.  For example, he was the first college coach in America to emphasize rest, to place as much value on recovery as on work.  But when he worked you, brother, he worked you.  Bowerman’s strategy for running the mile was simple.  Set a fast pace for the first two laps, run the third as hard as you can, then triple your speed on the fourth.  There was a Zen-like quality to this strategy because it was impossible.  And yet it worked.  Bowerman coached more sub-four-minute milers than anybody, ever.

Knight wrote Onitsuka and ordered three hundred pairs of shoes, which would cost $1,ooo.  Buck had to ask his dad for another loan, who asked him, “Buck, how long do you think you’re going to keep jackassing around with these shoes?”  His father told him he didn’t send him to Oregon and Stanford to be a door-to-door shoe salesman.

At this point, Knight’s mother told him she wanted to purchase a pair of Tigers.  This helped convince Knight’s father to give him another loan.

In April 1964, Knight got the shipment of Tigers.  Also, Mr. Miyazaki told him he could be the distributor for Onitsuka in the West.  Knight quit his accounting job to focus on selling shoes that spring.  His dad was horrified, his mom happy, remarks Knight.

After being rejected by a couple of sporting goods stores, Knight decided to travel around to various track meets in the Pacific Northwest.  Between races, he’d talk with the coaches, the runners, the fans.  He couldn’t write the orders fast enough.  Knight wondered how this was possible, given his inability to sell encyclopedias.

…So why was selling shoes so different?  Because, I realized, it wasn’t selling.  I believed in running.  I believed that if people got out and ran a few miles every day, the world would be a better place, and I believed these shoes were better to run in.  People, sensing my belief, wanted some of that belief for themselves.

Belief, I decided.  Belief is irresistable.

(Illustration by Lkeskinen0)

Knight started the mail order business because he started getting letters from folks wanting Tigers.  To help the process along, he mailed some handouts with big type:

‘Best news in flats!  Japan challenges European track shoe domination!  Low Japanese labor costs make it possible for an exciting new firm to offer these shoes at the low, low price of $6.95.’  [Note:  This is close to $54 in 2018 dollars, due to inflation.]

Knight had sold out his first shipment by July 4, 1964.  So he ordered 900 more.  This would cost $3,000.  His dad grudgingly gave him a letter of guarantee, which Buck took to the First National Bank of Oregon.  They approved the loan.

Knight wondered how to sell in California.  He couldn’t afford airfare.  So every other weekend, he’d stuff a duffel bag with Tigers.  He’d don his army uniform and head to the local air base.  The MPs would wave him on to the next military transport to San Francisco or Los Angeles.

When in Los Angeles, he’d save more money by staying with a friend from Stanford, Chuck Cale.  At a meet at Occidental College, a handsome guy approached Knight, introducing himself as Jeff Johnson.  He was a fellow runner whom Knight had run with and against while at Stanford.  At this point, Johnson was studying anthropology and planning on becoming a social worker.  But he was selling shoes – Adidas then – on weekends.  Knight tried to recruit him to sell Tigers instead.  No, because he was getting married and needed stability, responded Johnson.

Then Knight got a letter from a high school wrestling coach in Manhasset, New York, claiming that Onitsuka had named him the exclusive distributor for Tigers in the United States.  He ordered Knight to stop selling Tigers.

Knight contacted his cousin, Doug Houser, who’d recently graduated from Stanford Law School.  Houser found out Mr. Manhasset was a bit of a celebrity, a model who was one of the original Marlboro Men.  Knight:  “Just what I need.  A pissing match with some mythic American cowboy.”

Knight went into a funk for awhile.  Then he decided to go visit Onitsuka in Japan.  Knight bought a new suit and also a book, How to Do Business with the Japanese.

Knight realized he had to remain cool.  Emotion could be fatal.

The art of competition, I’d learned from track, was the art of forgetting, and now I reminded myself of that fact.  You must forget your limits.  You must forget your doubts, your pain, your past.  You must forget that internal voice screaming, begging, ‘Not one more step!’  And when it’s not possible to forget it, you must negotiate with it.  I thought over all the races in which my mind wanted one thing, and my body wanted another, those laps in which I’d had to tell my body, ‘Yes, you raise some excellent points, but let’s keep going anyway…’

After finding a place to stay in Kobe, Knight called Onitsuka and requested a meeting.  He got a call back saying Mr. Miyazaki no longer worked there.  Mr. Morimoto had replaced him, and didn’t want Knight to visit headquarters.  Mr. Morimoto would meet him for tea.  None of this was good.

At the meeting, Knight layed out the arguments.  They had had an agreement.  He also pointed out the very robust sales Blue Ribbon had had thus far.  He dropped the name of his business partner.  Mr. Morimoto, who was about Knight’s age, said he’d get back to him.

Knight thought it was over.  But then he got a call from Morimoto saying, “Mr. Onitsuka… himself… wishes to see you.”

At this meeting, Knight first presented his arguments again to those who were initially present.  Then Mr. Onitsuka arrived.

Dressed in a dark blue Italian suit, with a head of black hair as thick as shag carpet, he filled every man in the conference room with fear.  He seemed oblivious, however.  For all his power, for all his wealth, his movements were deferential… Morimoto tried to summarize my reasons for being there.  Mr. Onitsuka raised a hand, cut him off.

Without preamble, he launched into a long, passionate monologue.  Some time ago, he’d said, he’d had a vision.  A wondrous glimpse of the future.  ‘Everyone in the world wear athletic shoes all the time,’ he said.  ‘I know this day will come.’  He paused, looking around the table at each person, to see if they also knew.  His gaze rested on me.  He smiled.  I smiled.  He blinked twice.  ‘You remind me of myself when I am young,’ he said softly.  His stared into my eyes.  One second.  Two.  Now he turned his gaze to Morimoto.  ‘This about those thirteen western states?’ he said.  ‘Yes,’ Morimoto said.  ‘Hm,’ Onitsuka said.  ‘Hmmmm.’  He narrowed his eyes, looked down.  He seemed to be meditating.  Again he looked up at me.  ‘Yes,’ he said.  ‘Alright.  You have western states.’

Knight ordered $3,400 worth of shoes [about $26,000 in 2018 dollars].

(Mount Fuji, Photo by Wipark Kulnirandorn)

To celebrate, Knight decided to climb to the top of Mount Fuji.  Buck met a girl on the wap up, Sarah, who was studying philosophy at Connecticut College for Women.  It went well for a time.  Many letters back and forth.  A couple of visits.  But she decided Knight wasn’t “sophisticated” enough.  Jeanne, one of Buck’s younger sisters, found the letters, read them, and told Buck, “You’re better off without her.”  Buck then asked his sister – given her interest in mail – if she’d like to help with the mail order business for $1.50 an hour.  Sure.  Blue Ribbon Employee Number One.

 

1965

Buck got a letter from Johnson.  He’d bought some Tigers and loved them.  Could he become a commissioned salesman for Blue Ribbon?  Sure.  $1.75 for each pair of running shoes, $2 for spikes, were the commissions.

Then the letters from Johnson kept coming:

I liked his energy, of course.  And it was hard to fault his enthusiasm.  But I began to worry he might have too much of each.  With the twentieth letter, or the twenty-fifth, I began to worry that the man might be unhinged.  I wondered why everything was so breathless.  I wondered if he was ever going to run out of things he urgently needed to tell me, or ask me…

…He wrote to say that he wanted to expand his sales territory beyond California, to include Arizona, and possibly New Mexico.  He wrote to suggest that we open a retail store in Los Angeles.  He wrote to tell me that he was considering placing ads in running magazines and what did I think?  He wrote to inform me that he’d placed those ads in running magazines and the response was good.  He wrote to ask why I hadn’t answered any of his previous letters.  He wrote to plead for encouragement.  He wrote to complain that I hadn’t responded to his previous plea for encouragement.

I’d always considered myself a conscientious correspondent… And I always meant to answer Johnson’s letters.  But before I got around to it, there was always another one, waiting.  Something about the sheer volume of his correspondence stopped me…

Eventually Johnson realized he loved shoes and running more than anthropology or social work.

(Monk meditating, Photo by Ittipon)

In his heart of hearts Johnson believed that runners are God’s chosen, that running, done right, in the correct spirit and with the proper form, is a mystical exercise, no less than meditation or prayer, and thus he felt called to help runners reach their nirvana.  I’d been around runners much of my life, but this kind of dewy romanticism was something I’d never encountered.  Not even the Yahweh of running, Bowerman, was as pious about the sport as Blue Ribbon’s Part-Time Employee Number Two.

In fact, in 1965, running wasn’t even a sport.  It wasn’t popular, it wan’t unpopular, it just was.  To go out for a three-mile run was something weirdos did, presumably to burn off manic energy.  Running for exercise, running for pleasure, running for endorphins, running to live better and longer – these things were unheard of.

People often went out of their way to mock runners.  Drivers would slow down and honk their horns.  ‘Get a horse!,’ they’d yell, throwing a beer or soda at the runner’s head.  Johnson had been drenched by many a Pepsi.  He wanted to change all this…

Above all, he wanted to make a living doing it, which was next to impossible in 1965.  In me, in Blue Ribbon, he thought he saw a way.

I did everything I could to discourage Johnson from thinking like this.  At every turn, I tried to dampen his enthusiasm for me and my company.  Besides not writing back, I never phoned, never visited, never invited him to Oregon.  I also never missed an opportunity to tell him the unvarnished truth.  I put it flatly:  ‘Though our growth has been good, I owe First National Bank of Oregon $11,000… Cash flow is negative.’

He wrote back immediately, asking if he could work for me full-time…

Knight just shook his head.  Finally in last summer of 1965, Knight accepted Johnson’s offer.  Johnson had been making $460 as a social worker, so he proposed $400 a month [over $3,000 a month in 2018 dollars].  Knight very reluctantly agreed.  It seemed like a huge sum.  Knight writes:

As ever, the accountant in me saw the risk, the entrepreneur the possibility.  So I split the difference and kept moving forward.

Knight then forgot about Johnson because he had bigger issues.  Blue Ribbon had doubled its sales in one year.  But Knight’s banker said they were growing too fast for their equity.  Knight asked how doubling sales – profitably – can be a bad thing.

In those days, however, commercial banks were quite different from investment banks.  Commercial banks never wanted you to outgrow your cash balance.  Knight tried to explain that growing sales as much as possible – profitably – was essential to convince Onitsuka to stick with Blue Ribbon.  And then there’s the monster, Adidas.  But his banker kept repeating:

‘Mr. Knight, you need to slow down.  You don’t have enough equity for this kind of growth.’

Knight kept hearing the word “equity” in his head over and over.  “Cash,” that’s what it meant.  But he was deliberately reinvesting every dollar – on a profitable basis.  What was the problem?

Every meeting with his banker, Knight managed to hold his tongue and say nothing, basically agreeing.  Then he’d keep doubling his orders from Onitsuka.

Knight’s banker, Harry White, had essentially inherited the account.  Previously, Ken Curry was Knight’s banker, but Curry bailed when Knight’s father wouldn’t guarantee the account in the case of business failure.

Furthermore, the fixation on equity didn’t come from White, but from White’s boss, Bob Wallace.  Wallace wanted to be the next president of the bank.  Credit risks were the main roadblock to that goal.

Oregon was smaller back then.  First National and U.S. Bank were the only banks, and the second one had already turned Blue Ribbon down.  So Knight didn’t have a choice.  Also, there as no such thing as venture capital in 1965.

(First National Bank of Oregon, Photo by Steve Morgan, Wikimedia Commons)

To make matters worse, Onitsuka was always late in its shipments, no matter how much Knight pleaded with them.

By this point, Knight had passed the four parts of the CPA exam.  So he decided to get a job as an accountant.  He invested a good chunk of his paycheck into Blue Ribbon.

In analyzing companies as an accountant, Knight learned how they sold things or didn’t, how they survived or didn’t.  He learned how companies got into trouble and how they got out.

It was while working for the Portland branch of Price Waterhouse that he met Delbert J. Hayes, who was the best accountant in the office.  Knight describes Hayes as a man with “great talent, great wit, great passions – and great appetites.”  Hayes was six-foot-two and three hundred pounds.  He loved food and alcohol.  And he smoked two packs a day.

Hayes looked at numbers the way a poet looks at clouds or a geologist looks at rocks, says Knight.  He could see the beauty of numbers.  Numbers were a secret code.

Every evening, Hayes would insist on taking junior accountants out for a drink.  Hayes would talk nonstop, like he drank.  But while other accountants dismissed Hayes’ stories, Knight always paid careful attention.  In every tale told by Hayes was some piece of wisdom about business.  So Knight would match Hayes, shot for shot, in order to learn as much as he could.

The following morning, Knight was always sick.  But he willed himself to do the work.  Being in the Army Reserves at the same time wasn’t easy.  Meanwhile, the conflict in Vietnam was heating up.  Knight:

I had grown to hate that war.  Not simply because I felt it was wrong.  I also felt it was stupid, wasteful.  I hated stupidity.  I hated waste.  Above all, that war, more than other wars, seemed to be run along the same principles of my bank.  Fight not to win, but to avoid losing.  A surefire losing strategy.

Hayes came to appreciate Knight.  Hayes thought it was a tough time to launch a new company with zero cash balance.  But he did acknowledge that having Bowerman as a partner was a valuable, intangible asset.

Recently, Bowerman and Mrs. Bowerman had visited Onitsuka and charmed everyone.  Mr. Onitsuka told Bowerman about founding his shoe company in the rubble after World War II.

He’d built his first lasts, for a line of basketball shoes, by pouring hot wax from Buddhist candles over his own feet.  Though the basketball shoes didn’t sell, Mr. Onitsuka didn’t give up.  He simply switched to running shoes, and the rest is shoe history.  Every Japanese runner in the 1964 Games, Bowerman told me, was wearing Tigers.

Mr. Onitsuka also told Bowerman that the inspiration for the unique soles on Tigers had come to him while eating sushi.  Looking down at his wooden platter, at the underside of an octopus’s leg, he thought a similar suction cup might work on the sole of a runner’s flat.  Bowerman filed that away.  Inspiration, he learned, can come from quotidian things.  Things you might eat.  Or find lying around the house.

Bowerman started corresponding not only with Mr. Onitsuka, but with the entire production team at the Onitsuka factory.  Bowerman realized that Americans tend to be longer and heavier than the Japanese.  He thought the Tigers could be modified to fit Americans better.  Most of Bowerman’s letters went unanswered, but like Johnson Bowerman just kept writing more.

Eventually he broke through.  Onitsuka made prototypes that conformed to Bowerman’s vision of a more American shoe.  Soft inner sole, more arch support, heel wedge to reduce stress on the Achilles tendon – they sent the prototype to Bowerman and he went wild for it.  He asked for more.

Bowerman also experimented with drinks to help his runners recover.  He invented an early version of Gatorade.  As well, he conducted experiments to make the track softer.  He invented an early version of polyurethane.

 

1966

Johnson kept inundating Knight with long letters, including a boatload of parenthetical comments and a list of PS’s.  Knight felt he didn’t have time to send the requested words of encouragement.  Also, it wasn’t his style.

I look back now and wonder if I was truly being myself, or if I was emulating Bowerman, or my father, or both.  Was I adopting their man-of-few-words demeanor?  Was I maybe modeling all the men I admired?  At the time I was reading everything I could get my hands on about generals, samurai, shoguns, along with biographies of my three main heroes – Churchill, Kennedy, and Tolstoi.  I had no love of violence, but I was fascinated by leadership, or lack thereof, under extreme conditions…

I wasn’t that unique.  Throughout history men have looked to the warrior for a model of Hemingway’s cardinal virtue, pressurized grace… One lesson I took from all my home-schooling about heroes was that they didn’t say much.  None was a blabbermouth.  None micromanaged.  “Don’t tell people how to do things, tell them what to do and let them surprise you with their results.” 

(Winston Churchill in 1944, Wikimedia Commons)

Johnson never let Knight’s lack of communication discourage him.  Johnson was full of energy, passion, and creativity.  He was going all-out, seven days a week, to sell Blue Ribbon shoes.  Johnson had an index card for each customer including their shoe sizes and preferences.  He sent all of them birthday cards and Christmas cards.  Johnson developed extensive correspondence with hundreds of customers.

Johnson began aggregating customer feedback on the shoes.

…One man, for instance, complained that Tiger flats didn’t have enough cushion.  He wanted to run the Boston Marathon but didn’t think the Tigers would last the twenty-six miles.  So Johnson hired a local cobbler to graft rubber soles from a pair of shower shoes into a pair of Tiger flats.  Voila.  Johnsn’s Frankenstein flat had space-age, full-length, midsole cushioning.  (Today it’s standard in all training shoes for runners.)  The jerry-rigged Johnson sole was so dynamic, so soft, so new, Johnson’s customer posted a personal best in the Boston.  Johnson forwarded me the results and urged me to pass them along to Tiger.  Bowerman had just asked me to do the same with his batch of notes a few weeks earlier.  Good grief, I thought, one mad genius at a time.

Johnson had customers in thirty-seven states.  Knight meant to warn him about encroaching on Malboro Man’s territory.  But he never got around to it.

Knight did write to tell Johnson that if he could sell 3,250 shoes by the end of June 1966, then he could open the retail outlet he’d been asking about.  Knight calculated that 3,250 was impossible, so he wasn’t too worried.

Somehow Johnson hit 3,250.  So Blue Ribbon opened its first retail store in Santa Monica.

He then set about turning the store into a mecca, a holy of holies for runners.  He bought the most comfortable chairs he could find, and afford (yard sales), and he created a beautiful space for runners to hang out and talk.  He built shelves and filled them with books that every runner should read, many of them first editions from his own library.  He covered the walls with photos of Tiger-shod runners, and laid in a supply of silk-screened T-shirts with Tiger across the front, which he handed out to his best customers.  He also stuck Tigers to a black lacquered wall and illuminated them with a strip of can lights – very hip.  Very mod.  In all the world, there had never been a sanctuary for runners, a place that didn’t just sell them shoes but celebrated them and their shoes.  Johnson, the aspiring cult leader of runners, finally had his church.  Services were Monday through Saturday, nine to six.

When he first wrote me about the store, I thought of the temples and shrines I’d seen in Asia, and I was anxious to see how Johnson’s compared.  But there just wasn’t time…

Knight got a heads up that the Marlboro man had just launched an advertising campaign which involved poaching customers of Blue Ribbon.  So Knight flew down to see Johnson.

(Jeff Johnson, Employee Number One)

Johnson’s apartment was one giant running shoe.  There were running shoes seemingly everywhere.  And there were many books – mostly thick volumes on philosophy, religion, sociology, anthropology, and classics in Western literature.  Knight had thought he liked to read.  This was a new level, says Knight.

Johnson told Knight he had to go visit Onitsuka again.  Johnson started typing notes, ideas, lists, which would become a manifesto for Knight to take to Onitsuka.  Knight wired Onitsuka.  They got back to him, but it wasn’t Morimoto.  It was a new guy, Kitami.

Knight told Kitami and other executives about the performance of Blue Ribbon thus far, virtually doubling sales each year and projecting more of the same.  Kitami said they wanted someone more established, with offices on the East Coast.  Knight replied that Blue Ribbon had offices on the East Coast and could handle national distribution.  “Well,” said Kitami, “this changes things.”

The next morning, Kitami awarded Blue Ribbon exclusive distribution rights for the United States.  A three-year contract.  Knight promptly placed an order for 5,000 more shoes, which would cost $20,000 – more than $150,000 in 2018 dollars – that he didn’t have.  Kitami said he would ship them to Blue Ribbon’s East Coast office.

There was only one person crazy enough to move to the East Coast on a moment’s notice….

 

1967

Knight delayed telling Johnson.  Then he hired John Bork, a high school track coach and a friend of a friend, to run the Santa Monica store.  Bork showed up at the store and told Johnson that he, Bork, was the new boss so that Johnson could go back east.

Johnson called Knight.  Knight told him he’d had to tell Onitsuka that Blue Ribbon had an east coast office.  A huge shipment was due to arrive at this office.  Johnson was the only one who could manage the east coast store.  The fate of the company was on his shoulders.  Johnson was shocked, then mad, then freaked out.  Knight flew down to visit him.

Johnson talked himself into going to the east coast.

The forgiveness Johnson showed me, the overall good nature he demonstrated, filled me with gratitude, and a new fondness for the man.  And perhaps a deeper loyalty.  I regretted my treatment of him.  All those unanswered letters.  There are team players, I thought, and then there are team players, and then there’s Johnson.

Soon thereafter, Bowerman called asking Knight to add a new employee – Geoff Hollister.  A former track guy.  Full-time Employee Number Three.

Then Bowerman called again with yet another employee – Bob Woodell.

I knew the name, of course.  Everyone in Oregon knew the name.  Woodell had been a standout on Bowerman’s 1965 team.  Not quite a star, but a gritty and inspiring competitor.  With Oregon defending its second national championship in three years, Woodell had come out of nowhere and won the long jump against vaunted UCLA.  I’d been there, I’d watched him do it, and I’d come away mighty impressed.

The very next day, during a celebration, there had been an accident.  The float twenty guys were carrying collapsed after someone lost their footing.  It landed on Woodell and crushed one of his vertebra, paralyzing his legs.

Knight called Woodell.  Knight realized it was best to keep it strictly business.  So he told Woodell that Bowerman had recommended him.  Would he like to grab lunch to discuss the possibility of working for Blue Ribbon?  Sure thing, he said.

Woodell had already mastered a special car, a Mercury Cougar with hand controls.  At lunch, they hit it off and Woodell impressed Knight.

I wasn’t certain what Blue Ribbon was, or if it would ever become a thing at all, but whatever it was or might become, I hoped it would have something of this man’s spirit.

Knight offered Woodell a job opening a second retail store, in Eugene, for a monthly salary of $400.  Woodell immediately agreed.  They shook hands.  “He still hand the strong grip of an athlete.”

(Bob Woodell 1967)

Bowerman’s latest experiment was with the Spring Up.  He noticed the outer sole melted, whereas the midsole remained solid.  He convinced Onitsuka to fuse the outer sole to the midsole.  The result looked like the ultimate distance training shoe.  Onitsuka also accepted Bowerman’s suggestion of a name for the shoe, the “Aztec,” in homage to the upcoming 1968 Olympics in Mexico City.  Unfortunately, Adidas had a similar name for one of its shoes and threatened to sue.  So Bowerman changed the name to “Cortez.”

The situation with Adidas reminded Knight of when he had been a runner in high school.  The fastest runner in the state was Jim Grelle (pronounced “Grella”) and Knight had been second-fastest.  So Knight spent many races staring at Grelle’s back.  Then they both went to Oregon, so Knight spent more years staring at Grelle’s back.

Adidas made Knight think of Grelle.  Knight felt super motivated.

Once again, in my quixotic effort to overtake a superior opponent, I had Bowerman as my coach.  Once again he was doing everything he could to put me in position to win.  I often drew on the memory of his old prerace pep talks, especially when we were up against our blood rivals, Oregon State.  I would replay Bowerman’s epic speeches… Nearly sixty years later it gives me chills to recall his words, his tone.  No one could get your blood going like Bowerman, though he never raised his voice.

Thanks to the Cortez, Blue Ribbon finished the year strong.  They had nearly doubled their sales again, to $84,000.  Knight rented an office for $50 a month.  And he transferring Woodell to the “home office.”  Woodell had shown himself to be highly skilled and energetic, and in particular, he was excellent at organizing.

The office was cold and the floor was warped.  But it was cheap.  Knight built a corkboard wall, pinning up different Tiger models and borrowing some of Johnson’s ideas from the Santa Monica store.

Knight thought perhaps he could save even more money by living at his office.  Then he reflected that living at your office was what a crazy person does.  Then he got a letter from Johnson saying he was living at his office.  Johnson had set up shop in Wellesley, a suburb of Boston.

Johnson told Knight how he had chosen the location.  He’d seen people running along country roads, many of them women.  Ali MacGraw look-alikes.  Sold.

 

1968

Knight:

I wanted to dedicate every minute of every day to Blue Ribbon… I wanted to be present, always.  I wanted to focus constantly on the one task that really mattered.  If my life was to be all work and no play, I wanted my work to be play.  I wanted to quit Price Waterhouse.  Not that I hated it;  it just wasn’t me.

I wanted what everyone wants.  To be me, full-time.

Even though Blue Ribbon was on track to double sales again, there was never enough cash, certainly not to pay Knight.  Knight found another job he thought might fit better with his desire to focus as much as possible on Blue Ribbon.  Assistant Professor of Accounting at Portland State University.

Knight, a CPA who had worked for two accounting firms, knew accounting pretty well at this point.  But he was restless and twitchy, with several nervous tics – including wrapping rubber banks around his wrist and snapping them.  One of his students was named Penelope Parks.  Knight was captivated by her.

Knight decided to use the Socratic method to teach accounting.  Miss Parks turned out to be the best student in the class.  Soon thereafter, Miss Parks asked if Knight would be her advisor.  Knight then asked her if she’d like a job for Blue Ribbon to help with bookkeeping.  “Okay.”

On Miss Parks’ first day at Blue Ribbon, Woodell gave her a list of things – typing, bookkeeping, scheduling, stocking, filing invoices – and told her to pick two.  Hours later, she’d done every thing on the list.  Within a week, Woodell and Knight couldn’t remember how they’d gotten by without her, recalls Knight.

Furthermore, Miss Parks was “all-in” with respect to the mission of Blue Ribbon.  She was good with people, too.  She had a healing effect on Woodell, who was still struggling to adjust to his legs being paralyzed.

Knight often volunteered to go get lunch for the three of them.  But his head was usually so full of business matters that he would invariably get the orders mixed up.  “Can’t wait to see what I’m eating for lunch today,” Woodell might say quietly.  Miss Parks would hide a smile.

Later on, Knight found out that Miss Parks and Woodell weren’t cashing any of their paychecks.  They truly believed in Blue Ribbon.  It was more than just a job for them.

Knight and Penny started dating.  They were good at communicating nonverbally since they were both shy people.  They were a good match and eventually decided to get married.  Knight felt like she was a partner in life.

Knight made another trip to Onitsuka.  Kitami was very friendly this time, inviting him to the company’s annual picnic.  Knight met a man named Fujimoto at the picnic.  It turned out to be another life-altering partnership.

…I was doing business with a country I’d come to love.  Gone was the initial fear.  I connected with the shyness of the Japanese people, with the simplicity of their culture and products and arts.  I liked that they tried to add beauty to every part of life, from the tea ceremony to the commode.  I liked that the radio announced each day which cherry trees, on which corner, were blossoming, and how much.

(Cherry trees in Japan, Photo by Nathapon Triratanachat)

 

1969

Knight was able to hire more ex-runners on commission.  Sales in 1968 had been $150,000 and now they were on track for $300,000 for 1969.

Knight was finally able to pay himself a salary.  But before leaving Portland State, he happened to see a starving artist in the hallway and asked if she’d do advertising art part-time.  Her name was Carolyn Davidson, and she said OK.

Bowerman and Knight were losing trust in Kitami.  Bowerman thought he didn’t know much about shoes and was full of himself.  Knight hired Fujimoto to be a spy.  Knight pondered again that when it came to business in Japan, you never knew what a competitor or a partner would do.

Knight was absentminded.  He couldn’t go to the store and return with the one thing Penny asked for.  He misplaced wallets and keys frequently.  And he was constantly bumping into trees, poles, and fenders while driving.

Knight got in the habit of calling his father in the evening.  His father would be in his recliner, while Buck would be in his.  They’d hash things over.

Woodell and Knight began looking for a new office.  They started enjoying hanging out together.  Before parting, Knight would time Woodell on how fast he could fold up his wheelchair and get it and himself into his car.

Woodell was super positive and super energetic, a constant reminder of the importance of good spirits and a great attitude.

Buck and Penny would have Woodell over for dinner.  Those were fun times.  They would take turns describing what the company was and might be, and what it must never be.  Woodell was always dressed carefully and always had on a pair of Tigers.

Knight asked Woodell to become operations manager.  He’d demonstrated already that he was exceptionally good at managing day-to-day tasks.  Woodell was delighted.

 

1970

Knight visited Onitsuka again.  He discovered that Kitami was being promoted to operations manager.  Onitsuka and Blue Ribbon signed another 3-year agreement.  Knight looked into Kitami’s eyes and noticed something very cold.  Knight never forgot that cold look.

Knight pondered the fact that the shipments from Onitsuka were always late, and sometimes had the wrong sizes or even the wrong models.  Woodell and Knight discovered that Onitsuka always filled its orders from Japanese companies first, and then sent its foreign exports.

Meanwhile, Wallace at the bank kept making things difficult.  Knight concluded that a small public offerings could create the extra cash Blue Ribbon needed.  At the time, in 1970, a few venture capital firms had been launched.  But they were in California and mostly invested in high-tech.  So Knight formed Sports-Tek, Inc., as a holding company for Blue Ribbon.  They tried a small public offering.  It didn’t work.

Friends and family chipped in.  Woodell’s parents were particularly generous.

On June 15, 1970, Knight was shocked to see a Man of Oregon on the cover of Sports Illustrated.  His name was Steve Prefontaine.  He’d already set a national record in high school at the two-mile (8:41).  In 1970, he’d run three miles in 13:12.8, the fastest time on the planet.

Knight learned from a Fortune magazine about Japan’s hyper-aggressive sosa shoga, “trading companies.”  It was hard to see what these trading companies were exactly.  Sometimes they were importers.  Sometimes they were exporters.  Sometimes they were banks.  Sometimes they were an arm of the government.

After being harangued by Wallace at First National about cash balances again, Knight walked out and saw a sign for the Bank of Tokyo.  He was escorted to a back room, where a man appeared after a couple of minutes.  Knight showed the man his financials and said he needed credit.  The man said that Japan’s sixth-largest trading company had an office at the top floor of this same building.  Nissho Iwai was a $100-billion dollar company.

Knight met a man named Cam Murakami, who offered Knight a deal on the spot.  Knight said he had to check with Onitsuka first.  Knight wired Kitami, but heard nothing back at all for weeks.

Then Knight got a call from a guy on the east coast who told him that Onitsuka had approached him about becoming its new U.S. distributor.  Knight checked with Fujimoto, his spy.  Yes, it was true.  Onitsuka was considering a clean break with Blue Ribbon.

Knight invited Kitami to visit Blue Ribbon.

 

1971

March 1971.  Kitami was on his way.  Blue Ribbon vowed to give him the time of his life.

Kitami arrived with a personal assistant, Hiraku Iwano, who was just a kid.  At one point, Kitami told Knight that Blue Ribbon’s sales were disappointing.  Knight said sales were doubling every year.  “Should be triple some people say,” Kitami replied.  “What people?”, asked Knight.  “Never mind,” answered Kitami.

Kitami took a folder from his briefcase and repeated the charge.  Knight tried to defend Blue Ribbon.  Back and forth.  Kitami had to use the restroom.  When he left the meeting room, Knight looked into Kitami’s briefcase and tried to snag the folder that he thought Kitami had been referring to.

Kitami went back to his hotel.  Knight still had the folder.  He and Woodell opened it up.  They found a list of eighteen U.S. athletic shoe distributors.  These were the “some people” who told Kitami that Blue Ribbon wasn’t performing well enough.

I was outraged, of course.  But mostly hurt.  For seven years we’d devoted ourselves to Tiger shoes.  We’d introduced them to America, we’d reinvented the line.  Bowerman and Johnson had shown Onitsuka how to make a better shoe, and their designs were now foundational, setting sales records, changing the face of the industry – and this was how we were repaid?

At the end of Kitami’s visit, as planned, there was dinner with Bowerman, Mrs. Bowerman, and his friend (and lawyer), Jaqua.  Mrs. Bowerman usually didn’t allow alcohol, but she was making an exception.  Knight and Kitami both liked mai tais, which were being served.

Unfortunately, Bowerman had a few too many mai tais.  It appeared things might get out of hand.  Knight looked at Jaqua, remembering that he’d been a fighter pilot in World World II, and that his wingman, one of his closest friends, had been shot out of the sky by a Japanese Zero.  Knight thought he sensed something starting to erupt in Jaqua.

Kitami, however, was having a great time.  Then he found a guiter.  He started playing it and singing a country Western.  Suddenly, he sang “O Sole Mio.”

A Japanese businessman, strumming a Western guitar, singing an Italian ballad, in the voice of an Irish tenor.  It was surreal, then a few miles past surreal, and it didn’t stop.  I’d never know there were so many verses to “O Sole Mio.”  I’d never known a roomful of active, restless Oregonians could sit still and quite for so long.  When he set down the guitar, we all tried not to make eye contact with each other as we gave him a big hand.  I clapped and clapped and it all made sense.  For Kitami, this trip to the United States – the visit to the bank, the meetings with me, the dinner with the Bowermans – wasn’t about Blue Ribbon.  Nor was it about Onitsuka.  Like everything else, it was all about Kitami.

At a meeting soon thereafter, however, Kitami told Knight that Onitsuka wanted to buy Blue Ribbon.  If Blue Ribbon did not accept, Onitsuka would have to work with other distributors.  Knight knew he still needed Onitsuka, at least for awhile.  So he thought of a stall.  He told Kitami he’d have to talk with Bowerman.  Kitami said OK and left.

Knight sent the budget and forecast to First National.  White informed Knight at a meeting that First National would no longer be Blue Ribbon’s bank.  White was sick about it, the bank officers were divided, but it had been Wallace’s call.  Knight strove straight to U.S. Bank.  Sorry.  No.

Blue Ribbon was finishing 1971 with $1.3 million in sales, but it was in danger of failing.  Fortunately, Bank of Cal gave Blue Ribbon a small line of credit.

Knight went back to Nissho and met Tom Sumeragi.  Sumeragi told Knight that Nissho was willing to take a second position to their banks.  Also, Nissho had sent a delegation to Onitsuka to try to work out a deal on financing.  Onitsuka had tossed them out.  Nissho was embarrased that a $25 million company had thrown out a $100 billion company.  Sumeragi told Knight that Nissho could introduce him to other shoe manufacturers in Japan.

Knight knew he had to find a new shoe factory somewhere.  He found one in Gaudalajara, Mexico.  Knight placed an order for three thousand soccer shoes.  It’s at this point that Knight asked his part-time artist, Carolyn Davidson, to try to design a logo.  “Something that evokes a sense of motion.”  She came back two weeks later and her sketches had a theme.  But Knight was wondering what the theme was, “…fat lightning bolts?  Chubby check marks?  Morbidly obese squiggles?…”

Davidson returned later.  Same theme, but better.  Woodell, Johnson, and a few others liked it, saying it looked like a wing or a whoosh of air.  Knight wasn’t thrilled about it, but went along because they were out of time and had to send it to the factory in Mexico.

(Nike logo, Timidonfire, Wikimedia Commons)

They also needed a name.  Falcon.  Dimension Six.  These were possibilities they’d come up with.  Knight liked Dimension Six mostly because he’d come up with it.  Everyone told him it was awful.  It didn’t mean anything.  Bengal.  Condor.  They debated possibilities.

It was time to decide.  Knight still didn’t know.  Then Woodell told him that Johnson had had a dream and then woke up with the name clearly in mind:  “Nike.”

Knight reminisced…  “The Greek goddess of victory.  The Acropolis.  The Parthenon.  The Temple…”

Knight had to decide.  He hated having to decide under time pressure.  He’s not sure if it was luck or spirit or something else, but he chose “Nike.”  Woodell said, “Hm.”  Knight replied, “Yeah, I know.  Maybe it’ll grow on us.”

(Nike logo, Wikimedia Commons)

Meanwhile, Nissho was infusing Blue Ribbon with cash.  But Knight wanted a more permanent solution.  He conceived of a public offering of convertible debentures.  People bought them, including Knight’s friend Cale.

The factory in Mexico didn’t produce good shoes.  Knight talked with Sumeragi, who knew a great deal about shoe factories around the world.  Sumeragi also offered to introduce Knight to Jonas Senter, “a shoe dog.”

Shoe dogs were people who devoted themselves wholly to the making, selling, buying, or designing of shoes.  Lifers used the phrase cheerfully to describe other lifers, men and women who had toiled so long and hard in the shoe trade, they thought and talked about nothing else.  It was an all-consuming mania… But I understood.  The average person takes seventy-five hundred steps a day, 274 million steps over the course of a long life, the equivalent of six times around the globe – shoe dogs, it seemed to me, simply wanted to be part of that journey.  Shoes were their way of connecting with humanity…

Senter was the “knockoff king.”  He’d been behind a recent flood of knockoff Adidas.  Senter’s protege was a guy named Sole.

Knight wasn’t sure partnering with Nissho was the best move.  Jaqua suggested Knight meet with his brother-in-law, Chuck Robinson, CEO of Marcona Mining, which had many joint ventures.  Each of the big eight Japanese trading firms was a partner in at least one of Marcona’s mines, records Knight.  Chuck to Buck:  “If the Japanese trading company understands the rules from the first day, they will be the best partners you’ll ever have.”

Knight went to Sumeragi and said:  “No equity in my company.  Ever.”  Sumeragi consulted a few folks, came back and said:  “No problem.  But here’s our deal.  We take four percent off the top, as a markup on product.  And market interest rates on top of that.”  Done.

Knight met Sole, who mentioned five factories in Japan.

A bit later, Bowerman was eating breakfast when he noticed the waffle iron’s gridded pattern.  This gave him an idea and he started experimenting.

…he took a sheet of stainless steel and punched it with holes, creating a waffle-like surface, and brought this back to the rubber company.  The mold they made from that steel sheet was pliable, workable, and Bowerman now had two foot-sized squares of hard rubber nubs, which he brought home and sewed to the sole of a pair of running shoes.  He gave these to one of his runners.  The runner laced them on and ran like a rabbit.

Bowerman phoned me, excited, and told me about his experiment.  He wanted me to send a sample of his waffle-soled shoes to one of my new factories.  Of course, I said.  I’d send it right away – to Nippon Rubber.

I look back over decades and see him toiling in his workshop, Mrs. Bowerman carefully helping, and I get goosebumps.  He was Edison in Menlo Park, Da Vinci in Florence, Tesla in Wardenclyffe.  Divinely inspired.  I wonder if he knew, if he had any clue, that he was the Daedalus of sneakers, that he was making history, remaking an industry, transforming the way athletes would run and stop and jump for generations.  I wonder if he could conceive in that moment all he’d done.  All that would follow.

 

1972

The National Sporting Goods Association Show in Chicago in 1972 was extremely important for Blue Ribbon because they were going to introduce the world to Nike shoes.  If sales reps liked Nike shoes, Blue Ribbon had a chance to flourish.  If not, Blue Ribbon wouldn’t be back in 1973.

Right before the show, Onitsuka announced its “acquisition” of Blue Ribbon.  Knight had to reassure Sumeragi that there was no acquisition.  At the same time, Knight couldn’t break from Onitsuka just yet.

As Woodell and Johnson prepared the booth – with stacks of Tigers and also with stacks of Nikes – they realized the Nikes from Nippon Rubber weren’t as high-quality as the Tigers.  The swooshes were crooked, too.

Darn it, this was no time to be introducing flawed shoes.  Worse, we had to push these flawed shoes on to people who weren’t our kind of people.  They were salesmen.  They talked like salesmen, walked like salesmen, and they dressed like salesmen – tight polyester shirts, Sansabelt slacks.  They were extroverts, we were introverts.  They didn’t get us, we didn’t get them, and yet our future depended on them.  And now we’d have to persuade them, somehow, that this Nike thing was worth their time and trust – and money.

I was on the verge of losing it, right on the verge.  Then I saw Johnson and Woodell were already losing it, and I realized that I couldn’t afford to… ‘Look fellas, this is the worst the shoes will ever be.  They’ll get better.  So if we can just sell these… we’ll be on our way.’

The salesmen were skeptical and full of questions about the Nikes.  But by the end of the day, Blue Ribbon had exceeded its highest expectations.  Nikes had been the smash hit of the show.

Johnson was so perplexed that he demanded an answer from the representative of one his biggest accounts.  The rep explained:

‘We’ve been doing business with you Blue Ribbon guys for years and we know that you guys tell the truth.  Everyone else bullshits, you guys always shoot straight.  So if you say this new shoe, this Nike, is worth a shot, we believe.’

Johnson came back to the booth and said, “Telling the truth.  Who knew?”  Woodell laughed.  Johnson laughed.  Knight laughed.

Two weeks later, Kitami showed up without warning in Knight’s office, asking about “this… NEE-kay.”  Knight had been rehearsing for this situation.  He replied simply that it was a side project just in case Onitsuka drops Blue Ribbon.  Kitami seemed placated.

Kitami asked if the Nikes were in stores.  No, said Knight.  Kitami asked when Blue Ribbon was going to sell to Onitsuka.  Knight answered that he still needed to talk with Bowerman.  Kitami then said he had business in California, but would be back.

Knight called Bork in Los Angeles and told him to hide the Nikes.  Bork hid them in the back of the store.  But Kitami, when visiting the store, told Bork he had to use the bathroom.  While in the back, Kitami found stacks of Nikes.

Bork called Knight and told him, “Jig’s up… It’s over.”  Bork ended up quitting.  Knight discovered later that Bork had a new job… working for Kitami.

Kitami demanded a meeting.  Bowerman, Jaqua, and Knight were in attendance.  Jaqua told Knight to say nothing no matter what.  Jaqua told Kitami that he hoped something could still be worked out, since a lawsuit would be damaging to both companies.

Knight called a company-wide meeting to explain that Onitsuka had cut them off.  Many people felt resigned, says Knight, in part because there was a recession in the United States.  Gas lines, political gridlock, rising unemployment, Vietnam.  Knight saw the discouragement in the faces of Blue Ribbon employees, so he told them:

‘…This is the moment we’ve been waiting for.  One moment.  No more selling someone else’s brand.  No more working for someone else.  Onitsuka has been holding us down for years.  Their late deliveries, their mixed-up orders, their refusal to hear and implement our design ideas – who among us isn’t sick of dealing with all that?  It’s time we faced facts:  If we’re going to succeed, or fail, we should do so on our own terms, with our own ideas – our own brand.  We posted two million in sales last year… none of which had anything to do with Onitsuka.  That number was a testament to our ingenuity and hard work.  Let’s not look at this as a crisis.  Let’s look at this as our liberation.  Our Independence Day.’

Johnson told Knight, “Your finest hour.”  Knight replied he was just telling the truth.

The Olympic track-and-field trials in 1972 were going to be in Eugene.  Blue Ribbon gave Nikes to anyone who would take them.  And they handed out Nike T-shirts left and right.

The main event was on the final day, a race between Steve Prefontaine – known as “Pre” – and the great Olympian George Young.  Pre was the biggest thing to hit American track and field since Jesse Owens.  Knight tried to figure out why.  It was hard to say, exactly.  Knight:

Sometimes I thought the secret to Pre’s appeal was his passion.  He didn’t care if he died crossing the finish line, so long as he crossed first.  No matter what Bowerman told him, no matter what his body told him, Pre refused to slow down, ease off.  He pushed himself to the brink and beyond.  This was often a counterproductive strategy, and sometimes it was plainly stupid, and occasionally it was suicidal.  But it was always uplifting for the crowd.  No matter the sport – no matter the human endeavor, really – total effort will win people’s hearts.

(Steve Prefontaine)

Gerry Lindgren was also in this race with Pre and Young.  Lindgren may have been the best distance runner in the world at that time.  Lindgren had beaten Pre when Lindgren was a senior and Pre a freshman.

Pre took the lead right away.  Young tucked in behind him.  In no time they pulled way ahead of the field and it became a two-man affair… Each man’s strategy was clear.  Young meant to stay with Pre until the final lap, then use his superior sprint to go by and win.  Pre, meanwhile, intended to set such a fast pace at the outset that by the time they got to that final lap, Young’s legs would be gone.

For eleven laps they ran a half stride apart.  With the crowd now roaring, frothing, shrieking, the two men entered the final lap.  It felt like a boxing match.  It felt like a joust… Pre reached down, found another level – we saw him do it.  He opened up a yard lead, then two, then five.  We saw Young grimacing and we knew that he would not, could not, catch Pre.  I told myself, Don’t forget this.  Do not forget.  I told myself there was much to be learned from such a display of passion, whether you were running a mile or a company.

Both men had broken the American record.  Pre had broken it by a little bit more.

…What followed was one of the greatest ovations I’ve ever heard, and I’ve spent my life in stadiums.

I’d never witnessed anything quite like that race.  And yet I didn’t just witness it.  I took part in it.  Days later I felt sore in the hams and quads.  This, I decided, this is what sports are, what they can do.  Like books, sports give people a sense of having lived other lives, of taking part in other people’s victories.  And defeats.  When sports are at their best, the spirit of the fan merges with the spirit of the athlete, and in that convergence, in that transference, is the oneness that mystics talk about.

 

1973

Bowerman had retired from coaching, partly because of the sadness of the terrorist attacks at the 1972 Olympics in Munich.  Bowerman had been able to help hide one Israeli athlete.  Bowerman had immediately called the U.S. consul and shouted, “Send the marines!”  Eleven Israeli athletes had been captured and later killed.  An unspeakable tragedy.  Knight thought of the deaths of the two Kennedys, and Dr. King, and the students at Kent State.

Ours was a difficult, death-drenched age, and at least once every day you were forced to ask yourself:  What’s the point?

Although Bowerman had retired from coaching, he was still coaching Pre.  Pre had finished a disappointing fourth at the Olympics.  He could have gotten silver if he’d allowed another runner to be the front runner and if he’d coasted in his wake.  But, of course, Pre couldn’t do that.

It took Pre six months to re-emerge.  He won the NCAA three-mile for a fourth straight year, with a time of 13:05.3.  He also won in the 5,000 by a good margin with a time of 13:22.4, a new American record.  And Bowerman had finally convinced Pre to wear Nikes.

At that time, Olympic athletes couldn’t receive endorsement money.  So Pre sometimes tended bar and occasionally ran in Europe in exchange for illicit cash from promoters.

Knight decided to hire Pre, partly to keep him from injuring himself by racing too much.  Pre’s title was National Director of Public Affairs.  People often asked Knight what that meant.  Knight would say, “It means he can run fast.”  Pre wore Nikes everywhere and he preached Nike as gospel, says Knight.

Around this time, Knight realized that Johnson was becoming an excellent designer.  The East Coast was running smoothly, but needed reorganization.  So Knight asked Johnson to switch places with Woodell.  Woodell excelled at operations and thus would be a great fit for the East Coast situation.

Although Johnson and Woodell irritated one another, they both denied it.  When Knight asked them to switch places, the two exchanged house keys without the slightest complaint.

In the spring of 1973, Knight held his second meeting with the debenture holders.  He had to tell them that despite $3.2 million in sales, the company had lost $57,000.  The reaction was negative.  Knight tried to explain that sales continue to explode higher.  But the investors were not happy.

Knight left the meeting thinking he would never, ever take the company public.  He didn’t want to deal with that much negativity and rejection ever again.

Onitsuka filed suit against Blue Ribbon in Japan.  So Blue Ribbon had to file against them in the United States.

Knight asked his Cousin Houser to be in charge of the case.  Houser was a fine lawyer who carried himself with confidence.

Better yet, he was a tenacious competitor.  When we were kids Cousin Houser and I used to play vicious, marathon games of badminton in his backyard.  One summer we played exactly 116 games.  Why 116?  Because Cousin Houser beat me 115 straight times.  I refused to quit until I’d won.  And he had no trouble understanding my position.

More importantly, Cousin Houser was able to talk his firm into taking the Blue Ribbon case on contingency.

Knight continued his evening conversations with this father, who believed strongly in Blue Ribbon’s cause.  Knight’s father, who had been trained as a lawyer, spent time studying law books.  He reassured Buck, “we” are going to win.  This support from his father boosted Buck’s spirits at a challenging time.

(Law library, Photo by Spiroview Inc.)

Cousin Houser told Knight one day that he was bringing on a new member of the team, a young lawyer from UC Berkeley School of Law, Rob Strasser.  Not only was Strasser brilliant.  He also believed in the rightness of Blue Ribbon’s case, viewing it as a “holy crusade.”

Strasser was a fellow Oregonian who felt looked down on by folks north and south.  Moreover, he felt like an outcast.  Knight could relate.  Strasser often downplayed his intelligence for fear of alienating people.  Knight could relate to that, too.

Intelligence like Strasser’s, however, couldn’t be hidden for long.  He was one of the greatest thinkers I ever met.  Debator, negotiator, talker, seeker – his mind was always whirring, trying to understand.

When he wasn’t preparing for the trial, Knight was exclusively focused on sales.  It was essential that they sell out every pair of shoes in each order.  The company was still growing fast and cash was always short.

Whenever there was a delay, Woodell always knew what the problem was and could quickly let Knight know.  Knight on Woodell:

He had a superb talent for underplaying the bad, and underplaying the good, for simply being in the moment… throughout the day a steady rain of pigeon poop would fall on Woodell’s hair, shoulders, desktop.  But Woodell would simply dust himself off, casually clear his desk with the side of his hand, and continue with his work.

…I tried often to emulate Woodell’s Zen monk demeanor.  Most days, however, it was beyond me…

Blue Ribbon couldn’t meet demand.  This frustrated Knight.  Supplies were arriving on time.  But in 1973, it seemed that the whole world, all at once, wanted running shoes.  And there were never enough.  This made things precarious, to say the least, for Blue Ribbon:

…We were leveraged to the hilt, and like most people who live from paycheck to paycheck, we were walking the edge of a precipice.  When a shipment of shoes was late, our pair count plummeted.  When our pair count plummeted, we weren’t able to generate enough revenue to repay Nissho and the Bank of California on time.  When we couldn’t repay Nissho and the Bank of California on time, we couldn’t borrow more.  When we couldn’t borrow more we were late placing our next order.

Sales for 1973 hit $4.8 million, up 50 percent from the previous year.  But Blue Ribbon was still on fragile ground, it seemed.  Knight then thought of asking their retailers to sign up for large and unrefundable orders, six months in advance, in exchange for hefty discounts, up to 7 percent.  Such long-term commitments from well-established retailers like Nordstrom, Kinney, Athlete’s Foot, United Sporting Goods, and others, could then be used to get more credit from Nissho and the Bank of California.

Much later, after much protesting, the retailers signed on to the long-term commitments.

 

1974

The trial.  Federal courthouse in downtown Portland.  Wayne Hilliard was the lead lawyer for the opposition.  He was fiery and eloquent.  Cousin Houser was the lead for Blue Ribbon.  He’d convinced his firm to take the Blue Ribbon case on contingency.  But instead of a few months, it was now two years later.  Houser hadn’t seen a dime and costs were huge.  Moreover, Houser told Knight that his fellow law partners sometimes put a great deal of pressure on Houser to drop the Blue Ribbon case.

(Federal courthouse in Portland, Oregon, Wikimedia Commons)

Houser stuck with the case.  He wasn’t fiery.  But he was prepared and dedicated.  Knight was initially disappointed, but later came to admire him.  “Fire or no, Cousin Houser was a true hero.”

After being questioned by both sides, Knight felt he hadn’t done well at all, a D minus.  Houser and Strasser didn’t disagree.

The judge in the case was the Honorable James Burns.  He called himself James the Just.  Johnson made the mistake of discussing the trial with a store manager after James the Just had expressly forbidden all discussion of the case outside the courtroom.  James the Just was upset.  Knight:

Johnson redeemed himself with his testimony.  Articulate, dazzlingly anal about the tiniest details, he described the Boston and the Cortez better than anyone else in the world could, including me.  Hilliard tried and tried to break him, and couldn’t.

Later on, the testimony of Iwano, the young assistant who’d been with Kitami, was heard.  Iwano testified that Kitami had a fixed plan already in place to break the contract with Blue Ribbon.  Kitami had openly discussed this plan on many occasions, said Iwano.

Bowerman’s testimony was so-so because, out of disdain, he hadn’t prepared.  Woodell, for his part, was nervous.

Mr. Onitsuka said he hadn’t known anything about the conflict between Kitami and Knight.  Kitami, in his testimony, lied again and again.  He said that he had no plan to break the contract with Blue Ribbon.  He also claimed that meeting with other distributors had just been market research.  As well, the idea of acquiring Blue Ribbon “was initiated by Phil Knight.”

James the Just was convinced that Blue Ribbon had been more truthful.  In particular, Iwano seemed truthful, while Kitami didn’t.  On the issue of trademarks, Blue Ribbon would retain all rights to the Boston and the Cortez.

A bit later, Hilliard offered $400,000.  Finally, Blue Ribbon accepted.  Knight was happy for Cousin Houser, who would get half.  It was the largest payment in the history of his firm.

Knight, with help from Hayes, convinced Strasser to come work for Blue Ribbon.  Strasser later accepted.

Japanese labor costs were rising.  The yen was fluctuating.  Knight decided Blue Ribbon needed to find factories outside of Japan.  He looked at Taiwan, but shoe factories there weren’t quite ready.  He looked next at Puerto Rico.

Then Knight went to the east coast to look for possible factories.  The first factory owner laughed in Knight’s face.

The next empty factory Knight visited – with Johnson – the owner was willing to lease the third floor to Blue Ribbon.  He suggested a local guy to manage the factory, Bill Giampietro.  Giampietro turned out to be “a true shoe dog,” said Knight.  All he’d ever done is make shoes, like his father.  Perfect.  Could he get the old Exeter factory up and running?  How much would it cost?  No problem.  About $250,000.  Deal.

Knight asked Johnson to run the new factory.  Johnson said, “…what do I know about running a factory?  I’d be in completely over my head.”

Knight couldn’t stop laughing:  “Over your head?  Over your head!  We’re all in over our heads!  Way over!”

Knight writes that, at Blue Ribbon, it wasn’t that they thought they couldn’t fail.  On the contrary, they thought they would fail.  But they believed they would fail fast, learn from it, and be better for it.

Finishing up 1974, the company was on track for $8 million in sales.  Their contact at Bank of California, Perry Holland, kept telling them to slow down.  So they sped up, as usual.

 

1975

Knight kept telling Hayes, “Pay Nissho first.”  Blue Ribbon had a line of credit at the bank for $1 million.  They had a second million from Nissho.  That was absolutely essential.

…Grow or die, that’s what I believed, no matter the situation.  Why cut your order from $3 million down to $2 million if you believed in your bones that demand out there was for $5 million?  So I was forever pushing my conservative bankers to the brink, forcing them into a game of chicken.  I’d order a number of shoes that seemed to them absurd, a number we’d need to stretch to pay for, and I’d always just barely pay for them, in the nick of time, and then just barely pay our other monthly bills, at the last minute, always doing just enough, and no more, to prevent the bankers from booting us.  And then, at the end of the month, I’d empty our accounts to pay Nissho and start from zero again.

Demand was always greater than sales, so Knight concluded his approach was reasonable.  There was a new manager at Nissho’s Portland office, Tadayuki Ito, in place of Sumeragi.  (Sumeragi still helped with the Blue Ribbon account, though.)

One day in the spring of 1975, Blue Ribbon was $75,000 short of the $1 million they owed Nissho.  Blue Ribbon would have to completely drain every other account to make up for the shortfall.  Retail stores.  Johnson’s Exeter factory.  All of them.

(Illustration by Lkeskinen0)

In Exeter, a mob of angry workers was at Johnson’s door.  Giampetro drove with Johnson to see an old friend who owned a box company that depended on Blue Ribbon.  Giampetro asked for a loan of $5,000 (more than $25,000 in 2018), which was outrageous.  The man counted out fifty crisp hundred-dollar bills, says Knight.

Then Holland called Knight and Hayes to a meeting at the Bank of California.  The bank would no longer do business with Blue Ribbon.

Knight was worried how Ito and Sumeragi, representing Nissho, would react.  Ito and Sumeragi, after hearing what happened, said they would need to look at Blue Ribbon’s books.

On the weekend, Knight called a company-wide meeting to discuss the situation.  The Exeter factory had been a secret kept from Nissho.  But everyone agreed to give Nissho all information.

During this meeting, two creditors – owed $500,000 and $100,000 – called and were livid.  They were on their way to Oregon to collect and cash out.

On Monday, Ito and Sumeragi arrived at Blue Ribbon’s office.  Without a word, they went through the lobby to the conference room, sat down with the books and got to work.  Then Ito came to information related to the Exeter factory.  He did a slow double-take and then looked up at Knight.  Knight nodded.  Ito smiled.  Knight:

I gave him a weak half smile in return, and in that brief wordless exchange countless fates and futures were decided.

It turned out that Sumeragi had been trying to help Blue Ribbon by hiding Nissho’s invoices in a drawer.  Blue Ribbon had been stressing out trying to pay Nissho on time, but they’d never paid them on time because Sumeragi thought he was helping, writes Knight.

Ito accused Sumeragi of working for Blue Ribbon.  Sumeragi swore on his life that he’d acted independently.  Ito asked why.  Sumeragi answered that he thought Blue Ribbon would be a great success, perhaps a $20 million account.  Ito eventually forgave Blue Ribbon.  “There are worse things than ambition,” he said.

Ito accompanied Knight and Hayes to a meeting with the Bank of California.  Only this time, Ito – whom Knight saw as a “mythic samurai, wielding a jeweled sword” – was on their side.

(Samurai, Photo by Esolex)

According to Knight, Ito opened the meeting and “went all in.”  After confirming that Bank of California no longer wanted to handle Blue Ribbon’s account, Ito said Nissho wanted to pay off Blue Ribbon’s outstanding debt.  He asked for the number and it was the same number he’d learned earlier.  Ito already had a check made out for the amount and slid an envelope with the check across the table.  Ito insisted the check be deposited immediately.

After the meeting, Knight and Hayes bowed to Ito.  Ito remarked:

‘Such stupidity… I do not like such stupidity.  People pay too much attention to numbers.’

***

Blue Ribbon still needed a bank.  They started calling.  “The first six hung up on us,” recalls Knight.  First State Bank of Oregon didn’t hang up.  They offered one million in credit.

Pre died in a tragic car accident at the age of twenty-four.  At the time of his death, he held every American record from 2,000 to 10,000 meters, from two miles to six miles.  People created a shrine where Pre had died.  They left flowers, letters, notes, gifts.  Knight, Johnson, and Woodell decided that Blue Ribbon would curate Pre’s rock, making it a holy site forever.

 

1976

Knight had several meetings early in 1976 with Woodell, Hayes, and Strasser about the company’s cash situation.  Nissho was lending Blue Ribbon millions, but to keep up with demand, they needed millions more.  The most logical solution was to go public.  But Knight and the others felt that it just wasn’t who they were.  No way.

They found other ways to raise money, including a million-dollar loan guaranteed by the U.S. Small Business Administration.

Meanwhile, Bowerman’s waffle trainer was getting even more popular.

(Nike 1976 waffle trainer)

With its unique outer sole, and its pillowy midsole cushion, and its below-market price ($24.95), the waffle trainer was continuing to capture the popular imagination like no previous shoe.  It didn’t just feel different, or fit different – it looked different.  Radically so.  Bright red upper, fat white swoosh – it was a revolution in aesthetics.  Its look was drawing hundreds of thousands of new customers into the Nike fold, and its performance was sealing their loyalty.  It had better traction and cushioning than anything on the market.

Watching that shoe evolve in 1976 from popular accessory to cultural artifact, I had a thought.  People might start wearing this thing to class.

And the office.

And the grocery store.

And throughout their everyday lives.

It was a rather grandiose idea… So I ordered the factories to start making the waffle trainer in blue, which would go better with jeans, and that’s when it really took off.

We couldn’t make enough.  Retailers and sales reps were on their knees, pleading for all the waffle trainers we could ship.  The soaring pair counts were transforming our company, not to mention the industry.  We were seeing numbers that redefined our long-term goals, because they gave us something we’d always lacked – an identify.  More than a brand, Nike was now becoming a household word, to such an extent that we would have to change the company name.  Blue Ribbon, we decided, had run its course.  We would have to incorporate as Nike, Inc.

They needed to ramp up production.  Knight realized the time had come to visit Taiwan.  To help with the Taiwan effort, Knight turned to Jim Gorman.  Gorman had been raised in a series of foster homes.  Nike was the family he’d never had.

…In every instance, Gorman had done a fine job and never uttered a sour word.  He seemed the perfect candidate to take on the latest mission impossible – Taiwan.  But first I needed to give him a crash course on Asia.  So I scheduled a trip, just the two of us.

Gorman was full of questions for Knight and took notes on everything.  Knight enjoyed teaching Gorman, partly because Knight himself could learn what he knew even better through the process of teaching.

Taiwan had a hundred smaller factories, whereas South Korea had a few larger ones.  That’s why Nike needed to go to Taiwan at this juncture.  Demand for Nikes was exploding, but their volume was still too low for a giant shoe factory.  However, Knight knew it would be a challenge to get a shoe factory in Taiwan to improve its quality enough to be able to produce Nikes.

During the visit to various Taiwan shoe factories, Jerry Hsieh introduced himself to Knight and Gorman.  Hsieh was a genuine shoe dog, but quite young, twenty-something.  When Knight and Gorman found their way to Hsieh’s office – a room stuffed with shoes everywhere – Hsieh started sharing his deep knowledge of shoes.  Also, Hsieh told them he knew the very best shoe factories in Taiwan and for a small fee, would be happy to introduce them.  They agreed on a commission per pair.

The 1976 Olympic trials, again in Eugene.  In the 10,000 meter race, all top three finishers wore Nikes.  Some top finishers in other qualifying races also wore Nikes.  Meanwhile, Penny created a great number of Nike T-shirts.  People would see other people wearing the Nike T-shirts and want to buy one.  The Nike employees heard people whispering.  “Nike.”  “Nike.”  “Nike.”

At the close of 1976, Nike had doubled its sales to $14 million.  The company still had no cash, though.  Its bank accounts were often at zero.

The company’s biannual retreat was taking place.  People called it Buttface.

Johnson coined the phrase, we think.  At one of our earliest retreats he muttered:  “How many multi-million dollar companies can you yell out, ‘Hey, Buttface,’ and the entire management team turns around?”  It got a laugh.  And then it stuck.  And then it became a key part of our vernacular.  Buttface referred to both the retreat and the retreaters, and it not only captured the informal mood of those retreats, where no idea was too sacred to be mocked, and no person was too important to be ridiculed, it also summed up the company spirit, mission and ethos.

Knight continues:

…The problems confronting us were grave, complex, insurmountable… And yet we were always laughing.  Sometimes, after a really cathartic guffaw, I’d look around the table and feel overcome by emotion.  Camaraderie, loyalty, gratitude.  Even love.  Surely love.  But I also remember feeling shocked that these were the men I’d assembled.  These were the founding fathers of a multi-million dollar company that sold athletic shoes?  A paralyzed guy, two morbidly obese guys, a chain-smoking guy?  It was bracing to realize that, in this group, the one with whom I had the most in common was… Johnson.  And yet, it was undeniable.  While everyone else was laughing, rioting, he’d be the sane one, sitting quietly in the middle of the table reading a book.

A bit later, Knight writes:

Undoubtedly we looked, to any casual observer, like a sorry, motley crew, hopelessly mismatched.  But in fact we were more alike than different, and that gave a coherence to our goals and our efforts.  We were mostly Oregon guys, which was important.  We had an inborn need to prove ourselves, to show the world that we weren’t hicks and hayseeds.  And we were nearly all merciless self-loathers, which kept the egos in check.  There was none of that smartest-guy-in-the-room foolishness.  Hayes, Strasser, Woodell, Johnson, each would have been the smartest guy in any room, but none believed of himself, or the next guy.  Our meetings were defined by contempt, disdain, and heaps of abuse.

(Photo by Chris Dorney)

Knight records:

…Each of us had been misunderstood, misjudged, dismissed.  Shunned by bosses, spurned by luck, rejected by society, short-changed by fate when looks and other natural graces were handed out.  We’d each been forged by early failure.  We’d each given ourselves to some quest, some attempt at validation or meaning, and fallen short.

I identified with the born loser in each Buttface, and vice versa, and I knew that together we could become winners…

Knight’s management style continued to be very hands-off, following Patton’s leadership belief:

Don’t tell people how to do things, tell them what to do and let them surprise you with their results.

Nike’s culture seemed to be working thus far.  Since Bork, no one had gotten really upset, not even what they were paid, which is unusual, notes Knight.  Knight created a culture he himself would have wanted:  let people be, let people do, let people make their own mistakes.

 

1977

M. Frank Rudy, a former aerospace engineer, and his business partner, Bob Bogert, presented to Nike the idea of putting air in the soles of shoes.  Great cushioning, great support, a wonderful ride.  Knight tried wearing a pair Rudy showed him on a six-mile run.  Unstable, but one great ride.

Strasser, who by this point had become Nike’s negotiator, offered Rudy 10 cents for every pair we sold.  Rudy asked for twenty.  They settled somewhere in the middle.  Knight sent Rudy and his partner back to Exeter, which “was becoming our de facto Research and Development Department.”

Knight calls this time “an odd moment,” saying furthermore that “a second strange shoe dog showed up on our door step.  His name was Sonny Vaccaro…”.  Vaccaro had founded the Dapper Dan Classic, a high school all-star game that had become very popular.  Though it, Vaccaro had gotten to know many coaches.  Knight hired Vaccaro and sent him, with Strasser, to sign up college basketball coaches.  Knight expected them to fail.  But they succeeded.

Knight knew he had to meet again with Chuck Robinson, who’d served with distinction as a lieutenant commander on a battle ship in World War II.  Chuck knew business better than anyone Knight had ever met.  Recently, he’d been the number two guy under Henry Kissinger, so he wasn’t available for meetings.  Now Chuck was free.

Chuck took a look at Nike’s financials and couldn’t stop laughing, saying, “Compositionally, you are a Japanese trading company – 90 percent debt!”

Chuck told Buck, “You can’t live like this.”  The solution was to go public in order to raise a large amount of cash.  Knight invited Chuck to join the board.  Chuck agreed, to Knight’s surprise.

When Knight put the question of going public to a company vote, however, the consensus was still to remain private.

Then they received a letter from the U.S. Customs Service containing a bill for $25 million.  Nike’s competitors, Converse and Keds – plus a few small factories – were behind it.  They had been lobbying in Washington, DC, trying to slow Nike by enforcing the American Selling Price, an old law dating back to protectionist days.

(Photo by Ian Wilson)

ASP – American Selling Price – said that import duties on nylon shoes should be 20 percent of the shoe’s manufacturing cost.  Unless there was a “similar shoe” made by a U.S. competitor.  Then it should be 20 percent of that shoe’s selling price.  Nike’s competitors just needed to make some shoes deemed “similar,” price them very high, and voila – high import duties for Nike.

They’d managed to pull the trick off, raising Nike’s import duties retroactively by 40 percent.

Near the end of 1977, Nike’s sales were approaching $70 million.

 

1978

Knight calls Strasser the “five-star general” in the battle with the U.S. government.  But they knew they needed “a few good men.”  Strasser suggested a friend of his, a young Portland lawyer, Richard Werschkul.  Stanford undergrad, University of Oregon law.  A sharp guy with a presence.  And an eccentric streak.  Some worried he was too serious and obsessive.  But that seemed good to Knight.  And Knight trusted Strasser.  Werschkul was dispatched to Washington, DC.

Meanwhile, sales were on track for $140 million.  Furthermore, Nike shoes were finally recognized as higher quality than Adidas shoes.  Knight thought Nike had led in quality and innovation for years.

Nike had to start selling clothes, announced Knight at Buttface in 1978.  First, Adidas sold more apparel than shoes.  Second, it would be easier to get athletes into endorsement deals.

Knight decided to hire a young accountant, Bob Nelson, and put him in charge of the new line of Nike apparel.  But Nelson had no sense of style, unfortunately.  When he presented his ideas, they didn’t look good.  Knight decided to transfer him to an accounting position, where he would excel.  Knight writes:

…Then I quietly shifted Woodell to apparel.  He did his typically flawless job, assembling a line that gained immediate attention and respect in the industry.  I asked myself why I didn’t just let Woodell do everything.

Tailwind – a new Nike shoe with air – came out in late 1978.  Then Nike had to recall it due to a design flaw.  Knight concluded they’d learned a valuable lesson.  “Don’t put twelve innovations into one shoe.”

Around this time, many seemed to be suffering from burnout, including Knight.  And back in DC, Werschkul was becoming hyper obsessive.  He’d tried to talk with everyone possible.  They all told him to put something in writing so they could study it.

Werschkul spent months writing.  It became hundreds of pages.  “Without a shred of irony Werschkul called it:  Werschkul on American Selling Price, Volume I.”  Knight:

When you thought about it, when you really thought about it, what really scared you was that Volume I.

Knight sent Strasser to calm Werschkul down.  Knight realized that he himself would have to go to DC.  “Maybe the cure for any burnout… is just to work harder.”

 

1979

Senators Mark O. Hatfield and Bob Packwood helped Nike deal with the $25 million bill from U.S. Customs.  Knight started the process of looking for a factory in China.

 

1980

Chuck Robinson suggested to Knight that Nike could go public but have two classes of stock, class A and class B.  Nike insiders would own class A shares, which would allow them to name three-quarters of the board of directors.  The Washington Post Company and a few other companies had done this.

Knight explained the idea – going public with two classes of stock – to colleagues at Nike.  All agreed that it was time to go public to raise badly needed cash.

In China, Knight – with Strasser, Hayes, and others – signed a deal with China’s Ministry of Sports.  Four years later, at the Olympics in Los Angeles, the Chinese track-and-field team entered the stadium wearing Nike shoes and warm-ups.  Before leaving China, Nike signed a deal with two Chinese factories.

Knight then muses about “business”:

It seems wrong to call it “business.”  It seems wrong to throw all those hectic days and sleepless nights, all those magnificent triumphs and desperate struggles, under that bland, generic banner:  business.  What we were doing felt like so much more.  Each new day brought fifty new problems, fifty tough decisions that needed to be made, right now, and we were always acutely aware that one rash move, one wrong decision could be the end.  The margin for error was forever getting narrower, while the stakes were forever creeping higher – and none of us wavered in the belief that “stakes” didn’t mean “money.”  For some, I realize, business is the all-out pursuit of profits, period, full stop, but for us business was no more about making money than being human is about making blood.  Yes, the human body needs blood.  It needs to manufacture red and white cells and platelets and redistribute them evenly, smoothly, to all the right places, on time, or else.  But that day-to-day mission of the human body isn’t our mission as human beings.  It’s a basic process that enables our higher aims, and life always strives to transcend the basic processes of living – and at some point in the late 1970s, I did, too.  I redefined winning, expanded it beyond my original definition of not losing, of merely staying alive.  That was no longer enough to sustain me, or my company.  We wanted, as all great businesses do, to create, to contribute, and we dared to say so aloud.  When you make something, when you improve something, when you add to some new thing or service to the lives of strangers, making them happier, or healthier, or safer, or better, and when you do it all crisply and efficiently, smartly, the way everything should be done but so seldom is – you’re participating more fully in the whole grand human drama.  More than simply alive, you’re helping others to live more fully, and if that’s business, all right, call me a businessman.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

 

 

Quantitative Deep Value Investing

July 17, 2022

Virtually all of the historical evidence shows that quantitative deep value investing—systematically buying stocks at low multiples (low P/B, P/E, P/S, P/CF, and EV/EBITDA)—does better than the market over time.

Deep value investing means investing in ugly stocks that are doing terribly—with low- or no-growth—and that are trading at low multiples.  Quantitative deep value investing means that the portfolio of deep value stocks is systematically constructed based solely on quantitative factors including cheapness.  (It’s a process that can easily be automated.)

One of the best papers on quantitative deep value investing is by Josef Lakonishok, Andrei Shleifer, and Robert Vishny (1994), “Contrarian Investment, Extrapolation, and Risk.”  Link: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf

Buffett has called deep value investing the cigar butt approach:

…You walk down the street and you look around for a cigar butt someplace.  Finally you see one and it is soggy and kind of repulsive, but there is one puff left in it.  So you pick it up and the puff is free—it is a cigar butt stock.  You get one free puff on it and then you throw it away and try another one.  It is not elegant.  But it works.  Those are low return businesses.

(Photo by Sensay)

Outline for this blog post:

  • Rare Temperament
  • Early Buffett: Deep Value Investor
  • Investors Much Prefer Income Over Assets
  • Companies at Cyclical Lows

 

RARE TEMPERAMENT

Many value investors prefer to invest in higher-quality companies rather than deep value stocks.  A high-quality company has a sustainable competitive advantage that allows it to earn a high ROIC (return on invested capital) for a long time.  When you invest in such a company, you can simply hold the position for years as it compounds intrinsic value.  Assuming you’ve done your homework and gotten the initial buy decision right, you typically don’t have to worry much.

Investing in cigar butts (deep value stocks), however, means that you’re investing in many mediocre or bad businesses.  These are companies that have terrible recent performance.  Some of these businesses won’t survive over the longer term, although even the non-survivors often survive many years longer than is commonly supposed.

Deep value investing can work quite well, but it takes a certain temperament not to care about various forms of suffering—such as being isolated and looking foolish.  As Bryan Jacoboski puts it:

The very reason price and value diverge in predictable and exploitable ways is because people are emotional beings.  That’s why the distinguishing attribute among successful investors is temperament rather than brainpower, experience, or classroom training.  They have the ability to be rational when others are not.

(Photo by Nikki Zalewski)

In The Manual of Ideas (Wiley, 2013), John Mihaljevic explains the difficulty of deep value investing:

It turns out that Graham-style investing may be appropriate for a relatively small subset of the investment community, as it requires an unusual willingness to stand alone, persevere, and look foolish.

On more than one occasion, we have heard investors respond as follows to a deep value investment thesis: ‘The stock does look deeply undervalued, but I just can’t get comfortable with it.’  When pressed on the reasons for passing, many investors point to the uncertainty of the situation, the likelihood of negative news flow, or simply a bad gut feeling.  Most investors also find it less rewarding to communicate to their clients that they own a company that has been in the news for the wrong reasons.

Comfort can be expensive in investing.  Put differently, acceptance of discomfort can be rewarding, as equities that cause their owners discomfort frequently trade at exceptionally low valuations.

Many investors will look at a list of statistically cheap stocks and conclude that most of them would be awful investments.  But in practice, a basket of deep value stocks tends to outperform, given enough time.  And typically some of the big winners include stocks that looked the worst prior to being included in the portfolio.

 

EARLY BUFFETT: DEEP VALUE INVESTOR

Warren Buffett started out as a cigar-butt investor.  That was the method he learned from his teacher and mentor, Ben Graham, the father of value investing.  When Buffett ran his partnership, he generated exceptional performance using a deep value strategy focused on microcap stocks: https://boolefund.com/buffetts-best-microcap-cigar-butts/

(Early Buffett teaching at the University of Nebraska, via Wikimedia Commons)

One reason Buffett transitioned from deep value to buying high-quality companies (and holding them forever) was simply that the assets he was managing at Berkshire Hathaway became much too large for deep value.  But in his personal account, Buffett recently bought a basket of South Korean cigar butts and ended up doing very well.

Buffett has made it clear that if your assets under management are relatively small, then deep value investing—especially when focused on microcap stocks—can do better than investing in high-quality companies.  Buffett has said he could make 50% a year by investing in deep value microcap stocks: https://boolefund.com/buffetts-best-microcap-cigar-butts/

In the microcap world, since most professional investors don’t look there, if you turn over enough rocks you can find some exceptionally cheap companies.  If you don’t have sufficient time and interest to find the most attractive individual microcap stocks, using a quantitative approach is an excellent alternative.  A good quantitative value fund focused on microcaps is likely to do much better than the S&P 500 over time.  That’s the mission of the Boole Fund.

 

INVESTORS MUCH PREFER INCOME OVER ASSETS

Outside of markets, people naturally assess the value of possessions or private businesses in terms of net asset value—which typically corresponds with what a buyer would pay.  But in markets, when the current income of an asset-rich company is abnormally low, most investors fixate on the low income even when the best estimate of the company’s value is net asset value.  (Mihaljevic makes this point.)

If an investor is considering a franchise (high-quality) business like Coca-Cola or Johnson & Johnson, then it makes sense to focus on income, since most of the asset value involves intangible assets (brand value, etc).

But for many potential investments, net asset value is more important than current income.  Most investors ignore this fact and stay fixated on current income.  This is a major reason why stock prices occasionally fall far below net asset value, which creates opportunities for deep value investors.

(Illustration by Teguh Jati Prasetyo)

Over a long period of time, the income of most businesses does relate to net asset value.  Bruce Greenwald, in his book Value Investing (Wiley, 2004), explains the connection.  For most businesses, the best way to estimate intrinsic value is to estimate the reproduction cost of the assets.  And for most businesses—because of competition—earnings power over time will not be more than what is justified by the reproduction cost of the assets.

Only franchise businesses like Coca-Cola—with a sustainable competitive advantage that allows it to earn more than its cost of capital—are going to have normalized earnings that are higher than is justified by the reproduction cost of the assets.

Because most investors view cigar butts as unattractive investments—despite the overwhelming statistical evidence—there are always opportunities for deep value investors.  For instance, when cyclical businesses are at the bottom of the cycle, and current earnings are far below earnings power, investors’ fixation on current earnings can create very cheap stocks.

A key issue is whether the current low income reflects a permanently damaged business or a temporary—or cyclical—decline in profitability.

 

COMPANIES AT CYCLICAL LOWS

Although you can make money by buying cheap businesses that are permanently declining, you can usually make more money by buying stocks at cyclical lows.

(Illustration by Prairat Fhunta)

Mihaljevic:

Assuming a low enough entry price, money can be made in both cheap businesses condemned to permanent fundamental decline and businesses that may benefit from mean reversion as their industry moves through the cycle.  We much prefer companies that find themselves at a cyclical low, as they may restore much, if not all, of their earning power, providing multi-bagger upside potential.  Meanwhile, businesses likely to keep declining for a long time have to be extremely cheap and keep returning cash to shareholders to generate a positive investment outcome.

The question of whether a company has entered permanent decline is anything but easy to answer, as virtually all companies appear to be in permanent decline when they hit a rock-bottom market quotation.  Even if a business has been cyclical in the past, analysts generally adopt a ‘this time is different’ attitude.  As a pessimistic stock price inevitably influences the appraisal objectivity of most investors, it becomes exceedingly difficult to form a view strongly opposed to the prevailing consensus.

If you can stay calm and rational while being isolated and looking foolish, then you can buy deeply out of favor cyclical stocks, which often have multi-bagger upside potential.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

Capitalism without Capital

July 10, 2022

Capitalism without Capital: The Rise of the Intangible Economy, by Jonathan Haskel and Stian Westlake, is an excellent book that everyone should read.

Historically most assets were tangible rather than intangible.  Houses, castles, temples, churches, farms, farm animals, equipment, horses, weapons, jewels, precious metals, art, etc.  These types of tangible assets tended to hold their value, and naturally they were included on accountants’ balance sheets.

(Photo by W. Scott McGill)

Intangible assets are different.  It’s harder to account for investing in intangibles.  But intangible investment is important.  Haskel and Westlake explain why:

Investment is what builds up capital, which, together with labor, constitutes the two measured inputs to production that power the economy, the sinews and joints that make the economy work.  Gross domestic product is defined as the sum of the value of consumption, investment, government spending, and net exports; of these four, investment is often the driver of booms and recessions, as it tends to rise and fall in response to monetary policy and business confidence.

The problem is that national statistical offices have, until very recently, measured only tangible investments.

The Dark Matter of Investment

In 2002 in Washington, at a meeting of the Conference on Research in Income and Wealth, economists considered investments people made in the “new economy.”  Carol Corrado and Dan Sichel of the US Federal Reserve Board and Charles Hulten of the University of Maryland developed a framework for thinking about different types of investments.

Haskel and Westlake mention Microsoft as an example.  In 2006, Microsoft’s market value was about $250 billion.  There was $70 billion in assets, $60 billion of which was cash and cash equivalents.  Plant and equipment totaled only $3 billion, 4 percent of Microsoft’s assets and 1 percent of its market value.  In a sense, Microsoft is a miracle:  capitalism without capital.

(Photo by tashatuvango)

Charles Hulten sought to explain Microsoft’s value by using intangible assets:

Examples include the ideas generated by Microsoft’s investments in R&D and product design, the value of its brands, its supply chains and internal structures, and the human capital built up by training.

Such intangible assets are similar to tangible assets in that the company had to spend time and money on them up-front, while the value to the company was delivered over time.

Why Intangible Investment is Different

Businesses change what they invest in all the time, so how is intangible investment different?  Haskel and Westlake:

Our central argument in this book is that there is something fundamentally different about intangible investment, and that understanding the steady move to intangible investment helps us understand some of the key issues facing us today:  innovation and growth, inequality, the role of management, and financial and policy reform.

We shall argue there are two big differences with intangible assets.  First, most measurement conventions ignore them.  There are some good reasons for this, but as intangibles have become more important, it means we are now trying to measure capitalism without counting all the capital.  Second, the basic economic properties of intangibles make an intangible-rich economy behave differently from a tangible-rich one.

Outline for this blog post:

Part I  The Rise of the Intangible Economy

  • Capital’s Vanishing Act:  The Rise of Intangible Investment
  • How to Measure Intangible Investment
  • What’s Different About Intangible Investment?  The Four S’s of Intangibles

Part II  The Consequences of the Rise of the Intangible Economy

  • Intangibles, Investment, Productivity, and Secular Stagnation
  • Intangibles and the Rise of Inequality
  • Infrastructure for Intangibles, and Intangible Infrastructure
  • The Challenge of Financing an Intangible Economy
  • Competing, Managing, and Investing in the Intangible Economy
  • Public Policy in an Intangible Economy:  Five Hard Questions

 

Part I  The Rise of the Intangible Economy

CAPITAL’S VANISHING ACT

Investment has changed:

The type of investment that has risen inexorably is intangible: investment in ideas, in knowledge, in aesthetic content, in software, in brands, in networks and relationships.

Investment, assets, and capital all have multiple meanings.

For investment, Haskel and Westlake stick with the internationally agreed upon definition as given by the UN’s System of National Accounts:

Investment is what happens when a producer either acquires a fixed asset or spends resources (money, effort, raw materials) to improve it.

An asset is an economic resource that is expected to provide a benefit over a period of time.  A fixed asset is an asset that results from using up resources in the process of its production.

Spending resources:  To be an investment, the business doing the investing has to acquire the asset or pay some cost to produce it themselves.

Haskel and Westlake offer some examples of intangible investments:

Suppose a solar panel manufacturer researches and discovers a cheaper process for making photovoltaic cells:  it is incurring expense in the present to generate knowledge it expects to benefit from in the future.  Or consider a streaming music start-up that spends months designing and negotiating deals with record labels to allow it to use songs the record labels own—again, short-term expenditure to create longer-term gain.  Or imagine a training company pays for the long-term rights to run a popular psychometric test:  it too is investing.

(Photo by magele-picture)

Intangible investing results in intangible assets.  More examples of intangible investments:

  • Software
  • Databases
  • R&D
  • Mineral exploration
  • Creating entertainment, literary or artistic originals
  • Design
  • Training
  • Market research and branding
  • Business process re-engineering

Intangible Investment Has Steadily Grown

Supermarkets have developed complex pricing systems, more ambitious branding and marketing campaigns, and more detailed processes and systems (including better use of bar codes).  Moreover, as you might expect, tech firms make heavy use of intangible investments, as Haskel and Westlake explain:

Fast-growing tech companies are some of the most intangible-intensive of firms.  This is in part because software and data are intangibles, and the growing power of computers and telecommunications is increasing the scope of things that software can achieve.  But the process of “software eating the world,” in venture capitalist Marc Andreesen’s words, is not just about software:  it involves other intangibles in abundance.  Consider Apple’s designs and its unrivaled supply chain, which has helped it to bring elegant products to market quickly and in sufficient numbers to meet customer demand, or the networks of drivers and hosts that sharing-economy giants like Uber and AirBnB have developed, or Tesla’s manufacturing know-how.  Computers and the Internet are important drivers of this change in investment, but the change is long running and predates not only the World Wide Web but even the Internet and the PC.

By the mid-1990s, intangible investment in the United States exceeded tangible investment.  There is a similar pattern for the UK, Sweden, and Finland.  But tangible investment is still greater than intangible investment in Spain, Italy, Germany, Austria, Denmark, and the Netherlands.

Reasons for the Growth of Intangible Investment

Because the productivity of the manufacturing sector typically increases faster than that of the services sector, labor-intensive services gradually become more expensive compared to manufactured goods.  (This is called Baumol’s Cost Disease.)  This implies that intangible investing will grow faster than tangible investing over time.

Furthermore, new technology seems to create greater opportunities for businesses to invest productively in intangibles.  Haskel and Westlake give Uber as an example.  It would have been possible before computers and smartphones for Uber to develop its large network of drivers.  But smartphones—which connect people quickly, allow the rating of drivers, and make payment quick and easy—significantly boosted the return on investment for Uber.

It’s natural to wonder if computers are the cause of increased intangible investment.  Haskel and Westlake suggest that while computers may be a primary cause, they do not seem to be the only cause:

First of all, as we saw earlier, the rise of intangible investment began before the semiconductor revolution, in the 1940s and 1950s and perhaps before.  Second, while some intangibles like software and data strongly rely on computers, others do not:  brands, organizational development, and training, for example.  Finally, a number of writers in the innovation studies literature argue that it may be that it was the rise of intangibles that led to the development of modern IT as much as the other way around.

 

HOW TO MEASURE INTANGIBLE INVESTMENT

Productivity growth in the United States starting in the mid-1970s and throughout the 1980s seemed quite low.  Economists found this puzzling because computers seemed to be making a difference in a variety of areas.  Statistical agencies, led by the US Bureau of Economic Analysis (BEA), made two adjustments:

First, in the 1980s, in conjunction with IBM, the BEA started to produce indexes of computer prices that were quality adjusted.  This turned out to make a very big difference to measuring how much investment businesses were making in computer hardware.

In most cases—for products, for example—prices for the same good tend to rise gently in line with overall inflation.  But even if sticker prices for computers were rising, they were decidedly not the same good, since every dimension of their quality (speed, memory, and space) was improving incredibly.  So their “quality-adjusted” prices were, in fact, falling and falling very fast, meaning that the quality you could buy per dollar spent on computers was in fact rising very fast.

In the 1990s, statisticians looked at business spending that creates computer software.  Haskel and Westlake comment that banks are huge spenders on the creation of software (at one point, Citibank employed more programmers than Microsoft).  Software is an intangible good—knowledge written down in lines of code.

(Photo by Krisana Antharith)

By the early 2000s, many business economists realized that knowledge more generally is an intangible investment that should be included in GDP and productivity measures.  Gradually statistical offices began to incorporate various intangible investments into GDP statistics.  Haskel and Westlake:

And these changes added up.  In the United States, for example, the capitalization of software added about 1.1 percent to 1999 US GDP and R&D added 2.5 percent to 2012 GDP, with these numbers growing all the time…

What Sorts of Intangibles Are There?

Corrado, Hulten, and Sichel divided intangible investment into three broad types:

  • Computerized Information:  Software development;  Database development.
  • Innovative Property:  R&D;  Mineral exploration;  Creating entertainment and artistic originals;  Design and other product development costs.
  • Economic Competencies:  Training;  Market research and branding;  Business process re-engineering.

Right now, design and other product development costs are not included in official GDP measures.  Also not included:  training, market research and branding, and business process re-engineering.

Measuring Investment in Intangibles

Haskel and Westlake:

Measuring investment requires a number of steps.  First, we need to find out how much firms are spending on the intangible.  Second, in some cases, not all of that spending will be creating a long-lived asset… So we may have to adjust that spending to measure investment—that is, that part of spending creating a long-lived asset.  Third, we need to adjust that investment for inflation and quality change so we can compare investment in different periods when prices and quality are changing.

For most investment goods, national accountants simply send out a survey to companies asking them how much there are spending on each good.  It’s trickier, however, if it’s an intangible good that the company makes for itself, like writing its own software or doing its own R&D.  In this case, statisticians can figure out how much it costs a company—over and above wages—to produce the intangible good.  Statisticians also must estimate how much of that additional spending is an investment that will last for more than a year.  The third step is to adjust for inflation and quality changes.

To measure the intangible asset created by intangible investment, economists have to estimate depreciation.  Once you know the flow of intangible investment and you adjust for depreciation, you can then estimate the stock—the value of intangible assets in a given year.  For software, design, marketing, and training, depreciation is about 33 percent a year.  For R&D, depreciation is roughly 15 percent a year.  For entertainment and artistic originals and mineral exploration, depreciation is lower.

 

WHAT’S DIFFERENT ABOUT INTANGIBLE INVESTMENT?

An intangible-rich economy has four characteristics—the four S’s—that distinguish it from a tangible-rich economy.  Intangible assets:

  • Are more likely to be scalable;
  • Their costs are more likely to be sunk;
  • They are inclined to have spillovers;
  • They tend to exhibit synergies with each other.

Scalability

Why Are Intangibles Scalable?

Scalability derives from what economists call “non-rivalry” goods.  A rival good is like a loaf of bread.  Once one person eats the loaf of bread, no one else can eat that loaf.  In contrast, a non-rival good is not used up when one person uses it.  For instance, once a software program has been created, it can be reproduced an infinite number of times at almost no cost.  There’s virtually no limit to how many people can make use of that one software program.  Another example, given by Paul Romer—a pioneer of how economists think about economic growth—is oral rehydration therapy (ORT).  ORT is a simple treatment that has saved many lives in the developing world by stopping children’s deaths from diarrhea.  The idea of ORT can be used again and again—it’s never used up.

Note:  Scalability can really take off if there are “network effects.”  Haskel and Westlake mention networks like Uber drivers or Instagram users as examples.

(Illustration by Aquir)

Why Does Scalability Matter?

Haskel and Westlake say that we will see three unusual things happening in an economy where more investments are clearly scalable:

  • There will be some highly intangible-intensive businesses that have gotten very large.  Google, Microsoft, and Facebook are good examples.  Their software can be reproduced countless times at almost no cost.
  • Given the prospects of such large markets, ever more firms feel incentivized to go for it.
  • Businesses who compete with owners of scalable assets are in a tough position.  In markets with hugely scalable assets, the rewards for runners-up are often meager.

Sunkenness

Why Are Intangibles Sunk Costs?

Intangible assets are much harder to sell than tangible assets.  If an intangible investment works, creating value for the company that made the investment, then there’s no issue.  However, if an intangible investment doesn’t work or the company wants to back out, it’s often hard to sell.  Specifically, if knowledge isn’t protected by intellectual property rights, it’s often impossible to sell.

(Image by OpturaDesign)

Why Does Sunkenness Matter?

Because intangible investments frequently involve unrecoverable costs, they can be difficult to finance, especially with debt.  There’s a reason why many small business loans require a lien on directors’ houses:  a house is a tangible asset with ascertainable value.

Moreover, people tend to fall for the sunk-cost fallacy, whereby they overvalue an intangible asset that hasn’t worked out because of the time, energy, and resources they’ve poured into it.  People are inclined to continue putting in more time and resources.  This may contribute to bubbles.

Spillovers

Why Do Intangibles Generate Spillovers?

Intangible investments can be used relatively easily by companies that didn’t make the investments.  Consider R&D.  Unless it is protected by patents, knowledge gained through R&D can be re-used again and again.  Haskel and Westlake remark:

Patents and copyrights are, on the whole, less secure and more subject to challenge than the title deeds to farmland or the ownership of a shipping container or a computer.

One reason is that property rights related to tangible assets have been around for thousands of years.

Why Do Spillovers Matter?

(Photo by Vs1489)

Haskel and Westlake remark that spillovers matter for three reasons:

  • First, in a world where companies can’t be sure they will obtain the benefits of their investments, we would expect them to invest less.
  • Second, there is a premium on the ability to manage spillovers:  companies that can make the most of their own investments in intangibles, or that are especially good at exploiting the spillovers from others’ investments, will do particularly well.
  • Third, spillovers affect the geography of modern economies.

The U.S. government funds 30 percent of the R&D that happens in the country.  It’s the classic answer to the issue of companies being unsure about the benefits of intangible investments they’re considering.  Public R&D is particularly important for basic research.

Haskel and Westlake:

Patent trolls and copyright lawsuits catch our attention because they are newsworthy, but other ways of capturing the spillovers of intangible investment are common—in fact, they’re part of the invisible fabric of everyday business life.  They often involve reciprocity rather than compulsion or legal threats.  Software developers use online repositories like GitHub to share code; being an active contributor and an effective user of GitHub is a badge of honor for some developers.  Firms sometimes pool their patents; they realize that the spillovers from each company’s technologies are valuable, and that enforcing everyone’s individual legal rights is not worth it.  (Indeed, the US government helped end the patent war between the Wright Brothers and Curtiss Aeroplane and Motor Company that was holding back the US aircraft industry in the 1910s by getting everyone to set up a patent pool, the Manufacturers Aircraft Association.)

Synergies

Why Do Intangibles Exhibit Synergies?

Haskel and Westlake give the example of the microwave.  Near the end of World War II, Raytheon was mass-producing cavity magnetrons (similar to a vacuum tube), a crucial part of the radar defenses the British had invented.  A Raytheon engineer, Percy Spenser, realized the microwaves from magnetrons could heat food by creating electromagnetic fields in a box.

Haskel and Westlake write:

A few companies tried to sell domestic microwave ovens, but none were very successful.  Then, in the 1960s, Raytheon bought Amana, a white goods manufacturer, and combined their microwave expertise with Amana’s kitchen appliance knowledge to build a more successful product.  At the same time, Litton, another defense contractor, invented the modern microwave oven shape and tweaked the magnetron to make it safer.

In 1970 forty thousand microwaves were sold.  By 1975 it was a million.  What made this possible was the gradual accumulation of ideas and innovations.  The magnetron on its own wasn’t very useful to a customer, but combined with other incremental bits of R&D and the design and marketing ideas of Litton and Amana, it became a defining innovation of the late twentieth century.

The point of the microwave story is that intangible assets have synergies with one another.  Also, it’s hard to predict where innovations will come from or how they will combine.  In this example, military technology led to a kitchen appliance.

(Synergies in digital business, science, and technology:  Illustration by Agsandrew)

Intangible assets have synergies with tangible assets as well.  In the 1990s, productivity increased and at first people didn’t know why.  Haskel and Westlake explain:

In 2000 the McKinsey Global Institute analyzed the sources of this productivity increase.  Counterintuitively, they found that the bulk of it came from the way big chains retailers, in particular Walmart, were using computers and software to reorganize their supply chains, improve efficiency, and lower prices.  In a sense, it was a technological revolution.  But the gains were realized through organizational and business practice changes in a low-tech sector.  Or, to put it another way, there were big synergies between Walmart’s investment in computers and its investment in processes and supply chain development to make the most of the computers.

Why Do the Synergies of Intangible Assets Matter?

While spillovers cause firms to be protective of their intangible investments, synergies have the opposite effect and lead to open innovation.

In its simplest form, open innovation happens when a firm deliberately connects with and benefits from new ideas that arise outside the firm itself.  Cooking up ideas in a big corporate R&D lab is not open innovation; getting ideas by buying start-ups, partnering with academic researchers, or undertaking joint ventures with other companies is.

(Illustration by mindscanner)

Besides open innovation, there’s a second reason why synergies matter:

They also matter because they create an alternative way for firms to protect their intangible investments against competition:  by building synergistic clusters of intangible investments, rather than by protecting individual assets.

 

Part II  The Consequences of the Rise of the Intangible Economy

INTANGIBLES, INVESTMENT, PRODUCTIVITY, AND SECULAR STAGNATION

Two characteristics of secular stagnation are low investment and low interest rates.  Investment fell in the 1970s, recovered some in the mid-1980s, but fell sharply in the financial crisis (2008) and hasn’t recovered.

What’s puzzling is that investment hasn’t recovered despite low interest rates.  In the past, central banks relied on lowering rates to spur investment activity.  But that seems not to have worked this time.

(Illustration by ibreakstock)

One possible explanation is that technological progress has slowed.  Robert Gordon makes this argument in The Rise and Fall of American Growth (2016).  But technological progress is quite difficult to measure.

There are three more aspects to secular stagnation.

  • Corporate profits in the United States are higher than they’ve been for decades, and they seem to keep increasing.  Return on invested capital (ROIC) has grown significantly since the 1990s.
  • When it comes to both profitability and productivity, there is a growing gap between leaders and laggards.
  • Productivity growth has slowed due mostly to a decline in total factor productivity—workers are working less effectively with the capital they have.

Haskel and Westlake note that a good explanation for secular stagnation should explain four facts:

  • A fall in measured investment at the same time as a fall in interest rates
  • Strong profits
  • Increasingly unequal productivity and profits
  • Weak total factor productivity growth

Intangibles can help explain these facts.

Mismeasurement:  Intangibles and Apparently Low Investment

Intangible investment exceeds tangible investment in countries including the United States and the UK.  Are economies growing faster than reported because the value of intangibles is not being properly measured?  Haskel and Westlake show that including intangibles does not noticeably change investment/GDP.

Profits and Productivity Differences:  Scale, Spillovers, and the Incentives to Invest

Haskel and Westlake state:

…leading firms, which are confident of their ability to create scalable assets and to appropriate most of their benefits, will continue to invest (and enjoy a high rate of return on those investments); but laggard firms, expecting low private returns from their investments, will not.  In a world where there are a few leaders and many laggards, the net effect of this could be lower aggregate rates of investment, combined with high returns on those investments that do get made.

Spillovers:  Intangibles and Slowing TFP Growth A Lower Pace of Intangible Growth?

The slowdown in intangible investment since the financial crisis does seem to account for slowing TFP (Total Factor Productivity) growth, although the data are noisy and more exploration is needed.

Are Intangibles Generating Fewer Spillovers?

Lagging firms may be less able to absorb spillovers from leaders, possibly because leading firms can gain from synergies between different intangibles to a much greater extent than laggards.

 

INTANGIBLES AND THE RISE OF INEQUALITY

In addition to inequality of income and inequality of wealth, there is also what Haskel and Westlake call “inequality of esteem.”  Some communities feel left-behind and overlooked by America’s prosperous coastal cities.

Standard explanations for inequality

One standard explanation for inequality is that new technologies replace workers, which causes wages to fall and profits to rise.

A second explanation relates to trade.  In the 1980s, before the collapse of the Soviet Union and before market reforms in China and India, the global economy had 1.46 billion workers.  Then in the 1990s, the number of workers doubled to 2.93 billion workers.  This puts pressure on lower-skilled workers in developed economies.  The flip side is that lower-skilled workers in China and India end up far better off than they were before.

A third explanation for inequality is that capital tends to accumulate.  Capital tends to grow faster than the economy—this is Thomas Piketty’s famous r > g inequality—which causes capital to build up over time.

(Illustration by manakil)

How Intangibles Affect Income, Wealth, and Esteem Inequality

Intangibles, Firms, and Income Inequality

The best firms—owning scalable intangibles and able to extract spillovers from other businesses—will be highly productive and profitable while their competitors will lose out.  But that doesn’t necessarily mean the best firms pays all their workers more.  To explain rising wage inequality, more is needed.

Who is Benefiting from Intangible-Based Firm Inequality?

“Superstars” benefit by being associated with exceptionally valuable intangibles that can scale massively.  Whereas in most markets a top worker could probably be replaced by two not-as-fast workers, this isn’t true for superstar markets:  you can’t replace the best opera singer or the best basketball player with two not-quite-as-good ones.  Tech billionaires also tend to be superstars with large equity stakes in companies they founded—companies that probably scaled massively.

However, senior managers have also done very well.  Haskel and Westlake explain why:

Intangible investment increases.  Because of its scalability and the benefits to companies that can appropriate intangible spillovers, leading companies pull ahead of laggards in terms of productivity, especially in the more intangible-intensive industries.  The employees of these highly productive companies benefit from higher wages.  Because intangibles are contestable, companies are especially eager to hire people who are good at contesting them—appropriating spillovers from other firms or identifying and maximizing synergies.

Why are CEOs at many companies being paid so much more than other workers?  One reason relates to a “fundamental attribution error” whereby people explain a good business outcome by referring to what is simple and salient—like the skill of the CEO—rather than by acknowledging complexity and the fact that luck typically plays a major role.  It’s also possible, say Haskel and Westlake, that shareholders—especially those who are most diversified—are not paying much attention to CEO pay.

Housing Prices, Cities, Intangibles, and Wealth Inequality

Intangibles can help explain wealth inequality.  First, intangibles tend to drive up property prices.  Second, the mobility of intangible capital means it’s harder to tax.

In a world where intangibles are becoming more abundant and a more important part of the way businesses create value, the benefits to exploiting spillovers and synergies increase.  And as these benefits increase, we would expect businesses and their employees to want to locate in diverse, growing cities where synergies and spillovers abound.

Haskel and Westlake summarize how intangibles impact long-run inequality:

  • First, inequality of income.  The synergies and spillovers that intangibles create increase inequality between competing companies, and this inequality leads to increasing differences in employee pay… In addition, managing intangibles requires particular skills and education, and people with these skills are clustering in high-paid jobs in intangible-intensive firms.  Finally, the growing economic importance of the kind of people who manage intangibles helps foster myths that can be used to justify excessive pay, especially for top managers.
  • Second, inequality of wealth.  Thriving cities are places where spillovers and synergies abound.  The rise of intangibles makes cities increasingly attractive places to be, driving up the prices of prime property.  This type of inflation has been shown to be one of the major causes of the increase in the wealth of the richest.  In addition, intangibles are often mobile; they can be shifted across firms and borders.  This makes capital more mobile, which makes it harder to tax.  Since capital is disproportionately owned by the rich, this makes redistributive taxation to reduce wealth inequality harder.
  • Finally, inequality of esteem.  There is some evidence that supporters of populist movements… are more likely to hold traditional views and to score low on tests for the psychological trait of openness to experience.

 

INFRASTRUCTURE FOR INTANGIBLES, AND INTANGIBLE INFRASTRUCTURE

On the one hand, in order to thrive, the intangible economy needs new buildings in and around cities.  On the other hand, artistic and creative institutions are important for combinatorial innovation.  In the longer term, face-to-face interaction may eventually be phased out, but often these kinds of changes can take much longer than initially supposed.

(Illustration by Panimoni)

Haskel and Westlake comment:

The death of distance has failed to take place.  Indeed, the importance of spillovers and synergies has increased the importance of places where people come together to share ideas and the importance of the transport and social spaces that make cities work.

But the death of distance may have been postponed rather than cancelled.  Information technologies are slowly, gradually, replacing some aspects of face-to-face interaction.  This may be a slow-motion change, like the electrification of factories—if so, the importance of physical infrastructure will radically change.

Soft infrastructure will also matter increasingly.  The synergies between intangibles increase the importance of standards and norms, which together make up a kind of social infrastructure for intangible investment.  And standards and norms are underpinned by trust and social capital, which are particularly important in an intangible economy.

 

THE CHALLENGE OF FINANCING AN INTANGIBLE ECONOMY

Banks are often criticized for not providing enough capital for businesses to succeed.  Equity markets are criticized for being too short-term and also too influential.  Managers seem to fixate more and more on shorter term stock prices.  Managers may cut R&D to try to please short-term investors.  Haskel and Westlake remark:

These concerns drive public policy across the developed world:  most governments to some extent subsidize or coerce banks to lend to businesses, and they give tax advantages to companies that finance using debt.  Many countries are considering measures to make equity investors take a longer-term perspective, such as imposing taxes on short-term shareholdings or changing financial reporting requirements.  And most governments have spent money trying to encourage alternative forms of financing, particularly venture capital (VC), which is regarded as providing a big potential source of business growth and national wealth.

Banking:  The Problem of Lending in a World of Intangibles

When a bank lends money to a business, the bank usually has some recourse to the assets of the business if the debt isn’t repaid.  However, intangible assets are typically much harder to value than tangible assets, and frequently intangible assets don’t have much value at all when a business fails.  Thus it is difficult for a bank to lend to a business whose assets are mostly intangible.

This is why industries with mostly tangible assets—like oil and gas producers—have high leverage (are funded more with debt than equity), while industries with mostly intangible assets—like software—have less debt and more equity.

One way to increase bank lending to businesses with more intangible assets is for the government to cofund or guarantee bank loans.  A second way is financial innovation, such as finding ways to value intangible assets—like patents—more accurately.  A third way to deal with the issue of lending against intangibles is to get businesses to rely more on equity than debt.

Haskel and Westlake on how equity markets impact intangible investing:

There is some evidence that markets are short-termist, to the extent that management can sometimes boost their company’s share price by cutting intangible investment to preserve or increase profits, or cut investment to buy back stock.  But it also seems that some of what is happening is a sharpening of managerial incentives:  publicly held companies whose managers own stock focus on types of intangible investment that are more likely to be successful.  And the extent of market myopia varies:  companies with more concentrated, sophisticated investors are less likely to feel pressure to cut intangible investment than those with dispersed, unsophisticated ones.

Why VC Works for Intangibles

(Photo by designer491)

Haskel and Westlake observe:

VC has several characteristics that make it especially well-suited to intangible-intensive businesses:  VC firms take equity stakes, not debt, because intangible-rich businesses are unlikely to be worth much if they fail—all those sunk investments.  Similarly, to satisfy their own investors, VC funds rely on home-run successes, made possible by the scalability of assets like Google’s algorithms, Uber’s driver network, or Genentech’s patents.  Third, VC is often sequential, with rounds of funding proceeding in stages.  This is a response to the inherent uncertainty of intangible investment.

Leading VC firms and their partners are well-connected and credible, which helps in building networks to exploit synergies.

 

COMPETING, MANAGING, AND INVESTING IN THE INTANGIBLE ECONOMY

Businesses look to improve their performance in a way that is sustainable.  How can this be done?  The advice has always been to build and maintain distinctive assets.  Tangible assets are usually not distinctive, or at least not for long.  Haskel and Westlake:

It’s much more likely that the types of intangible assets we have talked about in this book are going to be distinctive:  reputation, product design, trained employees providing customer service.  Indeed, perhaps the most distinctive asset will be the ability to weave all these assets together; so a particularly valuable intangible asset will be the organization itself.

When it comes to management, Haskel and Westlake suggest replacing the question, “What are managers for?” with a deeper question, “What’s the role of authority in an economy?”

Markets work with minimal government interference.  However, firms can do a better job than dispersed individuals at organizing certain activities.  Managers are people at firms who have authority.  This is usually more efficient:  managers tell employees what to do rather than discussing or arguing about every step.

But if management is largely just monitoring, and software can do the job of monitoring, then what is the role of managers in an intangible-intensive economy?  For one, note Haskel and Westlake, the stakes tend to be much higher in the intangible economy.  Moreover, in synergistic firms, only managers may understand the big picture.

How can managers build a good organization in an intangible-intensive firm?  Haskel and Westlake explain:

…if you are primarily a producer of intangible assets (writing software, doing design, producing research) you probably want to build an organization that allows information to flow, helps serendipitous interactions, and keeps the key talent.  That probably means allowing more autonomy, fewer targets, and more access to the boss, even if that is at the cost of influence activities.

Leadership is important in an intangible economy.

(Photo by Raywoo)

Having voluntary followers is really useful in an intangible economy.  A follower will stay loyal to the firm, which keeps the tacit intangible capital at the firm.  Better, if they are inspired by and empathize with the leader, they will cooperate with each other and feed information up to the leader.  This is why leadership is going to be so valued in an intangible economy.  It can at best replace, and likely mitigate, the costly and possibly distortive aspects of managing by authority.

Investing

How can an investor discern if a business is building intangible assets?  Can investors learn about intangibles from accounting data?

Accountants try to match revenues with costs.  If the company has a long-lived asset that produces revenues, then the company measures the annual cost by depreciation or amortization of that asset.

The other way to measure the cost of a long-lived asset is to expense the entire cost of creating the asset in the year in which the expenditures are made.  However, this can lead to distortions.  First, the costs in creating the asset can make profits in that year appear unusually low.  By the same logic, if the asset in question continues producing revenues, then in future years profits will appear unusually high.

In the case of intangible assets, if the asset is bought from outside the company, then it is capitalized (and annual expenses are calculated based on depreciation or amortization).  If the asset is created within the company, then the costs are recognized when they are spent (even if the asset is long-lived).

The result is that much intangible investment is hidden because it is expensed.  This is a challenge for investors because economies are coming to rely increasingly on intangible assets.  Book value—which is frequently based largely on tangible assets—is less relevant for a company that relies on intangible assets—especially if the company develops those assets internally.

What Should Investors Do?

The simplest solution for investors is to invest in low-cost broad market index funds.  In this way, the investor will benefit from companies that rely on intangible assets.

Because index funds outpace 90-95% of all active investors if you measure performance over several decades, it already makes excellent sense for many investors to invest in index funds.

Haskel and Westlake sum up the chapter:

The growth of intangible investment has significant implications for managers, but it will affect different firms in different ways.  Firms that produce intangible assets will want to maximize synergies, create opportunities to learn from the ideas of others (and appropriate the spillovers from others’ intangibles), and retain talent.  These workplaces may end up looking rather like the popular image of hip knowledge-based companies.  But companies that rely on exploiting existing intangible assets may look very different, especially where the intangible assets are organizational structure and processes.  These may be much more controlled environments—Amazon’s warehouses rather than its headquarters.  Leadership will be increasingly prized, to the extent that it allows firms to coordinate intangible investments in different areas and exploit their synergies.

Financial investors who can understand the complexity of intangible-rich firms will also do well.  The greater uncertainty of intangible assets and the decreasing usefulness of company accounts put a premium on good equity research and on insight into firm management.

 

PUBLIC POLICY IN AN INTANGIBLE ECONOMY:  FIVE HARD QUESTIONS

Haskel and Westlake highlight five of the most important challenges in an intangible-rich economy:

  • First, intangibles tend to be contested:  it is hard to prove who owns them, and even then their benefits have a tendency to spill over to others.  Good intellectual property frameworks are important for an economy increasingly dependent on intangibles.
  • Second, in an intangible economy, synergies are very important. Combining different ideas and intangible assets is central to successful business innovation.  An important objective for policy makers is to create conditions for ideas to come together.
  • The third challenge relates to finance and investment.  Businesses and financial markets seem to underinvest in scalable, sunk intangible investments with a tendency to generate spillovers and synergies.  The current system of business finance exacerbates the problem.  A thriving intangible economy will significantly improve its financial system to make it easier for companies to invest in intangibles.
  • Fourth, it will probably be harder for most businesses to appropriate the benefits of capital investment in the economies of the future than in the tangible-rich economies we are familiar with.  Successful intangible-rich economies will have higher levels of public investment in intangibles.
  • Fifth, governments must work out how to deal with the dilemma of the particular type of inequality that intangibles seem to encourage.
(Illustration by Robert Wilson)

Clearer Rules and Norms about the Ownership of Intangibles

Stronger IP rights are not necessarily best because while they can increase incentive to invest, productivity gains are lowered.  Also, strengthening IP rights might accidentally favor incumbent rights-holders and patent trolls.

Clearer IP rights can be helpful, though.  They can reduce lawsuits that often end up in the notoriously troll-friendly Eastern District of Texas court.

Moreover, since intangible assets are often much more difficult to value than tangible assets, there are ways to help with this.  For instance, Ian Hargreaves in 2011 suggested that the UK have a Digital Copyright Exchange.  Another example is patent pools where firms coinvest in research and agree to share the resulting rights.

Helping Ideas Combine:  Maximizing the Benefits of Synergies

Good public policy should be just as assiduous about creating the conditions for knowledge to spread, mingle, and fructify as it is about creating property rights for those who invest in intangibles.

It should be easy to build new workplaces and homes in cities.  But simultaneously, cities have to be connected and livable.

A Financial Architecture for Intangible Investment

Governments should encourage new forms of debt that facilitate the ability to borrow against intangible assets.  Longer term, governments should help a shift from debt to equity financing.  Currently, debt is cheaper than equity due to the tax benefits of debt.  This must change, but it will be very difficult because vested interests still rely on debt.  Furthermore, new institutions will be required that provide equity financing to small and medium-size businesses.  Although these shifts will be challenging, the rewards will be ever greater, note Haskel and Westlake.

Solving the Intangible Investment Gap

Some large firms seem able to gain from both their own intangible investments and from intangible investments made by others.  These companies—like Google or Facebook—can be expected to continue making intangible investments.

Outside of these companies, the government and other public interest bodies (like large non-profit foundations) must make intangible investments.

The government is the investor of last resort.  Here are three practical tips given by Haskel and Westlake for government investment in intangibles:

  • Public R&D Funding.  This means the government spending more on university research, public research institutes, or research undertaken by businesses.  This type of government spending is not at all ideologically controversial and it can help a great deal over time.
  • Public Procurement.  When the US military funded the development of the semiconductor industry in the 1950s, they also acted as a lead customer.  This helped Texas Instruments and other firms not just to invest in R&D, but also to build the capacity to produce and sell chips.
  • Training and Education. Because it’s hard to predict what skills will be needed in 20 to 30 years, adult education may be a good area in which to invest.  This could also help with inequality to some extent.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

Warren Buffett’s Ground Rules

July 3, 2022

Warren Buffett’s Ground Rules: Words of Wisdom from the Partnership Letters of the World’s Greatest Investor, is an excellent book by Jeremy C. Miller.  Miller did the book with no input at all from Buffett.  But Buffett has commented quite favorably on the result:

Mr. Miller has done a superb job of researching and dissecting the operation of Buffett Partnership Ltd., and of explaining how Berkshire’s culture has evolved from its BPL origin.  If you are fascinated by investment theory and practice, you will enjoy this book.

Miller has arranged each chapter around a single theme.  Here is a brief summary of these chapters:

  • Orientation—The Principles of Ben Graham
  • Compounding
  • Measuring Up
  • The Partnership—An Elegant Structure
  • The Generals
  • Workouts
  • Controls
  • Dempster Diving—The Asset Conversion Play
  • Conservative versus Conventional
  • Size versus Performance
  • Go-Go or No-Go
  • Toward a Higher Form

(Buffett teaching at the University of Nebraska, via Wikimedia Commons)

 

ORIENTATION—The Principles of Ben Graham

At the beginning of the Buffett Partnership Ltd. (BPL), the small amount of capital Buffett was investing—$100,100—meant that, in a sense, his opportunities were similar to that of any small individual investor.  No companies were too small or obscure to be potential investment opportunities.

Ben Graham, the father of value investing, was Buffett’s teacher and mentor.  Buffett learned several key principles from Graham that are still true today and that still inform Buffett’s investing:

  • Margin of Safety
  • Market Prices
  • Owning Stock is Owning Part of a Business
  • Forecasting

Margin of Safety

Margin of safety means that if you think a stock is worth $20 a share, then you try to buy it at $10 (or lower).  You try to buy well below your estimate of the intrinsic value of the business.

No investor is always right.  Good value investors tend to be right about 60% of the time and wrong 40% of the time.  Sometimes an investor makes a mistake.  Other times an investor gets unlucky.  Luck does play a role, and the future is always unpredictable to an extent.

A margin of safety is meant to help limit losses in those cases where you make a mistake or are unlucky.

Market Prices

Market prices in the shorter term often deviate from intrinsic value.  The intrinsic value of any business is the total cash that can be taken out of the business over time, discounted back to the present.  (For some businesses, liquidation value is the best estimate of intrinsic value.)   Figuring out the intrinsic value of a given business requires careful analysis, which should be done without any input from stock price fluctuations.  Graham notes that many investors make the mistake of thinking that random stock price movements actually represent something fundamental, but they rarely do.

(Illustration by Prairat Fhunta)

It is only over a long period of time that a stock price will approximate the intrinsic value of a business based on the actual business results.  Over shorter periods of time, stock prices can be completely irrational, deviating significantly from the intrinsic value of a given business.

According to Graham, the wise, long-term value investor will buy if the price get irrationally low and will sell if the price gets irrationally high.  Most of the time, however, he will simply ignore the random daily gyrations of stock prices.  Summarizing Graham’s lesson, Buffett wrote:

[A] market quote’s availability should never be turned into a liability whereby its periodic aberrations in turn formulate your judgments.

It is only over a period of roughly 3 to 5 years—at a minimum—that the stock price of an individual business can be expected to track intrinsic value.

Owning Stock is Owning Part of a Business

A share of stock is a fractional ownership claim on the entire business.  Thus, if you can value the business—whether based on liquidation value, net asset value, or discounted cash flows—then you can value the stock.

(Illustration by Teguh Jati Prasetyo)

As Miller explains, a company’s shares over the lifetime of a business will necessarily produce a return equal to the returns produced by that business.  Any investor can enjoy the returns of a given business as long as they do not pay too high a price for the stock.

Value investors focus on valuing businesses, and they do not worry about unpredictable shorter term stock prices.  Buffett again:

We don’t buy and sell stocks based upon what other people think the stock market is going to do (I never have an opinion) but rather upon what we think the company is going to do.  The course of the stock market will determine, to a great degree, when we will be right, but the accuracy of our analysis of the company will largely determine whether we will be right.  In other words, we tend to concentrate on what should happen, not when it should happen.

Buffett stresses these lessons repeatedly.  As Miller writes, stocks are not pieces of paper to trade back and forth.  Stocks are claims on a business, and some of those businesses can be valued.  We cannot predict when a stock price will approximate intrinsic value, but we know that it will in the long run.  The market eventually gets it right.  The proper focus for an investor is finding the right businesses at the right prices, without worrying about when an investment will work.

Forecasting

Buffett learned from Graham that macro variables simply cannot be predicted.  It’s just too hard to forecast the stock market, interest rates, commodity prices, GDP, etc.  Regarding the annual values of macro variables, Buffett was (and still is) extremely consistent in his opinion:

I don’t have the first clue.

All of Buffett’s experience over the past 65+ years has convinced him even more that such variables simply can’t be predicted from year to year with any sort of reliability.  As Buffett wrote in 2014:

Anything can happen anytime in markets.  And no advisor, economist, or TV commentator—and definitely not Charlie nor I—can tell you when chaos will occur.  Market forecasters will fill your ear but will never fill your wallet.

Link: http://berkshirehathaway.com/letters/2014ltr.pdf

(Illustration by Maxim Popov)

Ben Graham:

If I have noticed anything over these sixty years on Wall Street, it is that people do not succeed in forecasting what’s going to happen to the stock market.

Seth Klarman:

In reality, no one knows what the market will do; trying to predict it is a waste of time and investing based upon that prediction is a speculative undertaking.

Finally, Buffett again:

Charlie and I never have an opinion on the market because it wouldn’t be any good and it might interfere with the opinions we have that are good.

The unpredictability of the stock market from year to year (along with other macro variables) is an extremely important lesson for investors.  History is full of examples of highly intelligent people making these types of predictions, and being wrong.  Miller notes:

Through Buffett’s insights, we learn not to fall victim to the siren songs of these ‘expert’ opinions and churn our portfolios, jumping from guesstimate to guesstimate and allowing what could otherwise be a decent result to be consumed by taxes, commissions, and random chance.

Buffett himself is a good example of how unpredictable the stock market is.  For most of the years when Buffett ran BPL—from the mid-1950’s until 1969—he often commented that he thought stocks were overvalued.  But as a value investor, Buffett focused nearly all his time on finding individual stocks that were undervalued.  He kept writing that the stock market would decline, even though he didn’t know when.  It turned out to take almost a decade from Buffett’s initial warning before the stock market actually did decline.  Because he stayed focused on individual stocks, his track record was stellar.  Had Buffett ever stopped focusing on individual stocks because he was worried about a stock market decline, he would have missed many years of excellent results.

Miller remarks:

A good deal of Buffett’s astonishing success during the Partnership years and beyond has come from never pretending to know things that were either unknowable or unknown.

Miller concludes:

The good news is that the occasional market drop is of little consequence to long-term investors.  Preparing yourself to shrug off the next downturn is an important element of the method Buffett lays out.  While no one knows what the market is going to do from year to year, odds are we will have at least a few 20-30% drops over the next decade or two.  Exactly when these occur is of no great significance.  What matters is where you start and where you end up—shuffle around the order of the plus and minus years and you still come to the same ultimate result in the end.  Since the general trend is up, as long as a severe 25-40% drop isn’t going to somehow cause you to sell out at the low prices, you’re apt to do pretty well in stocks over the long run.  You can allow the market pops and drops to come and go, as they inevitably will.

For the vast majority of investors, it is literally true that they would get the best long-term results if, after buying some decent investments (value investments or index funds), they completely forgot about these holdings.  One study by Fidelity showed that the best performing of all their account holders literally forgot they had portfolios at all.

Graham explained this long ago (as quoted by Miller):

The true investor scarcely ever is forced to sell his shares, and at all other times he is free to disregard the current price quotation.  He need pay attention to it and act upon it only to the extent that it suits his book, and no more.  Thus the investor who permits himself to be stampeded or unduly worried by unjustified market declines in his holdings is perversely transforming his basic advantage into a basic disadvantage.  That man would be better off if his stocks had no market quotation at all, for he would then be spared the mental anguish caused him by other persons’ mistakes of judgment.

 

COMPOUNDING

If Buffett skipped a haircut for $10 in 1956 and invested it instead, that $10 would be worth more than $1 million today ($10 compounded at 22% for 60 years).  Being keenly aware of the power of compounding, Buffett has always been exceptionally frugal.

(Photo by Bjørn Hovdal)

Another example of the power of compounding is Ronald Read, a gas station attendant.  As Miller observes, Read ended up with $8 million by consistently investing a small portion of his salary into high-quality dividend-paying stocks.

In Buffett’s case, after becoming the world’s richest man during a few different years, he was able to make the largest private charitable donation in history—to the Gates Foundation, run by his friends Bill and Melinda Gates.  It’s also noteworthy, says Miller, that Buffett is (and has long been) one of the happiest people on earth because he gets to spend the majority of his time doing things he loves doing.

Stocks versus Bonds Today

Miller writes (in 2016):

Today, with bond yields not too far from zero, a 5-6% per annum result over the next 20 to 30 years seems like a reasonable assumption [for stocks].  If we get those kinds of results, the power of compound interest will be just as important, but it will take longer for the effects to gain momentum.

Small costs add up to a very large difference over time.  Probably no one explains this better than Jack Bogle.  See: https://boolefund.com/bogle-index-funds/

 

MEASURING UP

One of Buffett’s “Ground Rules” for BPL was Ground Rule #5:

While I much prefer a five-year test, I feel three years is an absolute minimum for judging performance.  It is a certainty that we will have years when the partnership performance is poorer, perhaps substantially so, than the Dow.  If any three-year or longer period produces poor results, we should start looking around for other places to have our money.  An exception to the latter statement would be three years covering a speculative explosion in a bull market.

Buffett also set very ambitious goals at the outset of BPL, including beating the Dow by an average margin of 10 percentage points per year.  Buffett explains how his value investing approach could achieve this target:

I would consider a year in which we declined 15% and the Average 30% to be much superior to a year when both we and the Average advanced 20%.  Over a period of time there are going to be good and bad years; there is nothing to be gained by getting enthused or depressed about the sequence in which they occur.  The important thing is to be beating par; a four on a par three hole is not as good as a five on a par five hole and it is unrealistic to assume we are not going to have our share of both par three’s and par five’s.

 

THE PARTNERSHIP: AN ELEGANT STRUCTURE

Incentives drive human conduct.  The vast majority of people underestimate just how important incentives are when trying to predict or explain human behavior.  As Charlie Munger has said:

I think I’ve been in the top 5% of my age cohort almost my entire adult life in understanding the power of incentives, and yet I’ve always underestimated that power.  Never a year passes but I get some surprise that pushes a little further my appreciation of the incentive superpower.

(Image by Ctitze)

Buffett figured that stocks would increase 5-7% per year on average.  He designed the fee structure of BPL with this in mind.  The chief fee structure was as follows:  there would be no flat fee based on assets under management, and there would be no fee on the first 6% increase in any given year.  There would be a fee of 25% of profits above the first 6% increase in any given year.

The 6% would compound from year to year.  Because Buffett’s explicitly stated goal was to beat the Dow by an average of 10% per year, his fee structure was designed accordingly.  Unlike most professional investors, Buffett didn’t charge any flat fee just for having assets under management.  Rather, his entire fee essentially came from beating the market—or beating a 6% increase compounded each year.  If Buffett did much better than the market, then he would be rewarded accordingly.  Yet if Buffett fell behind the market, then it could take some time before he earned any fees, since the 6% level compounded each and every year.

In a nutshell, the incentives were well-designed for Buffett to minimize the downside and maximize the upside.  Because Buffett understood Graham’s value investing approach to be set up in just this way—where minimizing the downside was a part of maximizing the upside—Buffett was incentivized to do value investing as well as he possibly could.

Compare Buffett’s fee structure in BPL to the fee structure of many of today’s hedge funds.  These days, many hedge funds charge “2 and 20,” or a 2% flat fee for assets under management and 20% of all profits.  There are, of course, some hedge funds that have outstanding track records.  Yet there are quite a few hedge funds where the performance, net of all fees, is not very different (and frequently worse) than the S&P 500 Index.  Whereas Buffett’s entire fee was based upon performance above a 6% compounded annual return, there are many hedge funds bringing in huge fees even though their net results are not much different from 6% per year.

In pursuing his investment goals, Buffett used three categories of investments:

  • The Generals
  • Workouts
  • Controls

Miller discusses each category in turn.

 

THE GENERALS

Miller begins by highlighting that there are many different approaches to value investing.  You can focus on very cheap stocks, regardless of business quality or fundamentals.  You can instead look for great, well-protected franchise businesses that can compound value over time.  You can focus on tiny, obscure microcap companies that are much too small for most professional investors even to consider.  Or you could find value in mid- or large-cap companies.  And within these categories, you could take a passive approach—like an index fund or a quantitative fund—or you could adopt an active approach of carefully picking each individual stock.

Miller says Buffett essentially used all of these different approaches at one time or another.  Miller:

For Buffett, the Generals were a highly secretive, highly concentrated portfolio of undervalued common stocks that produced the majority of the Partnership’s overall gains.

With one exception, Buffett never revealed the names of the companies in which he was investing.  These were trade secrets.

Using the Moody’s Manuals and other primary sources of statistical data, Buffett scoured the field to find stocks trading at rock-bottom valuations.  Often these were tiny, obscure, and off-the-radar companies trading below their liquidation value.  In the early years especially, the Partnership was small enough to be largely unconstrained, allowing for a go-anywhere, do-anything approach, similar to that of most individual investors today.

Even today, it’s remarkable how many tiny microcap companies are virtually unknown.  They’re simply too small for most professional investors even to consider.  Quite a few have no analyst coverage.

(Photo by Sean824)

Buffett was never concerned about when specific cheap stocks would finally rise toward their intrinsic values.  Buffett:

Sometimes these work out very fast; many times they take years.  It is difficult at the time of purchase to know any specific reason why they should appreciate in price.  However, because of this lack of glamour or anything pending which might create immediate favorable market action, they are available at very cheap prices.  A lot of value can be obtained for the price paid.

Among the Generals, Buffett had two subdivisions, as Miller explains.

“Generals – Private Owner” were undervalued based on what a private owner would pay—which itself is still based on discounted future cash flows or liquidation value.  But in some cases, these Generals became controlled investments in BPL, meaning Buffett bought enough stock to be able to influence management.

“Generals – Relatively Undervalued” were undervalued stocks that lacked any prospect for BPL or any other private owner to acquire control.  Without the possibility of an activist, these cheap stocks were riskier than “Generals – Private Owner.”

Earlier I mentioned discounted cash flows and liquidation value as two primary ways to value companies.  These two valuation methods can also be referred to as earnings power value and net asset value.  They are linked in that net asset value for a going concern is based on the earnings power of the assets.

Often, however, net asset value is better approximated by liquidation value rather than earnings power.  Buffett referred to these deep value opportunities as cigar butts.  Like a soggy cigar butt found on a street corner, a deep value investment would often give “one free puff.”  Such a cigar butt is disgusting, but that one puff is “all profit.”

One potential problem with Graham’s cigar-butt approach—buying well below liquidation value—is that if a company continues to lose money, then the liquidation value gradually gets eroded.

(Illustration by Preecha Israphiwat)

In these cases, if possible, Buffett would try to buy enough stock in order to influence management.  Thus, a General would become a Control.  Buffett also looked for situations where another investor would take control.  Buffett called this “coattail riding.”

Buffett wrote that deep value cigar butts were central to the great performance of the Buffett Partnership:

… over the years this has been our best category, measured by average return, and has also maintained by far the best percentage of profitable transactions.  This approach was the way I was taught the business, and it formerly accounted for a large proportion of all our investment ideas.  Our total individual profits in this category during the twelve-year BPL history are probably fifty times or more our total losses.

Yet over time, Buffett evolved from primarily a deep value, cigar-butt strategy to an approach focused on higher quality businesses.  Buffett explained the difference in his 1967 letter to partners:

The evaluation of securities and businesses for investment purposes has always involved a mixture of qualitative and quantitative factors.  At the one extreme, the analyst exclusively oriented to qualitative factors would say, ‘Buy the right company (with the right prospects, inherent industry conditions, management, etc.) and the price will take care of itself.’  On the other hand, the quantitative spokesman would say, ‘Buy at the right price and the company (and stock) will take care of itself.’  As is so often the pleasant result in the securities world, money can be made with either approach.  And, of course, any analyst combines the two to some extent—his classification in either school would depend on the relative weight he assigns to the various factors and not to his consideration of one group of factors to the exclusion of the other group.

Interestingly enough, although I consider myself to be primarily in the quantitative school… the really sensational ideas I have had over the years have been heavily weighted toward the qualitative side where I have had a ‘high-probability insight.’  This is what causes the cash register to really sing.  However, it is an infrequent occurrence, as insights usually are, and, of course, no insight is required on the quantitative side—the figures should hit you over the head with a baseball bat.  So the really big money tends to be made by investors who are right on the qualitative decisions, but, at least in my opinion, the more sure money tends to be made on the obvious quantitative decisions.

Much later, in his 2014 Berkshire Hathaway Letter to Shareholders, Buffett would explain his evolution from deep value investing to investing in higher quality companies that could be held for a long time.  See page 25: http://berkshirehathaway.com/letters/2014ltr.pdf

My cigar-butt strategy worked very well while I was managing small sums.  Indeed, the many dozens of free puffs I obtained in the 1950’s made the decade by far the best of my life for both relative and absolute performance…

But a major weakness in this approach gradually became apparent:  Cigar-butt investing was scalable only to a point.  With large sums, it would never work well.

In addition, though marginal businesses purchased at cheap prices may be attractive as short-term investments, they are the wrong foundation on which to build a large and enduring enterprise.

Miller quotes Charlie Munger:

… having started out as Grahamites—which, by the way, worked fine—we gradually got what I would call better insights.  And we realized that some company that was selling at two or three times book value could still be a hell of a bargain because of the momentum implicit in its position, sometimes combined with an unusual managerial skill plainly present in some individual or other, or some system or other.

And once we’d gotten over the hurdle of recognizing that a thing could be based on quantitative measures that would have horrified Graham, we started thinking about better businesses… Buffett Partnership, for example, owned American Express and Disney when they got pounded down.

(Illustration by Patrick Marcel Pelz)

Buffett actually amended the Ground Rules so that he could put 40% of BPL into American Express, which had gotten cheap after a huge, but solvable problem—exposure to the Salad Oil Scandal.  This was the largest position the partnership ever held, both on a percentage and absolute dollar basis.  BPL’s $13 million investment into American Express produced $20 million in profits over the course of a few years, thus creating a large portion of the partnership’s performance during this time.  (In today’s dollars, BPL’s Amex investment was about $90 million, while the profit was about $140 million.)

A high quality company has a high and sustainable return on invested capital (ROIC).  That’s only possible if the business has a sustainable competitive advantage.  Buffett:

The key to investing is not assessing how much an industry is going to affect society, or how much it will grow, but rather determining the competitive advantage of any given company and, above all, the durability of that advantage.  The products or services that have wide, sustainable moats around them are the ones that deliver rewards to investors.

Any investor who could find a company like See’s Candies—the quintessential high quality business—and buy it at a reasonable price, would do extremely well over time.  But it is exceedingly difficult, even for the smartest investors, to find companies like See’s Candies.

(Photo by Cihcvlss, via Wikimedia Commons)

Buffett and his business partner, Charlie Munger, acquired See’s Candies in 1972.  The company has typically experienced a return on invested capital (ROIC) of over 100 percent, which is extraordinary.  Buffett and Munger purchased See’s Candies for $25 million.  Since then, the business has generated over $2 billion in pre-tax earnings.

Tom Gayner of the Markel Corporation is another investor who has done quite well by buying high quality businesses.  Miller notes:

Tom emphasizes that you have to get only a very small number of these right for this type of strategy to really pay off.  The companies you get right will harness the power of compounding and grow to dwarf the mistakes.  He argues that investors who make twenty or so sound purchases over a lifetime will come to see one or two grow to become a significant percentage of their net worth.

Tom has a great example of this phenomenon that also reminds us not to pigeonhole Ben Graham as purely a deep value investor.  Graham paid up for quality when he bought the insurance company GEICO—he ended up making more profits from that single investment than he did from all his other activities combined.

What Should You Do?

Assume that you are an investor operating with modest sums.  Is it best to follow the deep value, cigar butt approach, or is it best to look for high-quality companies that can compound business value over time?  Miller writes:

One can make a strong case for either method, just as many well-respected investors have done.  Both can work, but what’s right for you will depend on the size of funds you are working with, your personality, your own ability to do good valuation work, and your ability to define objectively the outer edges of your own competence.

Tobias Carlisle, with his 2014 book, Deep Value, comes out as a good example of a Graham purist.  His research shows that the worse a cheap company’s fundamentals, the better the stock is likely to do.  With his deeply quantitative orientation, Tobias has developed something he calls the ‘Acquirer’s Multiple’ to identify and systematically make good investment decisions.  He seems to have found something that he understands and that works well for him.  Note that he literally shuns quality in his approach to finding value.

… While he’s smart to have found something that works for him, he’s even smarter to avoid what doesn’t.  Of course he’d prefer to buy a great business over a poor business if he could be sure that it could maintain its high returns well into the future.  However, he hasn’t yet found a way to identify the companies with the factors needed to protect those high returns from competition, at least systematically, so he avoids them.

As Buffett himself has often written, a quantitative, deep value approach is a much surer source of investment profits than an approach based on finding high quality companies.  Many investors are better off following a cigar-butt approach.  (This is what the Boole Microcap Fund does.)

(Photo by Sensay)

Buffett himself got the highest returns of his career from microcap cigar butts.  See: https://boolefund.com/buffetts-best-microcap-cigar-butts/

Concentration

Buffett has often observed that only a small handful of investments have been responsible for the vast majority of wealth he’s created over time.  Buffett:

I will only swing at pitches that I really like.  If you do it 10 times in your life, you’ll be rich.  You should approach investing like you have a punch card with 20 punch-outs, one for each trade in your life.

I think people would be better off if they only had 10 opportunities to buy stocks throughout their lifetime.  You know what would happen?  They would make sure that each buy was a good one.  They would do lots and lots of research before they made the buy.  You don’t have to have many 4X growth opportunities to get rich.  You don’t need to do too much, but the environment makes you feel like you need to do something all the time.

Whether you use a deep value approach or a strategy based on higher quality, it is possible to concentrate.

That said, if you use a quantitative approach—which works well for deep value—then having at least 15-20 positions generally works better over time.  Part of the reason is that, when buying a basket of deep value stocks—stocks which are typically very ugly—it is rarely possible to say which ones will be the best performers.  The legendary value investor Joel Greenblatt, who has excelled at both deep value and high-quality value, has readily admitted that the deep value stocks he picks as best often are not the best.  Greenblatt also has said:

In our experience, eliminating the stocks you would obviously not want to own eliminates many big winners.

As Tobias Carlisle so clearly illustrates in Deep Value, the ugliest of the ugly often end up being among the best performers.  Without a fully quantitative strategy—which forces you to buy the cheapest, ugliest stocks—it is easy to miss many big winners.

Tom Gayner’s strategy is almost the opposite of Tobias Carlisle’s but he understands it and it works for him.  Neither one is ‘right’ or ‘wrong’; each has developed a value system that works for him.  What’s right in investing is what works for the individual.

 

WORKOUTS

What Buffett called Workouts is now known as merger arbitrage (or risk arbitrage).  When one company announces that it will buy another, the acquisition target stock will move up towards the announced price, but not all the way.

With a sufficient spread and with a high probability of the deal closing, Buffett would take a position in the target company’s stock.  Buffett learned the technique from Graham.  If one were to combine the record of Graham-Newman, BPL, and Berkshire Hathaway through 1988—a total period of 65 years—Buffett calculated that merger arbitrage produced unlevered returns of about 20% per year.  So this was a very profitable category for the Buffett Partnership.

Because Buffett would often use up to 10% margin—and never more than 25%—the actual net returns for BPL were likely higher than 20% per year.  Thus, not only could Workouts do just as well as the Generals—because of the modest leverage used in merger arbitrage—but even more importantly, Workouts were largely uncorrelated (and often negatively correlated) with the overall stock market.  So even when the overall market was flat or down—which often meant that the Generals were flat or down—Workouts could and sometimes did produce a positive return.  As Buffett wrote:

Obviously the workouts (along with controls) saved the day in 1962, and if we had been light in this category that year, our final result would have been much poorer, although still quite respectable considering market conditions during the year.  We could just as well have had a much smaller percentage of our portfolio in workouts that year; availability decided it, not any notion on my part as to what the market was going to do.  Therefore, it is important to realize that in 1962 we were just plain lucky regarding mix of categories.

In 1963 we had one sensational workout which greatly influenced results, and generals gave a good account of themselves, resulting in a banner year.  If workouts had been normal, (say, more like 1962) we would have looked much poorer compared to the Dow….

Buffett goes on to note that in 1964, Workouts were a big drag on performance.  So Workouts didn’t work in every year, but they did tend to produce excellent returns over time.  And these returns were uncorrelated or negatively correlated with the returns of the Generals.  Buffett wrote: “In years of market decline, it piles up a big edge for us;  during bull markets, it is a drag on performance.”

Note:  Merger arbitrage has gotten much more difficult and competitive these days based on a much larger number of investors and based on huge computing power.  Thus, merger arbitrage is best not to do for most investors today.  Yet there are other types of investments with low correlation with the overall market that nonetheless can provide good long-term returns.  For instance, privately owned businesses might serve in this role.  Energy-related stocks—if held for at least 5 years—have low correlation with the overall market and also tend to outperform it.  Similarly, many microcap stocks have relatively low correlation with the broad market and outperform it over time.

 

CONTROLS

Controls are situations when Buffett bought enough stock so as to influence management to unlock value.  Miller gives the example of the Sanborn Map Company.  Buffett had more than one-third of the Partnership invested in this stock.  The company published and constantly revised highly detailed maps of all cities in the United States.  Fire insurance companies were the primary users of these maps.  Buffett wrote:

In the early 1950’s a competitive method of underwriting known as ‘carding’ made inroads on Sanborn’s business and after-tax profits of the map business fell from an average level of over $500,000 in the late 1930’s to under $100,000 in 1958 and 1959.  Considering the upward bias in the economy during this period, this amounted to an almost complete elimination of what had been sizable, stable earning power.

However, during the early 1930’s Sanborn had begun to accumulate an investment portfolio.  There were no capital requirements to the business so that any retained earnings could be devoted to this project.  Over a period of time, about $2.5 million was invested, roughly half in bonds and half in stocks.  Thus, in the last decade particularly, the investment portfolio blossomed while the operating map business wilted.

Let me give you some idea of the extreme divergence of these two factors.  In 1938 when the Dow-Jones Industrial Average was in the 100-120 range, Sanborn sold at $110 per share.  In 1958 with the Average in the 550 area, Sanborn sold at $45 per share.  Yet during that same period the value of the Sanborn investment portfolio increased from about $20 per share to $65 per share.  This means, in effect, that the buyer of Sanborn stock in 1938 was placing a positive valuation of $90 per share on the map business ($110 less the $20 value of the investments unrelated to the map business) in a year of depressed business and stock market conditions.  In the tremendously more vigorous climate of 1958 the same map business was evaluated at a minus $20 with the buyer of the stock unwilling to pay more than 70 cents on the dollar for the investment portfolio with the map business thrown in for nothing.

Buffett:

… The very fact that the investment portfolio had done so well served to minimize in the eyes of most directors the need for rejuvenation of the map business.  Sanborn had a sales volume of about $2 million per year and owned about $7 million worth of marketable securities.  The income from the investment portfolio was substantial, the business had no possible financial worries, the insurance companies were satisfied with the price paid for maps, and the stockholders still received dividends.  However, these dividends were cut five times in eight years although I could never find any record of suggestions pertaining to cutting salaries or director’s and committee fees.

[Most board members owned virtually no stock…]  The officers were capable, aware of the problems of the business, but kept in a subservient role by the Board of Directors.  The final member of our cast was a son of a deceased president of Sanborn.  The widow owned about 15,000 shares of stock.

In late 1958, the son, unhappy with the trend of the business, demanded the top position in the company, was turned down, and submitted his resignation, which was accepted.  Shortly thereafter we made a bid to his mother for her block of stock, which was accepted.  At the time there were two other large holdings, one of about 10,000 shares (dispersed among customers of a brokerage firm) and one of about 8,000.  These people were quite unhappy with the situation and desired a separation of the investment portfolio from the map business as did we.

Buffett continues:

There was considerable opposition on the Board to change of any type, particularly when initiated by an outsider, although management was in complete accord with our plan… To avoid a proxy fight… and to avoid time delay with a large portion of Sanborn’s money tied up in blue-chip stocks which I didn’t care for at current prices, a plan was evolved taking out all stockholders at fair value who wanted out.  The SEC ruled favorably on the fairness of the plan.  About 72% of the Sanborn stock, involving 50% of the 1,600 stockholders, was exchanged for portfolio securities at fair value.  The map business was left with over $1.25 million in government and municipal bonds as a reserve fund, and a potential corporate capital gains tax of over $1 million was eliminated.  The remaining stockholders were left with a slightly improved asset value, substantially higher earnings per share, and an increased dividend rate.

Lessons from Controls

Miller reminds us that investing in a stock is becoming a part owner of the business:

In 1960, one-third of the Partnership was in Sanborn’s stock, meaning one-third of the Partnership was in the business of selling insurance maps and managing a securities portfolio.  In his discussion of Controls, Buffett is teaching us to not think about ‘investing in a stock’ but instead to think about ‘being in a business.’

Miller again:

Whether you are running a business or evaluating one, a singular question remains paramount: what is its value, both in terms of the assets involved and the earnings produced, then, how can it be maximized?  The skill in answering these questions determines the success of investors and business managers like.

Buffett often quotes Ben Graham on this point:

Investment is most intelligent when it is most businesslike and business is most intelligent when it’s most investment-like.

In some cases, a General would languish in price for years, allowing BPL to continue acquiring the stock at cheap prices.  In this way, a General would sometimes become a Control.  A General is attractive as a cheap stock.  When a General becomes a Control, it becomes more attractive to the extent that BPL can actively work to unlock value.

In the case of Controls, Buffett was willing to work actively to unlock value, but it did often require taking actions that would be criticized, as Miller writes:

… he had to threaten Sanborn Map’s board with a proxy fight (legal battle) to get them to act… At Dempster Mill, we’ll see that he had to fire the CEO and bring in his own man, Harry Bottle.  Together they liquidated large parts of the business to restore the economics of the company.  Buffett was vilified in the local newspaper for doing so.  While he saw himself as saving the business by excising the rotten parts, critics only saw the lost jobs.  Early at Berkshire, he had to fire the CEO and hit the brakes on capital expenditures in textiles before redirecting the company’s focus to insurance and banking.  It was never easy and often stressful, but when action was needed, action was taken.  As he said, ‘Everything else being equal, I would much rather let others do the work.  However, when an active role is necessary to optimize the employment of capital, you can be sure we will not be standing in the wings.’

The ability to actively unlock value led Buffett naturally to concentrate heavily.  A situation like Sanborn had high upside and a tiny risk of loss, so it made sense to bet big.

With Dempster Mill, Berkshire, and Diversified Retailing Company (DRC), the values had to be estimated by Buffett and confirmed by auditors.  In the case of Dempster and Berkshire, BPL owned so much stock that trying to trade it could dramatically impact the market price.  That is why the year-end values had to be estimated, which Buffett did conservatively based on current value rather than future value.  DRC also had to be valued this way because it was a privately owned business that never had a publicly traded stock.

Correctly valuing the Controls was important.  Not only would it impact the year-end overall performance of BPL—too high of an estimate would inflate the performance, while too low of an estimate would depress the performance.  But also, correctly valuing Controls would impact limited partners who were entering or leaving the Partnership.  Exiting limited partners would benefit at the expense of remaining limited partners if the estimated value of the Controls was too high.  Conversely, new limited partners would benefit at the expense of existing limited partners if the estimated value of the Controls was too low.  Buffett was very careful, and his estimates were audited by the firm that would later become KPMG.

Buffett’s November 1966 letter to partners gives some detail on the appraisal process:

The dominant factors affecting control valuations are earnings power (past and prospective) and asset values.  The nature of our controlled businesses, the quality of the assets involved, and the fact that the Federal Income Tax basis applicable to the net assets substantially exceeds our valuations, cause us to place considerably more weight on the asset factor than is typical in most business valuations…. The Partnership Agreement charges me with the responsibility for establishing fair value for controlling interests, and this means fair to both adding and withdrawing partners at a specific point in time.  Wide changes in the market valuations accorded stocks at some point obviously find reflection in the valuation of businesses, although this factor is of much less importance when asset factors (particularly when current assets are significant) overshadow earnings power considerations in the valuation process…

It’s worth noting that Sanborn, Dempster, and Berkshire were all cigar butts where net asset value was much higher than the current market price.  They were very cheap businesses, but they were not good businesses, which is part of why valuing them was mostly based on asset value rather than earnings power.

Because Ben Graham relied mostly on the cigar-butt approach, basing his investments on discounts to liquidation value, Buffett had already learned how to value companies based on their assets.  Miller quotes Chapter 43 of Graham and Dodd’s Security Analysis:

The rule in calculating liquidating value is that the liabilities are real but the value of the assets must be questioned. This means that all true liabilities shown on the books must be deducted at their face amount.  The value to be ascribed to the assets, however, will vary according to their character.

Graham advised the following rule of thumb for liquidation analysis: 100 cents on the dollar for cash, 80 cents on the dollar for receivables, 67 cents on the dollar for inventory (with a wide range depending on the business), and 15 cents on the dollar for fixed assets.

In the case of Sanborn, the company had a hidden asset in the form of a large investment portfolio that was not reflected on its balance sheet.  Dempster Mill’s net assets were much higher on the balance sheet than was indicated by the market price.  Buffett had to determine what the assets were really worth.  With Berkshire, part of the value would be determined by redeploying capital into higher return opportunities.  (Buffett’s successful redeployment of Berkshire’s cash formed the foundation for Berkshire Hathaway, now one of the largest and most successful U.S. companies.)

Circle of Competence

A central concept for Buffett and Munger is circle of competence.  For any given company, are you capable of reasonably estimating what the assets are worth?  If not, you can either spend the time required to understand the company and the industry, or you can put it into the TOO HARD pile.

Buffett and Munger have three piles:  IN, OUT, and TOO HARD.  A great many public companies simply go into the TOO HARD pile.  This limitation—sticking with companies you can understand well—has been a key to the excellent long-term performance of Buffett and Munger.

For a value investor managing a smaller sum, who can focus on tiny, obscure microcap companies, there are thousands and thousands of businesses.  When there are so many that you probably can understand well, it makes no sense to spend long periods of time on businesses that are decidedly difficult to understand.

For example, you could spend months gaining an understanding of General Electric, or you could spend that same amount of time gaining a complete understanding of at least a dozen tiny microcap companies.  Many microcap businesses are quite simple.

Here’s the thing:  As Buffett has pointed out, frequently you don’t get paid for degree of difficulty in investing.  If you’re willing to turn over enough rocks, eventually you can find a microcap business that you can easily understand and that is extraordinarily cheap.  You’ll almost certainly do far better with that type of investment than with a mid-cap or large-cap company that’s much harder to understand and probably not nearly as cheap.

 

DEMPSTER DIVING:  THE ASSET CONVERSION PLAY

Dempster was a tiny micro cap, a family-owned company in Beatrice, Nebraska, that manufactured windmills and farm equipment.

(Photo by Digikhmer)

Miller:

Much of the fun in investing comes from the hunting process itself… Picture the pulse-quickening moment in 1956 when Buffett, thumbing through the Moody’s Manual, came across a tiny, obscure manufacturing company whose stock had fallen 75% in the previous year.  Realizing that it was now available for a fraction of its net working capital and an even smaller fraction of its book value, he started buying the stock as low as $17 a share.  He got out at $80.

Miller writes that Dempster can serve as a template for valuing businesses using the net asset value approach.  Dempster’s profits were very low, but the stock traded far below its asset value.

Buffett joined the board of directors soon after his first purchase.  He kept buying the stock for the next five years.  A large block of stock from the Dempster family became available for sale in 1961.  By August of that year, BPL owned 70% of Dempster and a few “associates” owned another 10%.  BPL’s average price was $1.2 million ($28/share), roughly a 50% discount to working capital and 66% discount to book value.  Dempster accounted for roughly 20% of BPL’s total assets by year-end.

The situation was challenging at first because the inventories were high and rising.  Buffett tried to work with existing management, but had to throw them out because inventories kept rising.  The company’s bank was threatening to seize the collateral backing the loan.  With 20% of BPL in Dempster, if the company went under it would have a large negative impact on the Partnership.  At Munger’s recommendation, Buffett met and hired an “operating man” name Harry Bottle.

Bottle was a turnaround specialist.  Buffett was so happy with Bottle’s work that in the next year’s letter, Buffett named him “man of the year.”  He cut inventories from $4 million to $1 million, quickly repaid the bank loan, cut administrative and selling expenses in half, and closed five unprofitable branches.  With help from Buffett and Munger, Bottle also raised prices up to 500% on their used equipment.  There was little impact on sales volume.  All of these steps worked together to put Dempster on a healthy economic footing.

Buffett then took an unusual step.  Whereas most managers feel automatically that they must reinvest profits into the business, even if the business is creating low returns, Buffett was more rational.  Miller explains:

With Dempster he wasn’t at all bogged down with all the emotional baggage of being a veteran of the windmill business.  He was in it to produce the highest rate of return on the capital he had tied up in the assets of the business.  This absolute scale allowed him to see that the fix for Dempster would come by not reinvesting back in windmills.  He immediately stopped the company from putting more capital in and started taking the capital out.

Instead, Buffett invested the capital into the cheapest stocks he could find, those offering the highest potential returns.  In effect, he was converting capital from a low-return business to a high-return business—buying cheap stocks until they rose towards intrinsic value.  Over time, Dempster looked less like a manufacturing company and more like the investment partnership.  Miller observes:

The willingness and ability to see investment capital as completely fungible, whether it is capital tied up in the assets of a business or capital that’s invested in securities, is an exceedingly rare trait.

Dempster initially was worth $35/share in 1961.  By year-end 1962, Dempster was worth $51/share, with market securities worth $35/share and the manufacturing operations worth $16/share.

Buffett also learned from this experience the importance of a high-quality and trustworthy CEO.  Buffett heaped praise on Harry Bottle.  Miller points out that Buffett developed a style like that of Dale Carnegie: Praise by name, criticize by category.

It should also be noted that Dempster’s market value in 1961 was $1.6 million, a tiny microcap company.  This kind of opportunity—including being able to buy control—is open to those investing relatively small sums.  Very often the cheapest stocks can be found among microcap companies.  This high degree of inefficiency results from the fact that most professionals investors never look at micro caps.

Miller sums it up:

Buffett teaches investors to think of stocks as a conduit through which they can own their share of the assets that make up a business.  The value of that business will be determined by one of two methods: (1) what the assets are worth if sold, or (2) the level of profits in relation to the value of assets required in producing them.  This is true for each and every business and they are interrelated.  Buffett commented, ‘Harry has continued this year to turn under-utilized assets into cash, but in addition, he has made the remaining needed assets productive.’

Operationally, a business can be improved in only three ways: (1) increase the level of sales; (2) reduce costs as a percent of sales; (3) reduce assets as a percentage of sales.  The other factors, (4) increase leverage or (5) lower the tax rate, are the financial drivers of business value.  These are the only ways a business can make itself more valuable.

Buffett ‘pulled all the levers’ at Dempster.  Raising prices on replacement parts and reducing operating costs pulled levers #1 and #2.  Lever #3 was pulled as inventories (assets) were reduced.  Lever #4 was pulled when Buffett borrowed money to buy more stocks.  Lever #5 was pulled when he avoided a big tax bill by selling all the operating assets of the company.

When profitability goes up and the capital required to produce it goes down, the returns and the value of the business go straight up.  Buffett understood this intrinsically and Dempster is now a powerful example for today’s investors who obsess over (1) and (2) at the expense of (3).  Pulling underutilized assets out of a company not only produces cash to be used elsewhere, it makes the business better and more valuable.  It is a wonderful reminder to individual and professional investors alike to focus their attention first on the balance sheet (there is a reason it comes first in the set of financial statements).  Never lose sight of the fact that without tangible assets, there would be no earnings in the first place.

 

CONSERVATIVE VERSUS CONVENTIONAL

Although following the crowd made sense in our evolutionary history, and still makes sense in many circumstances, following the crowd kills your ability to outperform the stock market.  Miller explains:

Successful investing requires you to do your own thinking and train yourself to be comfortable going against the crowd.  You could say that good results come primarily from a properly calibrated balance of hubris and humility—hubris enough to think you can have insights that are superior to the collective wisdom of the market, humility enough to know the limits of your abilities and to be willing to change course when errors are recognized.

You’ll have to evaluate facts and circumstances, apply logic and reason to form a hypothesis, and then act when the facts line up, irrespective of whether the crowd agrees or disagrees with your conclusions.  Investing well goes against the grain of social proof; it goes against the instincts that have been genetically programmed into our human nature.  That’s part of what makes it so hard.

Howard Marks, a Buffett contemporary who also has a literary bent, challenges his readers to “dare to be great” in order to dare to be better investors.  As he tells his readers, “the real question is whether you dare to do the things that are necessary in order to be great.  Are you willing to be different, and are you willing to be wrong?  In order to have a chance at great results, you have to be open to being both.”

There are two key ideas in Buffett’s highly independent approach:

  • The best purchases are made when your thinking puts you in opposition to conventional wisdom or popular trends.
  • A concentrated portfolio can actually be more conservative than a diversified one when the right conditions are met.

Conventional, academic thinking equates the riskiness of a stock with its beta, which is a measure of its volatility.  Buffett, later in his career, gave the following example to illustrate the silliness of beta:

The Washington Post Company in 1973 was selling for $80 million in the market.  At the time, that day, you could have sold the assets to any one of ten buyers for not less than $400 million, probably appreciably more…

Now, if the stock had declined even further to a price that made the valuation $40 million instead of $80 million, it’s beta would have been greater.  And to people who think beta [or, more importantly, downside volatility] measures risk, the cheaper price would have made it look riskier.  This is truly Alice in Wonderland.  I have never been able to figure out why it’s riskier to buy $400 million worth of properties for $40 million than $80 million….

In the 1970’s, the Washington Post Company was an outstanding, high-return business and remained so for decades.  Of course, like most businesses, its high profitability did not last, in this case because of the internet.

But the point is that if you, as a value investor, buy something at 20% of probable intrinsic value, and the stock then drops 50% and you buy a bunch more, your investment now has 10x upside instead of 5x, and simultaneously, your investment is now probably safer.

Having the expected return from your investment double, while at the same time having the downside risk get cut in half, is completely contrary to what is taught in modern finance theory.  Finance theory says that a higher potential return always requires higher risk.  Yet the experience of many value investors is that quite often an increase in potential return also means a decrease in risk.  Thus, a value investor cheers (and backs up the truck) when his or her best idea keeps going down in price, and this happens routinely.

Thinking for Yourself

The best time to buy is when the crowd is most fearful.  But this requires thinking for yourself.  A good example is when Buffett put 40% of BPL into American Express after the Salad Oil Scandal.  Miller:

The Partnership lessons teach investors that there is only one set of circumstances where you or anyone else should make an investment—when the important facts in a situation are fully understood and when the course of action is as plain as day.  Otherwise, pass.  For instance, in Sanborn, when Buffett realized he was virtually assured to make money in the stock given he was buying the securities portfolio at 70 cents on the dollar with the map company coming for free, he invested heavily.  When he saw Dempster was selling below the value of its excess inventory alone, he loaded up.

Miller quotes Buffett:

When we really sit back with a smile on our face is when we run into a situation we can understand, where the facts are ascertainable and clear, and the course of action is obvious.  In that case—whether conventional or unconventional—whether others agree or disagree—we feel—we are progressing in a conservative manner.

Ben Graham:

You’re neither right nor wrong because the crowd disagrees with you.  You’re right because your data and reasoning are right.

Buffett again:

You will not be right simply because a large number of people momentarily agree with you.  You will not be right simply because important people agree with you… You will be right, over the course of many transactions, if your hypotheses are correct, your facts are correct, and your reasoning is correct.

Buffett, once more:

A public opinion poll is no substitute for thought.

Loading Up

Buffett thought it was conservative and rational to put 40% of the Partnership assets into American Express.  Buffett had amended the Ground Rules of the Partnership to include a provision that allowed up to 40% of BPL’s assets to be in a single security under conditions “coupling an extremely high probability that our facts and reasoning are correct with a very low probability that anything could drastically change the underlying value of the investment.”

Miller notes that Buffett gave the following advice to a group of students in the late 1990s:

If you can identify six wonderful businesses, that is all the diversification you need.  And you will make a lot of money.  And I can guarantee that going into a seventh one instead of putting more money into your first one is going to be a terrible mistake.  Very few people have gotten rich on their seventh best idea.  But a lot of people have gotten rich with their best idea.  So I would say for anyone working with normal capital who really knows the businesses they have gone into, six is plenty, and I [would] probably have half of [it in] what I like best.

Your Best Ideas Define Your Next Choice

If you’re using concentrated value investing, then the simple test for whether to add a new idea to your portfolio is to compare any new idea to your best current ideas.

Successful concentrated value investing requires a great deal of passion, curiosity, patience, and prior experience (i.e., lots of mistakes).  It also often requires a focus on tiny, obscure micro caps, since this is the most inefficient part of the market and it contains many simple businesses.

Buffett explains:

Simply stated, this means I am willing to concentrate quite heavily in what I believe to be the best investment opportunities recognizing very well that this may cause an occasional very sour year—one somewhat more sour, probably, than if I had diversified more.  While this means our results will bounce around more, I think it also means that our long-term margin of superiority should be greater.

Buffett in the January 25, 1967, BPL Letter:

Our relative performance in this category [Generals–Relatively Undervalued] was the best we have ever had—due to one holding which was our largest investment at yearend 1965 and also yearend 1966.  This investment has substantially outperformed the general market for us during each year (1964, 1965, 1966) that we have held it.  While any single year’s performance can be quite erratic, we think the probabilities are highly favorable for superior future performance over a three or four year period.  The attractiveness and relative certainty of this particular security are what caused me to introduce Ground Rule 7 in November, 1965 to allow individual holdings of up to 40% of our net assets.  We spend considerable effort continuously evaluating every facet of the company and constantly testing our hypothesis that this security is superior to alternative investment choices.  Such constant evaluation and comparison at shifting prices is absolutely essential to our investment operation.

It would be much more pleasant (and indicate a more favorable future) to report that our results in the Generals—Relatively Undervalued category represented fifteen securities in ten industries, practically all of which outperformed the market.  We simply don’t have that many good ideas…

 

SIZE VERSUS PERFORMANCE

Miller comments that Buffett, if he were managing a relatively small amount of money, probably would have stayed fully invested even during the speculative peak of the late 1990’s.  This is largely because there are almost always cheap microcap companies that are too small and obscure to be noticed by most investors.  As Buffett said during the late 1990’s:

If I was running $1 million, or $10 million for that matter, I’d be fully invested.

There were times when he was managing BPL when Buffett recognized that more assets under management would increase the Partnership’s ability to do Control investments.  But according to Buffett, it was also sometimes true that less assets under management made it easier to invest in tiny, cheap microcap companies.  So Buffett wrote:

What is more important—the decreasing prospects of profitability in passive investments or the increasing prospects in control investments?  I can’t give a definite answer to this since to a great extent it depends on the type of market in which we are operating.  My present opinion is that there is no reason to think these should not be offsetting factors;  if my opinion should change, you will be told.  I can say, most assuredly, that our results in 1960 and 1961 would not have been better if we had been operating with the much smaller sums of 1956 and 1957.

By 1966, however, when assets under management reached $43 million, Buffett changed his mind.  He wrote his partners:

As circumstances presently appear, I feel substantially greater size is more likely to harm future results than to help them.  This might not be true for my own personal results, but it is likely to be true for your results.

Buffett saw a drag on performance that would probably develop as a result of two factors:  larger assets under management, and a stock market that was high overall, with far fewer opportunities.  It’s important to note again that Buffett did not think a high market would be a factor if he were managing smaller sums.  As Buffett said in 2005, when asked if he could still make 50% per year with smaller sums:

Yes, I would still say the same thing today.  In fact, we are still earning those types of returns on some of our smaller investments.  The best decade was the 1950s;  I was earning 50% plus returns with small amounts of capital.  I could do the same thing today with smaller amounts.  It would perhaps even be easier to make that much money in today’s environment because information is easier to access.  You have to turn over a lot of rocks to find those little anomalies.  You have to find the companies that are off the map—way off the map.  You may find local companies that have nothing wrong with them at all.  A company that I found, Western Insurance Securities, was trading for $3/share when it was earning $20/share!!  I tried to buy up as much of it as possible.  No one will tell you about these businesses.  You have to find them.

Ideas versus Capital

The bottom line is simple:  If you have more capital than ideas, then assets are too large and will be a drag on performance.  If you have more ideas than capital, then assets are not a drag and may even be too small.

 

GO-GO OR NO-GO

In 1956, Buffett had told his partners that he thought the stock market was high relative to intrinsic value.  Since he never tried to predict the market, he remained focused on finding tiny microcap companies that were cheap.  Staying focused on finding what was cheapest was central to the 29.8% per year the BPL achieved over the ensuing decade.  Had Buffett ever invested less because he was worried about a stock market decline, his record would have been nowhere near as good.

An expensive stock market says nothing about when a correction will happen.  And an expensive stock market rarely means that there are no obscure, cheap microcap companies.

By 1966, however, because BPL had more assets under management and because Buffett thought the stock market was even more overvalued, Buffett finally decided not to accept any new capital.

Somewhat ironically, BPL had its best year ever in 1968, with a return of 58.8%.  But this also led Buffett to consider closing the Partnership altogether.  Buffett had simply run out of ideas, due to the combination of his assets under management and a stock market that was quite overvalued in his view.

In May 1969, Buffett announced his decision to liquidate the Partnership.  Performance in 1969 was mediocre, and Buffett wrote:

… I would continue to operate the Partnership in 1970, or even 1971, if I had some really first class ideas.  Not because I want to, but simply because I would so much rather end with a good year than a poor one.  However, I just don’t see anything available that gives any reasonable hope of delivering such a good year and I have no desire to grope around, hoping to ‘get lucky’ with other people’s money.  I am not attuned to this market environment and I don’t want to spoil a decent result by trying to play a game I don’t understand just so I can go out a hero.

Go-Go Years – Jerry Tsai

The big bull market run of the 1960s became known as the Go-Go years.  Jerry Tsai’s highly speculative investment style, which produced high returns for some time, was representative of the Go-Go years.  In 1968, Tsai shrewdly sold his Manhattan Fund, which had $500 million under management.  The fund went on to lose 90% of its value over the next several years.

 

TOWARD A HIGHER FORM

Buffett constantly evolved as an investor.  As Miller writes:

A good deal of this evolution occurred throughout the Partnership years, where we have seen a willingness to concentrate his investments to greater and greater degrees, a steady migration toward quality compounders from statistically cheap cigar butts, and the forging of his highly unique ability to break down the distinction between assets and capital in a way that allows for their fungibility in the pursuit of higher returns.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.  

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.