Cheap, Solid Microcaps Far Outperform the S&P 500

(Image: Zen Buddha Silence, by Marilyn Barbone)

December 1, 2019

The wisest 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: http://boolefund.com/warren-buffett-jack-bogle/

But you can do significantly better — roughly 7% per year (on average) — 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: http://boolefund.com/buffetts-best-microcap-cigar-butts/

Look at this summary of the CRSP Decile-Based Size and Return Data from 1927 to 2015:

Decile Market Cap-Weighted Returns Equal Weighted Returns Number of Firms (year-end 2015) Mean Firm Size (in millions)
1 9.29% 9.20% 173 84,864
2 10.46% 10.42% 178 16,806
3 11.08% 10.87% 180 8,661
4 11.32% 11.10% 221 4,969
5 12.00% 11.92% 205 3,151
6 11.58% 11.40% 224 2,176
7 11.92% 11.87% 300 1,427
8 12.00% 12.27% 367 868
9 11.40% 12.39% 464 429
10 12.50% 17.48% 1,298 107
9+10 11.85% 16.14% 1,762 192

(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 2015:

Microcap equal weighted returns = 16.14% per year

Large-cap equal weighted returns = ~11% 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 2015, versus 11% per year for an equal weighted large-cap approach.

Still, if you can do 3% 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/EBIT 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:  http://boolefund.com/joseph-piotroski-value-investing/

 

BOTTOM LINE

In sum, over time, a quantitative value strategy — applied to cheap microcap stocks with improving fundamentals — has high odds of returning at least 7% (+/- 3%) more per year than an S&P 500 index fund.

If you’d like to learn more about how the Boole Fund can help you do roughly 7% better per year than the S&P 500, please call or e-mail me any time.

E-mail: jb@boolefund.com  (Jason Bond)

 

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:  http://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

(Image:  Zen Buddha Silence by Marilyn Barbone.)

November 17, 2019

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:  http://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 Black Swan


(Image:  Zen Buddha Silence by Marilyn Barbone.)

November 3, 2019

Nassim Nicholas Taleb  is the author of several books, including Fooled by RandomnessThe Black Swan, and Antifragile.  I wrote about Fooled by Randomness here: http://boolefund.com/fooled-by-randomness/

Today’s blog post is a summary of Taleb’s The Black Swan.  If you’re an investor, or if you have any interest in predictions or in history, then this is a MUST-READ book.  One of Taleb’s main points is that Black Swans, which are unpredictable, can be either positive or negative.  It’s crucial to try to be prepared for negative Black Swans and to try to benefit from positive Black Swans.  However, many measurements of risk in finance assume a statistical distribution that is normal when they should assume a distribution that is fat-tailed.  These standard measures of risk won’t prepare you for a Black Swan.

That said, Taleb is an option trader, whereas I am a value investor.  For me, if you buy a stock far below liquidation value, then usually you have a margin of safety.  A group of such stocks will outperform the market over time while carrying low risk.  Furthermore, if you’re a long-term investor, then you can either adopt a value investing approach or you can simply invest in low-cost index funds.  Either way, given a long enough period of time, you should get good results.  The market has recovered from every crash and has eventually gone on to new highs.  Yet Taleb misses this point.

Nonetheless, although Taleb overlooks value investing and index funds, his views on predictions and on history are very insightful and should be studied by every thinking person.

Black Swan in Auckland, New Zealand.  Photo by Angela Gibson.

Here’s the outline:

    • Prologue

PART ONE: UMBERTO ECO’S ANTILIBRARY, OR HOW WE SEEK VALIDATION

    • Chapter 1: The Apprenticeship of an Empirical Skeptic
    • Chapter 2: Yevgenia’s Black Swan
    • Chapter 3: The Speculator and the Prostitute
    • Chapter 4: One Thousand and One Days, or How Not to Be a Sucker
    • Chapter 5: Confirmation Schmonfirmation!
    • Chapter 6: The Narrative Fallacy
    • Chapter 7: Living in the Antechamber of Hope
    • Chapter 8: Giacomo Casanova’s Unfailing Luck: The Problem of Silent Evidence
    • Chapter 9: The Ludic Fallacy, or the Uncertainty of the Nerd

PART TWO: WE JUST CAN’T PREDICT

    • Chapter 10: The Scandal of Prediction
    • Chapter 11: How to Look for Bird Poop
    • Chapter 12: Epistemocracy, a Dream
    • Chapter 13: Apelles the Painter, or What Do You Do if You Cannot Predict?

PART THREE: THOSE GRAY SWANS OF EXTREMISTAN

    • Chapter 14: From Mediocristan to Extremistan, and Back
    • Chapter 15: The Bell Curve, That Great Intellectual Fraud
    • Chapter 16: The Aesthetics of Randomness
    • Chapter 17: Locke’s Madmen, or Bell Curves in the Wrong Places
    • Chapter 18: The Uncertainty of the Phony

PART FOUR: THE END

    • Chapter 19: Half and Half, or How to Get Even with the Black Swan

 

PROLOGUE

Taleb writes:

Before the discovery of Australia, people in the Old World were convinced that all swans were white, an unassailable belief as it seemed completely confirmed by empirical evidence… It illustrates a severe limitation to our learning from observations or experience and the fragility of our knowledge.  One single observation can invalidate a general statement derived from millenia of confirmatory sightings of millions of white swans.

Taleb defines a black swan as having three attributes:

    • First, it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility.
    • Second, it carries an extreme impact.
    • Third, in spite of its outlier status, human nature makes us concoct explanations for its occurrence after the fact, making it explainable and predictable.

Taleb notes that the effect of Black Swans has been increasing in recent centuries.  Furthermore, social scientists still assume that risks can be modeled using the normal distribution, i.e., the bell curve.  Social scientists have not incorporated “Fat Tails” into their assumptions about risk.  (A fat-tailed statistical distribution, as compared to a normal distribution, carries higher probabilities for extreme outliers.)

(Illustration by Peter Hermes Furian:  The red curve is a normal distribution, whereas the orange curve has fat tails.)

Taleb continues:

Black Swan logic makes what you don’t know far more relevant than what you do know.  Consider that many Black Swans can be caused and exacerbated by their being unexpected.

Taleb mentions the Sept. 11, 2001 terrorist attack on the twin towers.  If such an attack had been expected, then it would have been prevented.  Taleb:

Isn’t it strange to see an event happening precisely because it was not supposed to happen?  What kind of defense do we have against that? … It may be odd that, in such a strategic game, what you know can be truly inconsequential.

Taleb argues that Black Swan logic applies to many areas in business and also to scientific theories.  Taleb makes a general point about history:

The inability to predict outliers implies the inability to predict the course of history, given the share of these events in the dynamics of events.

Indeed, people, especially experts, have a terrible record in forecasting political and economic events.  Taleb advises:

Black Swans being unpredictable, we need to adjust to their existence (rather than naively try to predict them).  There are so many things we can do if we focus on antiknowledge, or what we do not know.  Among many other benefits, you can set yourself up to collect serendipitous Black Swans (of the positive kind) by maximizing your exposure to them.  Indeed, in some domains—such as scientific discovery and venture capital investments—there is a disproportionate payoff from the unknown, since you typically have little to lose and plenty to gain from a rare event… The strategy is, then, to tinker as much as possible and try to collect as many Black Swan opportunities as you can.

Taleb introduces the terms Platonicity and the Platonic fold:

Platonicity is what makes us think that we understand more than we actually do.  But this does not happen everywhere.  I am not saying that Platonic forms don’t exist.  Models and constructions, these intellectual maps of reality, are not always wrong; they are wrong only in some specific applications.  The difficulty is that a) you do not know before hand (only after the fact) where the map will be wrong, and b) the mistakes can lead to severe consequences…

The Platonic fold is the explosive boundary where the Platonic mindset enters in contact with messy reality, where the gap between what you know and what you think you know becomes dangerously wide.  It is here that the Black Swan is produced.

 

PART ONE: UMBERTO ECO’S ANTILIBRARY, OR HOW WE SEEK VALIDATION

Umberto Eco’s personal library contains thirty thousand books.  But what’s important are the books he has not yet read.  Taleb:

Read books are far less valuable than unread books.  The library should contain as much of what you do not know as your financial means, mortgage rates, and the currently tight real-estate market allow you to put there… Indeed, the more you know, the larger the rows of unread books.  Let us call this collection of unread books an antilibrary.

Taleb adds:

Let us call an antischolar—someone who focuses on the unread books, and makes an attempt not to treat his knowledge as a treasure, or even a possession, or even a self-esteem enhancement device—a skeptical empiricist.

(Photo by Pp1)

 

CHAPTER 1: THE APPRENTICESHIP OF AN EMPIRICAL SKEPTIC

Taleb says his family is from “the Greco-Syrian community, the last Byzantine outpost in northern Syria, which included what is now called Lebanon.”  Taleb writes:

People felt connected to everything they felt was worth connecting to; the place was exceedingly open to the world, with a vastly sophisticated lifestyle, a prosperous economy, and temperate weather just like California, with snow-covered mountains jutting above the Mediterranean.  It attracted a collection of spies (both Soviet and Western), prostitutes (blondes), writers, poets, drug dealers, adventurers, compulsive gamblers, tennis players, apres-skiers, and merchants—all professions that complement one another.

Taleb writes about when he was a teenager.  He was a “rebellious idealist” with an “ascetic taste.”  Taleb:

As a teenager, I could not wait to go settle in a metropolis with fewer James Bond types around.  Yet I recall something that felt special in the intellectual air.  I attended the French lycee that had one of the highest success rates for the French baccalaureat (the high school degree), even in the subject of the French language.  French was spoken there with some purity: as in prerevolutionary Russia, the Levantine Christian and Jewish patrician class (from Istanbul to Alexandria) spoke and wrote formal French as a language of distinction.  The most privileged were sent to school in France, as both my grandfathers were… Two thousand years earlier, by the same instinct of linguistic distinction, the snobbish Levantine patricians wrote in Greek, not the vernacular Aramaic… And, after Hellenism declined, they took up Arabic.  So in addition to being called a “paradise,” the place was also said to be a miraculous crossroads of what are superficially tagged “Eastern” and “Western” cultures.

Then a Black Swan hit:

The Lebanese “paradise” suddenly evaporated, after a few bullets and mortar shells… after close to thirteen centuries of remarkable ethnic coexistence, a Black Swan, coming out of nowhere, transformed the place from heaven to hell.  A fierce civil war began between Christians and Moslems, including the Palastinian refugees who took the Moslem side.  It was brutal, since the combat zones were in the center of town and most of the fighting took place in residential areas (my high school was only a few hundred feet from the war zone).  The conflict lasted more than a decade and a half.

Taleb makes a general point about history:

The human mind suffers from three ailments as it comes into contact with history, what I call the triplet of opacity.  They are:

    • the illusion of understanding, or how everyone thinks he knows what is going on in a world that is more complicated (or random) than they realize;
    • the retrospective distortion, or how we can assess matters only after the fact, as if they were in a rearview mirror (history seems clearer and more organized in history books than in empirical reality); and
    • the overvaluation of factual information and the handicap of authoritative and learned people, particularly when they create categories—when they “Platonify.”

Taleb points out that a diary is a good way to record events as they are happening.  This can help later to put events in their context.

Photo by Anton Samsonov

Taleb writes about the danger of oversimplification:

Any reduction of the world around us can have explosive consequences since it rules out some sources of uncertainty; it drives us to a misunderstanding of the fabric of the world.  For instance, you may think that radical Islam (and its values) are your allies against the threat of Communism, and so you may help them develop, until they send two planes into downtown Manhattan.

 

CHAPTER 2: YEVGENIA’S BLACK SWAN

Taleb:

Five years ago, Yevgenia Nikolayevna Krasnova was an obscure and unpublished novelist, with an unusual background.  She was a neuroscientist with an interest in philosophy (her first three husbands had been philosophers), and she got it into her stubborn Franco-Russian head to express her research and ideas in literary form.

Most publishers largely ignored Yevgenia.  Publishers who did look at Yevnegia’s book were confused because she couldn’t seem to answer the most basic questions.  “Is this fiction or nonfiction?”  “Who is this book written for?”  (Five years ago, Yevgenia attended a famous writing workshop.  The instructor told her that her case was hopeless.)

Eventually the owner of a small unknown publishing house agreed to publish Yevgenia’s book.  Taleb:

It took five years for Yevnegia to graduate from the “egomaniac without anything to justify it, stubborn and difficult to deal with” category to “persevering, resolute, painstaking, and fiercely independent.”  For her book slowly caught fire, becoming one of the great and strange successes in literary history, selling millions of copies and drawing so-called critical acclaim…

Yevgenia’s book is a Black Swan.

 

 

CHAPTER 3: THE SPECULATOR AND THE PROSTITUTE

Taleb introduces Mediocristan and Extremistan:

Mediocristan Extremistan
Nonscalable Scalable
Mild or type 1 randomness Wild (even superwild) type 2 randomness
The most typical member is mediocre The most “typical” is either giant or dwarf, i.e., there is no typical member
Winners get a small segment of the total pie Winner-take-almost-all effects
Example: Audience of an opera singer before the gramophone Today’s audience for an artist
More likely to be found in our ancestral environment More likely to be found in our modern environment
Impervious to the Black Swan Vulnerable to the Black Swan
Subject to gravity There are no physical constraints on what a number can be
Corresponds (generally) to physical quantities, i.e., height Corresponds to numbers, say, wealth
As close to utopian equality as reality can spontaneously deliver Dominated by extreme winner-take-all inequality
Total is not determined by a single instance or observation Total will be determined by a small number of extreme events
When you observe for a while you can get to know what’s going on It takes a long time to get to know what’s going on
Tyranny of the collective Tyranny of the accidental
Easy to predict from what you see and extend to what you do not see Hard to predict from past information
History crawls History makes jumps
Events are distributed according to the “bell curve” or its variations The distribution is either Mandelbrotian “gray” Swans (tractable scientifically) or totally intractable Black Swans

Taleb observes that Yevgenia’s rise from “the second basement to superstar” is only possible in Extremistan.

(Photo by Flavijus)

Taleb comments on knowledge and Extremistan:

What you can know from data in Mediocristan augments very rapidly with the supply of information.  But knowledge in Extremistan grows slowly and erratically with the addition of data, some of it extreme, possibly at an unknown rate.

Taleb gives many examples:

Matters that seem to belong to Mediocristan (subjected to what we call type 1 randomness): height, weight, calorie consumption, income for a baker, a small restaurant owner, a prostitute, or an orthodontist; gambling profits (in the very special case, assuming the person goes to a casino and maintains a constant betting size), car accidents, mortality rates, “IQ” (as measured).

Matters that seem to belong to Extremistan (subjected to what we call type 2 randomness): wealth, income, book sales per author, book citations per author, name recognition as a “celebrity,” number of references on Google, populations of cities, uses of words in a vocabulary, numbers of speakers per language, damage caused by earthquakes, deaths in war, deaths from terrorist incidents, sizes of planets, sizes of companies, stock ownership, height between species (consider elephants and mice), financial markets (but your investment manager does not know it), commodity prices, inflation rates, economic data.  The Extremistan list is much longer than the prior one.

Taleb concludes the chapter by introducing “gray” swans, which are rare and consequential, but somewhat predictable:

They are near-Black Swans.  They are somewhat tractable scientifically—knowing about their incidence should lower your surprise; these events are rare but expected.  I call this special case of “gray” swans Mandelbrotian randomness.  This category encompasses the randomness that produces phenomena commonly known by terms such as scalable, scale-invariant, power laws, Pareto-Zipf laws, Yule’s law, Paretian-stable processes, Levy-stable, and fractal laws, and we will leave them aside for now since they will be covered in some depth in Part Three…

You can still experience severe Black Swans in Mediocristan, though not easily.  How?  You may forget that something is random, think that it is deterministic, then have a surprise.  Or you can tunnel and miss on a source of uncertainty, whether mild or wild, owing to lack of imagination—most Black Swans result from this “tunneling” disease, which I will discuss in Chapter 9.

 

CHAPTER 4: ONE THOUSAND AND ONE DAYS, OR HOW NOT TO BE A SUCKER

Photo of turkey by Chris Galbraith

Taleb introduces the Problem of Induction by using an example from the philosopher Bertrand Russell:

How can we logically go from specific instances to reach general conclusions?  How do we know what we know?  How do we know that what we have observed from given objects and events suffices to enable us to figure out their other properties?  There are traps built into any kind of knowledge gained from observation.

Consider a turkey that is fed every day.  Every single feeding will firm up the bird’s belief that it is a general rule of life to be fed every day by friendly members of the human race “looking out for its best interests,” as a politician would say.  On the afternoon of the Wednesday before Thanksgiving, something unexpected will happen to the turkey.  It will incur a revision of belief.

The rest of this chapter will outline the Black Swan problem in its original form: How can we know the future, given knowledge of the past; or, more generally, how can we figure out properties of the (infinite) unknown based on the (finite) known?

Taleb says that, as in the example of the turkey, the past may be worse than irrelevant.  The past may be “viciously misleading.”  The turkey’s feeling of safety reached its high point just when the risk was greatest.

Roasted turkey.  Photo by Alexander Raths.

Taleb gives the example of banking, which was seen and presented as “conservative,” based on the rarity of loans going bust.  However, you have to look at the loans over a very long period of time in order to see if a given bank is truly conservative.  Taleb:

In the summer of 1982, large American banks lost close to all their past earnings (cumulatively), about everything they ever made in the history of American banking—everything.  They had been lending to South and Central American countries that all defaulted at the same time—”an event of an exceptional nature”… They are not conservative; just phenomenally skilled at self-deception by burying the possibility of a large, devastating loss under the rug.  In fact, the travesty repeated itself a decade later, with the “risk-conscious” large banks once again under financial strain, many of them near-bankrupt, after the real-estate collapse of the early 1990s in which the now defunct savings and loan industry required a taxpayer-funded bailout of more than half a trillion dollars.

Taleb offers another example: the hedge fund Long-Term Capital Management (LTCM).  The fund calculated risk using the methods of two Nobel Prize-winning economists.  According to these calculations, risk of blowing up was infinitesimally small.  But in 1998, LTCM went bankrupt almost instantly.

A Black Swan is always relative to your expectations.  LTCM used science to create a Black Swan.

Taleb writes:

In general, positive Black Swans take time to show their effect while negative ones happen very quickly—it is much easier and much faster to destroy than to build.

Although the problem of induction is often called “Hume’s problem,” after the Scottish philosopher and skeptic David Hume, Taleb holds that the problem is older:

The violently antiacademic writer, and antidogma activist, Sextus Empiricus operated close to a millenium and a half before Hume, and formulated the turkey problem with great precision… We surmise that he lived in Alexandria in the second century of our era.  He belonged to a school of medicine called “empirical,” since its practitioners doubted theories and causality and relied on past experience as guidance in their treatment, though not putting much trust in it.  Furthermore, they did not trust that anatomy revealed function too obviously…

Sextus represented and jotted down the ideas of the school of the Pyrrhonian skeptics who were after some form of intellectual therapy resulting from the suspension of belief… The Pyrrhonian skeptics were docile citizens who followed customs and traditions whenever possible, but taught themselves to systematically doubt everything, and thus attain a level of serenity.  But while conservative in their habits, they were rabid in their fight against dogma.

Taleb asserts that his main aim is how not to be a turkey.

In a way, all I care about is making a decision without being the turkey.

Taleb introduces the themes for the next five chapters:

    • We focus on preselected segments of the seen and generalize from it to the unseen: the error of confirmation.
    • We fool ourselves with stories that cater to our Platonic thirst for distinct patterns: the narrative fallacy.
    • We behave as if the Black Swan does not exist: human nature is not programmed for Black Swans.
    • What we see is not necessarily all that is there.  History hides Black Swans from us and gives us a mistaken idea about the odds of these events: this is the distortion of silent evidence.
    • We “tunnel”: that is, we focus on a few well-defined sources of uncertainty, on too specific a list of Black Swans (at the expense of the others that do not easily come to mind).

 

CHAPTER 5: CONFIRMATION SHMONFIRMATION!

Taleb asks about two hypothetical situations.  First, he had lunch with O.J. Simpson and O.J. did not kill anyone during the lunch.  Isn’t that evidence that O.J. Simpson is not a killer?  Second, Taleb imagines that he took a nap on the railroad track in New Rochelle, New York.  He didn’t die during his nap, so isn’t that evidence that it’s perfectly safe to sleep on railroad tracks?  Of course, both of these situations are analogous to the 1,001 days during which the turkey was regularly fed.  Couldn’t the turkey conclude that there’s no evidence of any sort of Black Swan?

The problem is that people confuse no evidence of Black Swans with evidence of no possible Black Swans.  Just because there has been no evidence yet of any possible Black Swans does not mean that there’s evidence of no possible Black Swans.  Taleb calls this confusion the round-trip fallacy, since the two statements are not interchangeable.

Taleb writes that our minds routinely simplify matters, usually without our being consciously aware of it.  Note: In his book, Thinking, Fast and Slow, the psychologist Daniel Kahneman argues that System 1, our intuitive system, routinely oversimplifies, usually without our being consciously aware of it.

Taleb continues:

Many people confuse the statement “almost all terrorists are Moslems” with “almost all Moslems are terrorists.”  Assume that the first statement is true, that 99 percent of terrorists are Moslems.  This would mean that only about .001 percent of Moslems are terrorists, since there are more than one billion Moslems and only, say, ten thousand terrorists, one in a hundred thousand.  So the logical mistake makes you (unconsciously) overestimate the odds of a randomly drawn individual Moslem person… being a terrorist by close to fifty thousand times!

Taleb comments:

Knowledge, even when it is exact, does not often lead to appropriate actions because we tend to forget what we know, or forget how to process it properly if we do not pay attention, even when we are experts.

Taleb notes that the psychologists Daniel Kahneman and Amos Tversky did a number of experiments in which they asked professional statisticians statistical questions not phrased as statistical questions.  Many of these experts consistently gave incorrect answers.

Taleb explains:

This domain specificity of our inferences and reactions works both ways: some problems we can understand in their applications but not in textbooks; others we are better at capturing in the textbook than in the practical application.  People can manage to effortlessly solve a problem in a social situation but struggle when it is presented as an abstract logical problem.  We tend to use different mental machinery—so-called modules—in different situations: our brain lacks a central all-purpose computer that starts with logical rules and applies them equally to all possible situations.

Note: Again, refer to Daniel Kahneman’s book, Thinking, Fast and Slow.  System 1 is the fast-thinking intuitive system that works effortlessly and often subconsciously.  System 1 is often right, but sometimes very wrong.  System 2 is the logical-mathematical system that can be trained to do logical and mathematical problems.  System 2 is generally slow and effortful, and we’re fully conscious of what System 2 is doing because we have to focus our attention for it to operate. See: http://boolefund.com/cognitive-biases/

Taleb next writes:

An acronym used in the medical literature is NED, which stands for No Evidence of Disease.  There is no such thing as END, Evidence of No Disease.  Yet my experience discussing this matter with plenty of doctors, even those who publish papers on their results, is that many slip into the round-trip fallacy during conversation.

Doctors in the midst of the scientific arrogance of the 1960s looked down at mothers’ milk as something primitive, as if it could be replicated by their laboratories—not realizing that mothers’ milk might include useful components that could have eluded their scientific understanding—a simple confusion of absence of evidence of the benefits of mothers’ milk with evidence of absence of the benefits (another case of Platonicity as “it did not make sense” to breast-feed when we could simply use bottles).  Many people paid the price for this naive inference: those who were not breast-fed as infants turned out to be at an increased risk of a collection of health problems, including a higher likelihood of developing certain types of cancer—there had to be in mothers’ milk some necessary nutrients that still elude us.  Furthermore, benefits to mothers who breast-feed were also neglected, such as a reduction in the risk of breast cancer.

Taleb makes the following point:

I am not saying here that doctors should not have beliefs, only that some kinds of definitive, closed beliefs need to be avoided… Medicine has gotten better—but many kinds of knowledge have not.

Taleb defines naive empiricism:

By a mental mechanism I call naive empiricism, we have a natural tendency to look for instances that confirm our story and our vision of the world—these instances are always easy to find…

Taleb makes an important point here:

Even in testing a hypothesis, we tend to look for instances where the hypothesis proved true.

Daniel Kahneman has made the same point.  System 1 (intuition) automatically looks for confirmatory evidence, but even System 2 (the logical-mathematical-rational system) naturally looks for evidence that confirms a given hypothesis.  We have to train System 2 not only to do logic and math, but also to look for disconfirming rather than confirming evidence.  Taleb says:

We can get closer to the truth by negative instances, not by verification!  It is misleading to build a general rule from observed facts.  Contrary to conventional wisdom, our body of knowledge does not increase from a series of confirmatory observations, like the turkey’s.

Taleb adds:

Sometimes a lot of data can be meaningless; at other times one single piece of information can be very meaningful.  It is true that a thousand days cannot prove you right, but one day can prove you to be wrong.

Taleb introduces the philosopher Karl Popper and his method of conjectures and refutations.  First you develop a conjecture (hypothesis).  Then you focus on trying to refute the hypothesis.  Taleb:

If you think the task is easy, you will be disappointed—few humans have a natural ability to do this.  I confess that I am not one of them; it does not come naturally to me.

Our natural tendency, whether using System 1 or System 2, is to look only for corroboration.  This is called confirmation bias.

Illustration by intheskies

There are exceptions, notes Taleb.  Chess grand masters tend to look at where their move might be weak, whereas rookie chess players only look for confirmation.  Similarly, George Soros developed a unique ability to look always for evidence that his current hypothesis is wrong.  As a result of this and not getting attached to his opinions, Soros quickly exited many of his trades that wouldn’t have worked.  Soros is one of the most successful macro investors ever.

Taleb observes that seeing a red mini Cooper actually confirms the statement that all swans are white.  Why?  Because if all swans are white, then all nonwhite objects are not swans; in other words, the statement “if it’s a swan, then it’s white” is logically equivalent to the statement “if it’s not white, then it’s not a swan” (since all swans are white).  Taleb:

This argument, known as Hempel’s raven paradox, was rediscovered by my friend the (thinking) mathematician Bruno Dupire during one of our intense meditating walks in London—one of those intense walk-discussions, intense to the point of our not noticing the rain.  He pointed to a red Mini and shouted, “Look, Nassim, look!  No Black Swan!”

Again: Finding instances that confirm the statement “if it’s not white, then it’s not a swan” is logically equivalent to finding instances that confirm the statement “if it’s a swan, then it’s white.”  So consider all the objects that confirm the statement “if it’s not white, then it’s not a swan”:  red Mini’s, gray clouds, green cucumbers, yellow lemons, brown soil, etc.  The paradox is that we seem to gain ever more information about swans by looking at an infinite series of nonwhite objects.

Taleb concludes the chapter by noting that our brains evolved to deal with a much more primitive environment than what exists today, which is far more complex.

…the sources of Black Swans today have multiplied beyond measurability.  In the primitive environment they were limited to newly encountered wild animals, new enemies, and abrupt weather changes.  These events were repeatable enough for us to have built an innate fear of them.  This instinct to make inferences rather quickly, and to “tunnel” (i.e., focus on a small number of sources of uncertainty, or causes of known Black Swans) remains rather ingrained in us.  This instinct, in a word, is our predicament.

 

CHAPTER 6: THE NARRATIVE FALLACY

Taleb introduces the narrative fallacy:

We like stories, we like to summarize, and we like to simplify, i.e., to reduce the dimension of matters… The [narrative] fallacy is associated with our vulnerability to overinterpretation and our predilection for compact stories over raw truths.  It severely distorts our mental representation of the world; it is particularly acute when it comes to the rare event.

Taleb continues:

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.

Taleb clarifies:

To help the reader locate himself: in studying the problem of induction in the previous chapter, we examined what could be inferred about the unseen, what lies outside our information set.  Here, we look at the seen, what lies within the information set, and we examine the distortions in the act of processing it.

Taleb observes that our brains automatically theorize and invent explanatory stories to explain facts.  It takes effort NOT to invent explanatory stories.

Taleb mentions post hoc rationalization.  In an experiment, women were asked to choose from among twelve pairs of nylon stockings the ones they preferred.  Then they were asked for the reasons for their choice.  The women came up with all sorts of explanations.  However, all the stockings were in fact identical.

Photo by Narokzaad

Split-brain patients have no connection between the left and right hemispheres of their brains.  Taleb:

Now, say that you induced such a person to perform an act—raise his finger, laugh, or grab a shovel—in order to ascertain how how he ascribes a reason to his act (when in fact you know that there is no reason for it other than your inducing it).  If you ask the right hemisphere, here isolated from the left side, to perform the action, then ask the other hemisphere for an explanation, the patient will invariably offer some interpretation: “I was pointing at the ceiling in order to…,” “I saw something interesting on the wall”…

Now, if you do the opposite, namely instruct the isolated left hemisphere of a right-handed person to perform an act and ask the right hemisphere for the reasons, you will be plainly told “I don’t know.”

Taleb notes that the left hemisphere deals with pattern recognition.  (But, in general, Taleb warns against the common distinctions between the left brain and the right brain.)

Taleb gives another example.  Read the following:

A BIRD IN THE

THE HAND IS WORTH

TWO IN THE BUSH

Notice anything unusual?  Try reading it again.  Taleb:

The Sydney-based brain scientist Alan Snyder… made the following discovery.  If you inhibit the left hemisphere of a right-handed person (more technically, by directing low-frequency magnetic pulses into the left frontotemporal lobes), you will lower his rate of error in reading the above caption.  Our propensity to impose meaning and concepts blocks our awareness of the details making up the concept.  However, if you zap people’s left hemispheres, they become more realistic—they can draw better and with more verisimilitude.  Their minds become better at seeing the objects themselves, cleared of theories, narratives, and prejudice.

Again, System 1 (intuition) automatically invents explanatory stories.  System 1 automatically finds patterns, even when none exist.

Moreover, neurotransmitters, chemicals thought to transport signals between different parts of the brain, play a role in the narrative fallacy.  Taleb:

It appears that pattern perception increases along with the concentration in the brain of the chemical dopamine.  Dopamine also regulates moods and supplies an internal reward system in the brain (not surprisingly, it is found in slightly higher concentrations in the left side of the brains of right-handed persons than on the right side).  A higher concentration of dopamine appears to lower skepticism and result in greater vulnerability to pattern detection;  an injection of L-dopa, a substance used to treat patients with Parkinson’s disease, seems to increase such activity and lowers one’s suspension of belief.  The person becomes vulnerable to all manner of fads…

Dopamine molecule. Illustration by Liliya623.

Our memory of the past is impacted by the narrative fallacy:

Narrativity can viciously affect the remembrance of past events as follows: we will tend to more easily remember those facts from our past that fit a narrative, while we tend to neglect others that do not appear to play a causal role in that narrative.  Consider that we recall events in our memory all the while knowing the answer of what happened subsequently.  It is literally impossible to ignore posterior information when solving a problem.  This simple inability to remember not the true sequence of events but a reconstructed one will make history appear in hindsight to be far more explainable than it actually was—or is.

Taleb again:

So we pull memories along causative lines, revising them involuntarily and unconsciously.  We continuously renarrate past events in the light of what appears to make what we think of as logical sense after these events occur.

One major problem in trying to explain and predict the facts is that the facts radically underdetermine the hypotheses that logically imply those facts.  For any given set of facts, there exist many theories that can explain and predict those facts.  Taleb:

In a famous argument, the logician W.V. Quine showed that there exist families of logically consistent interpretations and theories that can match a given set of facts.  Such insight should warn us that mere absence of nonsense may not be sufficient to make something true.

There is a way to escape the narrative fallacy.  Develop hypotheses and then run experiments that test those hypotheses.  Whichever hypotheses best explain and predict the phenomena in question can be provisionally accepted.

The best hypotheses are only provisionally true and they are never uniquely true.  The history of science shows that nearly all hypotheses, no matter how well-supported by experiments, end up being supplanted.  Odds are high that the best hypotheses of today—including general relativity and quantum mechanics—will be supplanted at some point in the future.  For example, perhaps string theory will be developed to the point where it can predict the phenomena in question with more accuracy and with more generality than both general relativity and quantum mechanics.

Taleb continues:

Let us see how narrativity affects our understanding of the Black Swan.  Narrative, as well as its associated mechanism of salience of the sensational fact, can mess up our projection of the odds.  Take the following experiment conducted by Kahneman and Tversky… : the subjects were forecasting professionals who were asked to imagine the following scenarios and estimate their odds.

Which is more likely?

    • A massive flood somewhere in America in which more than a thousand people die.
    • An earthquake in California, causing massive flooding, in which more than a thousand people die.

Most of the forecasting professionals thought that the second scenario is more likely than the first scenario.  But logically, the second scenario is a subset of the first scenario and is therefore less likely.  It’s the vividness of the second scenario that makes it appear more likely.  Again, in trying to understand these scenarios, System 1 can much more easily imagine the second scenario and so automatically views it as more likely.

Next Taleb defines two kinds of Black Swan:

…there are two varieties of rare events: a) the narrated Black Swans, those that are present in the current discourse and that you are likely to hear about on television, and b) those nobody talks about, since they escape models—those that you would feel ashamed discussing in public because they do not seem plausible.  I can safely say that it is entirely compatible with human nature that the incidences of Black Swans would be overestimated in the first case, but severely underestimated in the second one.

 

CHAPTER 7: LIVING IN THE ANTECHAMBER OF HOPE

Taleb explains:

Let us separate the world into two categories.  Some people are like the turkey, exposed to a major blowup without being aware of it, while others play reverse turkey, prepared for big events that might surprise others.  In some strategies and life situations, you gamble dollars to win a succession of pennies while appearing to be winning all the time.  In others, you risk a succession of pennies to win dollars.  In other words, you bet either that the Black Swan will happen or that it will never happen, two strategies that require completely different mind-sets.

Taleb adds:

So some matters that belong to Extremistan are extremely dangerous but do not appear to be so beforehand, since they hide and delay their risks—so suckers think they are “safe.”  It is indeed a property of Extremistan to look less risky, in the short run, than it really is.

Illustration by Mariusz Prusaczyk

Taleb describes a strategy of betting on the Black Swan:

…some business bets in which one wins big but infrequently, yet loses small but frequently, are worth making if others are suckers for them and if you have the personal and intellectual stamina.  But you need such stamina.  You also need to deal with people in your entourage heaping all manner of insult on you, much of it blatant.  People often accept that a financial strategy with a small chance of success is not necessarily a bad one as long as the success is large enough to justify it.  For a spate of psychological reasons, however, people have difficulty carrying out such a strategy, simply because it requires a combination of belief, a capacity for delayed gratification, and the willingness to be spat upon by clients without blinking.

 

CHAPTER 8: GIACOMO CASANOVA’S UNFAILING LUCK: THE PROBLEM OF SILENT EVIDENCE

Taleb:

Another fallacy in the way we understand events is that of silent evidence.  History hides both Black Swans and its Black Swan-generating ability from us.

Taleb tells the story of the drowned worshippers:

More than two thousand years ago, the Roman orator, belletrist, thinker, Stoic, manipulator-politician, and (usually) virtuous gentleman, Marcus Tullius Cicero, presented the following story.  One Diagoras, a nonbeliever in the gods, was shown painted tablets bearing the portraits of some worshippers who prayed, then survived a subsequent shipwreck.  The implication was that praying protects you from drowning.  Diagoras asked, “Where were the pictures of those who prayed, then drowned?”

This is the problem of silent evidence.  Taleb again:

As drowned worshippers do not write histories of their experiences (it is better to be alive for that), so it is with the losers in history, whether people or ideas.

Taleb continues:

The New Yorker alone rejects close to a hundred manuscripts a day, so imagine the number of geniuses that we will never hear about.  In a country like France, where more people write books while, sadly, fewer people read them, respectable literary publishers accept one in ten thousand manuscripts they receive from first-time authors.  Consider the number of actors who have never passed an audition but would have done very well had they had that lucky break in life.

Luck often plays a role in whether someone becomes a millionaire or not.  Taleb notes that many failures share the traits of the successes:

Now consider the cemetery.  The graveyard of failed persons will be full of people who shared the following traits: courage, risk taking, optimism, et cetera.  Just like the population of millionaires.  There may be some differences in skills, but what truly separates the two is for the most part a single factor: luck.  Plain luck.

Of course, there’s more luck in some professions than others.  In investment management, there’s a great deal of luck.  One way to see this is to run computer simulations.  You can see that by luck alone, if you start out with 10,000 investors, you’ll end up with a handful of investors who beat the market for 10 straight years.

(Photo by Volodymyr Pyndyk)

Taleb then gives another example of silent evidence.  He recounts reading an article about the growing threat of the Russian Mafia in the United States.  The article claimed that the toughness and brutality of these guys were because they were strengthened by their Gulag experiences.  But were they really strengthened by their Gulag experiences?

Taleb asks the reader to imagine gathering a representative sample of the rats in New York.  Imagine that Taleb subjects these rats to radiation.  Many of the rats will die.  When the experiment is over, the surviving rats will be among the strongest of the whole sample.  Does that mean that the radiation strengthened the surviving rats?  No.  The rats survived because they were stronger.  But every rat will have been weakened by the radiation.

Taleb offers another example:

Does crime pay?  Newspapers report on the criminal who get caught.  There is no section in The New York Times recording the stories of those who committed crimes but have not been caught.  So it is with cases of tax evasion, government bribes, prostitution rings, poisoning of wealthy spouses (with substances that do not have a name and cannot be detected), and drug trafficking.

In addition, our representation of the standard criminal might be based on the properties of those less intelligent ones who were caught.

Taleb next writes about politicians promising “rebuilding” New Orleans after Hurricane Katrina:

Did they promise to do so with the own money?  No.  It was with public money.  Consider that such funds will be taken away from somewhere else… That somewhere else will be less mediatized.  It may be… cancer research… Few seem to pay attention to the victims of cancer lying lonely in a state of untelevised depression.  Not only do these cancer patients not vote (they will be dead by the next ballot), but they do not manifest themselves to our emotional system.  More of them die every day than were killed by Hurricane Katrina; they are the ones who need us the most—not just our financial help, but our attention and kindness.  And they may be the ones from whom the money will be taken—indirectly, perhaps even directly.  Money (public or private) taken away from research might be responsible for killing them—in a crime that may remain silent.

Giacomo Casanova was an adventurer who seemed to be lucky.  However, there have been plenty of adventurers, so some are bound to be lucky.  Taleb:

The reader can now see why I use Casanova’s unfailing luck as a generalized framework for the analysis of history, all histories.  I generate artificial histories featuring, say, millions of Giacomo Casanovas, and observe the difference between the attributes of the successful Casanovas (because you generate them, you know their exact properties) and those an observer of the result would obtain.  From that perspective, it is not a good idea to be a Casanova.

 

CHAPTER 9: THE LUDIC FALLACY, OR THE UNCERTAINTY OF THE NERD

Taleb introduces Fat Tony (from Brooklyn):

He started as a clerk in the back office of a New York bank in the early 1980s, in the letter-of-credit department.  He pushed papers and did some grunt work.  Later he grew into giving small business loans and figured out the game of how you can get financing from the monster banks, how their bureaucracies operate, and what they like to see on paper.  All the while an employee, he started acquiring property in bankruptcy proceedings, buying it from financial institutions.  His big insight is that bank employees who sell you a house that’s not theirs just don’t care as much as the owners; Tony knew very rapidly how to talk to them and maneuver.  Later, he also learned to buy and sell gas stations with money borrowed from small neighborhood bankers.

…Tony’s motto is “Finding who the sucker is.”  Obviously, they are often the banks: “The clerks don’t care about nothing.”  Finding these suckers is second nature to him.

Next Taleb introduces non-Brooklyn John:

Dr. John is a painstaking, reasoned, and gentle fellow.  He takes his work seriously, so seriously that, unlike Tony, you can see a line in the sand between his working time and his leisure activities.  He has a PhD in electrical engineering from the University of Texas at Austin.  Since he knows both computers and statistics, he was hired by an insurance company to do computer simulations; he enjoys the business.  Much of what he does consists of running computer programs for “risk management.”

Taleb imagines asking Fat Tony and Dr. John the same question: Assume that a coin is fair.  Taleb flips the coin ninety-nine times and gets heads each time.  What are the odds that the next flip will be tails?

(Photo by Christian Delbert)

Because he assumes a fair coin and the flips are independent, Dr. John answers one half (fifty percent).  Fat Tony answers, “I’d say no more than 1 percent, of course.”  Taleb questions Fat Tony’s reasoning.  Fat Tony explains that the coin must be loaded.  In other words, it is much more likely that the coin is loaded than that Taleb got ninety-nine heads in a row flipping a fair coin.

Taleb explains:

Simply, people like Dr. John can cause Black Swans outside Mediocristan—their minds are closed.  While the problem is very general, one of its nastiest illusions is what I call the ludic fallacy—the attributes of the uncertainty we face in real life have little connection to the sterilized ones we encounter in exams and games.

Taleb was invited by the United States Defense Department to a brainstorming session on risk.  Taleb was somewhat surprised by the military people:

I came out of the meeting realizing that only military people deal with randomness with genuine, introspective intellectual honesty—unlike academics and corporate executives using other people’s money.  This does not show in war movies, where they are usually portrayed as war-hungry autocrats.  The people in front of me were not the people who initiate wars.  Indeed, for many, the successful defense policy is the one that manages to eliminate potential dangers without war, such as the strategy of bankrupting the Russians through the escalation in defense spending.  When I expressed my amazement to Laurence, another finance person who was sitting next to me, he told me that the military collected more genuine intellects and risk thinkers than most if not all other professions.  Defense people wanted to understand the epistemology of risk.

Taleb notes that the military folks had their own name for a Black Swan: unknown unknown.  Taleb came to the meeting prepared to discuss a new phrase he invented: the ludic fallacy, or the uncertainty of the nerd.

(Photo by Franky44)

In the casino you know the rules, you can calculate the odds, and the type of uncertainty we encounter there, we will see later, is mild, belonging to Mediocristan.  My prepared statement was this: “The casino is the only human venture I know where the probabilities are known, Gaussian (i.e., bell-curve), and almost computable.”…

In real life you do not know the odds; you need to discover them, and the sources of uncertainty are not defined.

Taleb adds:

What can be mathematized is usually not Gaussian, but Mandelbrotian.

What’s fascinating about the casino where the meeting was held is that the four largest losses incurred (or narrowly avoided) had nothing to do with gambling.

    • First, they lost around $100 million when an irreplaceable performer in their main show was maimed by a tiger.
    • Second, a disgruntled contractor was hurt during the construction of a hotel annex.  He was so offended by the settlement offered him that he made an attempt to dynamite the casino.
    • Third, a casino employee didn’t file required tax forms for years.  The casino ended up paying a huge fine (which was the least bad alternative).
    • Fourth, there was a spate of other dangerous scenes, such as the kidnapping of the casino owner’s daughter, which caused him, in order to secure cash for the ransom, to violate gambling laws by dipping into the casino coffers.

Taleb draws a conclusion about the casino:

A back-of-the-envelope calculation shows that the dollar value of these Black Swans, the off-model hits and potential hits I’ve just outlined, swamp the on-model risks by a factor of close to 1,000 to 1.  The casino spent hundreds of millions of dollars on gambling theory and high-tech surveillance while the bulk of their risks came from outside their models.

All this, and yet the rest of the world still learns about uncertainty and probability from gambling examples.

Taleb wraps up Part One of his book:

We love the tangible, the confirmation, the palpable, the real, the visible, the concrete, the known, the seen, the vivid, the visual, the social, the embedded, the emotionally laden, the salient, the stereotypical, the moving, the theatrical, the romanced, the cosmetic, the official, the scholarly-sounding verbiage (b******t), the pompous Gaussian economist, the mathematicized crap, the pomp, the Academie Francaise, Harvard Business School, the Nobel Prize, dark business suits with white shirts and Ferragamo ties, the moving discourse, and the lurid.  Most of all we favor the narrated.

Alas, we are not manufactured, in our current edition of the human race, to understand abstract matters—we need context.  Randomness and uncertainty are abstractions.  We respect what had happened, ignoring what could have happened.

 

PART TWO: WE JUST CAN’T PREDICT

Taleb:

…the gains in our ability to model (and predict) the world may be dwarfed by the increases in its complexity—implying a greater and greater role for the unpredicted.

 

CHAPTER 10: THE SCANDAL OF PREDICTION

Taleb highlights the story of the Sydney Opera House:

The Sydney Opera House was supposed to open in early 1963 at a cost of AU$ 7 million.  It finally opened its doors more than ten years later, and, although it was a less ambitious version than initially envisioned, it ended up costing around AU$ 104 million.

Taleb then asks:

Why on earth do we predict so much?  Worse, even, and more interesting: Why don’t we talk about our record in predicting?  Why don’t we see how we (almost) always miss the big events?  I call this the scandal of prediction.

The problem is that when our knowledge grows, our confidence about how much we know generally increases even faster.

Illustration by Airdone.

Try the following quiz.  For each question, give a range that you are 90 percent confident contains the correct answer.

    • What was Martin Luther King, Jr.’s age at death?
    • What is the length of the Nile river, in miles?
    • How many countries belong to OPEC?
    • How many books are there in the Old Testament?
    • What is the diameter of the moon, in miles?
    • What is the weight of an empty Boeing 747, in pounds?
    • In what year was Mozart born?
    • What is the gestation period of an Asian elephant, in days?
    • What is the air distance from London to Tokyo, in miles?
    • What is the deepest known point in the ocean, in feet?

If you’re not overconfident, then you should have gotten nine out of ten questions right because you gave a 90 percent confidence interval for each question.  However, most people get more than one question wrong, which means most people are overconfident.

(Answers:  39, 4132, 12, 39, 2158.8, 390000, 1756, 645, 5959, 35994.)

A similar quiz is to randomly select some number, like the population of Egypt, and then ask 100 random people to give their 98 percent confidence interval.  “I am 98 percent confident that the population of Egypt is between 40 million and 120 million.”  If the 100 random people are not overconfident, then 98 out of 100 should get the question right.  In practice, however, it turns out that a high number (15 to 30 percent) get the wrong answer.  Taleb:

This experiment has been replicated dozens of times, across populations, professions, and cultures, and just about every empirical psychologist and decision theorist has tried it on his class to show his students the big problem of humankind: we are simply not wise enough to be trusted with knowledge.  The intended 2 percent error rate usually turns out to be between 15 percent and 30 percent, depending on the population and the subject matter.

I have tested myself and, sure enough, failed, even while consciously trying to be humble by carefully setting a wide range… Yesterday afternoon, I gave a workshop in London… I decided to make a quick experiment during my talk.

I asked the participants to take a stab at a range for the number of books in Umberto Eco’s library, which, as we know from the introduction to Part One, contains 30,000 volumes.  Of the sixty attendees, not a single one made the range wide enough to include the actual number (the 2 percent error rate became 100 percent).

Taleb argues that guessing some quantity you don’t know and making a prediction about the future are logically similar.  We could ask experts who make predictions to give a confidence interval and then track over time how accurate their predictions are compared to the confidence interval.

Taleb continues:

The problem is that our ideas are sticky: once we produce a theory, we are not likely to change our minds—so those who delay developing their theories are better off.  When you develop your opinions on the basis of weak evidence, you will have difficulty interpreting subsequent information that contradicts these opinions, even if this new information is obviously more accurate.  Two mechanisms are at play here: …confirmation bias… and belief perseverance [also called consistency bias], the tendency not to reverse opinions you already have.  Remember that we treat ideas like possessions, and it will be hard for us to part with them.

…the more detailed knowledge one gets of empirical reality, the more one will see the noise (i.e., the anecdote) and mistake it for actual information.  Remember that we are swayed by the sensational.

Taleb adds:

…in another telling experiment, the psychologist Paul Slovic asked bookmakers to select from eighty-eight variables in past horse races those that they found useful in computing the odds.  These variables included all manner of statistical information about past performances.  The bookmakers were given the ten most useful variables, then asked to predict the outcome of races.  Then they were given ten more and asked to predict again.  The increase in the information set did not lead to an increase in their accuracy; their confidence in their choices, on the other hand, went up markedly.  Information proved to be toxic.

When it comes to dealing with experts, many experts do have a great deal of knowledge.  However, most experts have a high error rate when it comes to making predictions.  Moreover, many experts don’t even keep track of how accurate their predictions are.

Another way to think about the problem is to try to distinguish those with true expertise from those without it.  Taleb:

    • Experts who tend to be experts: livestock judges, astronomers, test pilots, soil judges, chess masters, physicists, mathematicians (when they deal with mathematical problems, not empirical ones), accountants, grain inspectors, photo interpreters, insurance analysts (dealing with bell curve-style statistics).
    • Experts who tend to be… not experts: stockbrokers, clinical psychologists, psychiatrists, college admissions officers, court judges, councilors, personnel selectors, intelligence analysts… economists, financial forecasters, finance professors, political scientists, “risk experts,” Bank for International Settlements staff, august members of the International Association of Financial Engineers, and personal financial advisors.

Taleb comments:

You cannot ignore self-delusion.  The problem with experts is that they do not know what they do not know.  Lack of knowledge and delusion about the quality of you knowledge come together—the same process that makes you know less also makes you satisfied with your knowledge.

Taleb asserts:

Our predictors may be good at predicting the ordinary, but not the irregular, and this is where they ultimately fail.  All you need to do is miss one interest-rates move, from 6 percent to 1 percent in a longer-term projection (what happened between 2000 and 2001) to have all your subsequent forecasts rendered completely ineffectual in correcting your cumulative track record.  What matters is not how often you are right, but how large your cumulative errors are.

And these cumulative errors depend largely on the big surprises, the big opportunities.  Not only do economic, financial, and political predictors miss them, but they are quite ashamed to say anything outlandish to their clients—and yet events, it turns out, are almost always outlandish.  Furthermore… economic forecasters tend to fall closer to one another than to the resulting outcome.  Nobody wants to be off the wall.

Taleb notes a paper that analyzed two thousand predictions by brokerage-house analysts.  These predictions didn’t predict anything at all.  You could have done about as well by naively extrapolating the prior period to the next period.  Also, the average difference between the forecasts was smaller than the average error of the forecasts.  This indicates herding.

Taleb then writes about the psychologist Philip Tetlock’s research.  Tetlock analyzed twenty-seven thousand predictions by close to three hundred specialists.  The predictions took the form of more of x, no change in x, or less of x.  Tetlock found that, on the whole, these predictions by experts were little better than chance.  You could have done as well by rolling a dice.

Tetlock worked to discover why most expert predictors did not realize that they weren’t good at making predictions.  He came up with several methods of belief defense:

    • You tell yourself that you were playing a different game.  Virtually no social scientist predicted the fall of the Soviet Union.  You argue that the Russians had hidden the relevant information.  If you’d had enough information, you could have predicted the fall of the Soviet Union.  “It is not your skills that are to blame.”
    • You invoke the outlier.  Something happened that was outside the system.  It was a Black Swan, and you are not supposed to predict Black Swans.  Such events are “exogenous,” coming from outside your science.  The model was right, it worked well, but the game turned out to be a different one than anticipated.
    • The “almost right” defense.  Retrospectively, it is easy to feel that it was a close call.

Taleb writes:

We attribute our successes to our skills, and our failures to external events outside our control, namely to randomness… This causes us to think that we are better than others at whatever we do for a living.  Nine-four percent of Swedes believe that their driving skills  put them in the top 50 percent of Swedish drivers; 84 percent of Frenchmen feel that their lovemaking abilities put them in the top half of French lovers.

Taleb observes that we tend to feel a little unique, unlike others.  If we get married, we don’t consider divorce a possibility.  If we buy a house, we don’t think we’ll move.  People who lose their job often don’t expect it.  People who try drugs don’t think they’ll keep doing it for long.

Taleb says:

Tetlock distinguishes between two types of predictors, the hedgehog and the fox, according to a distinction promoted by the essayist Isaiah Berlin.  As in Aesop’s fable, the hedgehog knows one thing, the fox knows many things… Many of the prediction failures come from hedgehogs who are mentally married to a single big Black Swan event, a big bet that is not likely to play out.  The hedgehog is someone focusing on a single, improbable, and consequential event, falling for the narrative fallacy that makes us so blinded by one single outcome that we cannot imagine others.

Taleb makes it clear that he thinks we should be foxes, not hedgehogs.  Taleb has never tried to predict specific Black Swans.  Rather, he wants to be prepared for whatever might come.  That’s why it’s better to be a fox than a hedgehog.  Hedgehogs are much worse, on the whole, at making predictions than foxes are.

Taleb mentions a study by Spyros Makridakis and Michele Hibon of predictions made using econometrics.  They discovered that “statistically sophisticated or complex methods” are not clearly better than simpler ones.

Projects usually take longer and are more expensive than most people think.  For instance, students regularly underestimate how long it will take them to complete a class project.  Taleb then adds:

With projects of great novelty, such as a military invasion, an all-out war, or something entirely new, errors explode upward.  In fact, the more routine the task, the better you learn to forecast.  But there is always something nonroutine in our modern environment.

Taleb continues:

…we are too focused on matters internal to the project to take into account external uncertainty, the “unknown unknown,” so to speak, the contents of the unread books.

Another important bias to understand is anchoring.  The human brain, relying on System 1, will grab on to any number, no matter how random, as a basis for guessing some other quantity.  For example, Kahneman and Tversky spun a wheel of fortune in front of some people.  What the people didn’t know was that the wheel was pre-programmed to either stop at “10” or “65.”  After the wheel stopped, people were asked to write down their guess of the number of African countries in the United Nations.  Predictably, those who saw “10” guessed a much lower number (25% was the average guess) than those who saw “65” (45% was the average guess).

Next, Taleb points out that life expectancy is from Mediocristan.  It is not scalable.  The longer we live, the less long we are expected to live.  By contrast, projects and ventures tend to be scalable.  The longer we have waited for some project to be completed, the longer we can be expected to have to wait from that point forward.

Taleb gives the example of a refugee waiting to return to his or her homeland.  The longer the refugee has waited so far, the longer they should expect to have to wait going forward.  Furthermore, consider wars: they tend to last longer and kill more people than expected.  The average war may last six months, but if your war has been going on for a few years, expect at least a few more years.

Taleb argues that corporate and government projections have an obvious flaw: they do not include an error rate.  There are three fallacies involved:

    • The first fallacy: variability matters.  For planning purposes, the accuracy of your forecast matters much more than the forecast itself, observes Taleb.  Don’t cross a river if it is four feet deep on average.  Taleb gives another example.  If you’re going on a trip to a remote location, then you’ll pack different clothes if it’s supposed to be seventy degrees Fahrenheit with an expected error rate of forty degrees than if the margin of error was only five degrees.  “The policies we need to make decisions on should depend far more on the range of possible outcomes than on the expected final number.”
    • The second fallacy lies in failing to take into account forecast degradation as the projected period lengthens… Our forecast errors have traditionally been enormous…
    • The third fallacy, and perhaps the gravest, concerns a misunderstanding of the random character of the variables being forecast.  Owing to the Black Swan, these variables can accomodate far more optimistic—or far more pessimistic—scenarios than are currently expected.

Taleb points out that, as in the case of the depth of the river, what matters even more than the error rate is the worst-case scenario.

A Black Swan has three attributes: unpredictability, consequences, and retrospective explainability.  Taleb next examines unpredictability.

 

CHAPTER 11: HOW TO LOOK FOR BIRD POOP

Taleb notes that most discoveries are the product of serendipity.

Photo by Marek Uliasz

Taleb writes:

Take this dramatic example of a serendipitous discovery.  Alexander Fleming was cleaning up his laboratory when he found that penicillium mold had contaminated one of his old experiments.  He thus happened upon the antibacterial properties of penicillin, the reason many of us are alive today (including…myself, for typhoid fever is often fatal when untreated)… Furthermore, while in hindsight the discovery appears momentous, it took a very long time for health officials to realize the importance of what they had on their hands.  Even Fleming lost faith in the idea before it was subsequently revived.

In 1965 two radio astronomists at Bell Labs in New Jersey who were mounting a large antenna were bothered by a background noise, a hiss, like the static that you hear when you have bad reception.  The noise could not be eradicated—even after they cleaned the bird excrement out of the dish, since they were convinced that bird poop was behind the noise.  It took a while for them to figure out that what they were hearing was the trace of the birth of the universe, the cosmic background microwave radiation.  This discovery revived the big bang theory, a languishing idea that was posited by earlier researchers.

What’s interesting (but typical) is that the physicists—Ralph Alpher, Hans Bethe, and George Gamow—who conceived of the idea of cosmic background radiation did not discover the evidence they were looking for, while those not looking for such evidence found it.

Furthermore, observes Taleb:

When a new technology emerges, we either grossly underestimate or severely overestimate its importance.  Thomas Watson, the founder of IBM, once predicted that there would be no need for more than just a handful of computers.

Taleb adds:

The laser is a prime illustration of a tool made for a given  purpose (actually no real purpose) that then found applications that were not even dreamed of at the time.  It was a typical “solution looking for a problem.”  Among the early applications was the surgical stitching of detached retinas.  Half a century later, The Economist asked Charles Townes, the alleged inventor of the laser, if he had had retinas on his mind.  He had not.  He was satisfying his desire to split light beams, and that was that.  In fact, Townes’s colleagues teased him quite a bit about the irrelevance of his discovery.  Yet just consider the effects of the laser in the world around you: compact disks, eyesight corrections, microsurgery, data storage and retrieval—all unforeseen applications of the technology.

Taleb mentions that the French mathematician Henri Poincare was aware that equations have limitations when it comes to predicting the future.

Poincare’s reasoning was simple: as you project into the future you may need an increasing amount of precision about the dynamics of the process that you are modeling, since your error rate grows very rapidly… Poincare showed this in a very simple case, famously known as the “three body problem.”  If you have only two planets in a solar-style system, with nothing else affecting their course, then you may be able to indefinitely predict the behavior of these planets, no sweat.  But add a third body, say a comet, ever so small, between the planets.  Initially the third body will cause no drift, no impact; later, with time, its effects on the other two bodies may become explosive.

Our world contains far more than just three bodies.  Therefore, many future phenomena are unpredictable due to complexity.

The mathematician Michael Berry gives another example: billiard balls.  Taleb:

If you know a set of basic parameters concerning the ball at rest, can compute the resistance of the table (quite elementary), and can gauge the strength of the impact, then it is rather easy to predict what would happen at the first hit… The problem is that to correctly predict the ninth impact, you need to take into account the gravitational pull of someone standing next to the table… And to compute the fifty-sixth impact, every single elementary particle of the universe needs to be present in your assumptions!

Moreover, Taleb points out, in the billiard ball example, we don’t have to worry about free will.  Nor have we incorporated relativity and quantum effects.

You can think rigorously, but you cannot use numbers.  Poincare even invented a field for this, analysis in situ, now part of topology…

In the 1960s the MIT meteorologist Edward Lorenz rediscovered Poincare’s results on his own—once again, by accident.  He was producing a computer model of weather dynamics, and he ran a simulation that projected a weather system a few days ahead.  Later he tried to repeat the same simulation with the exact same model and what he thought were the same input parameters, but he got wildly different results… Lorenz subsequently realized that the consequential divergence in his results arose not from error, but from a small rounding in the input parameters.  This became known as the butterfly effect, since a butterfly moving its wings in India could cause a hurricane in New York, two years later.  Lorenz’s findings generated interest in the field of chaos theory.

Much economics has been developed assuming that agents are rational.  However, Kahneman and Tversky have shown—in their work on heuristics and biases—that many people are less than fully rational.  Kahneman and Tversky’s experiments have been repeated countless times over decades.  Some people prefer apples to oranges, oranges to pears, and pears to apples.  These people do not have consistent preferences.  Furthermore, when guessing at an unknown quantity, many people will anchor on any random number even though the random number often has no relation to the quantity guessed at.

People also make different choices based on framing effects.  Kahneman and Tversky have illustrated this with the following experiment in which 600 people are assumed to have a deadly disease.

First Kahneman and Tversky used a positive framing:

    • Treatment A will save 200 lives for sure.
    • Treatment B has a 33% chance of saving everyone and a 67% chance of saving no one.

With this framing, 72% prefer Treatment A and 28% prefer Treatment B.

Next a negative framing:

    • Treatment A will kill 400 people for sure.
    • Treatment B has a 33% chance of killing no one and a 67% chance of killing everyone.

With this framing, only 22% prefer Treatment A, while 78% prefer Treatment B.

Note:  The two frames are logically identical, but the first frame focuses on lives saved, whereas the second frame focuses on lives lost.

Taleb argues that the same past data can confirm a theory and also its exact opposite.  Assume a linear series of points.  For the turkey, that can either confirm safety or it can mean the turkey is much closer to being turned into dinner.  Similarly, as Taleb notes, each day you live can either mean that you’re more likely to be immortal or that you’re closer to death.  Taleb observes that a linear regression model can be enormously misleading if you’re in Extremistan: Just because the data thus far appear to be in a straight line tells you nothing about what’s to come.

Taleb says the philosopher Nelson Goodman calls this the riddle of induction:

Let’s say that you observe an emerald.  It was green yesterday and the day before yesterday.  It is green again today.  Normally this would confirm the “green” property: we can assume that the emerald will be green tomorrow.  But to Goodman, the emerald’s color history could equally confirm the “grue” property.  What is this grue property?  The emerald’s grue property is to be green until some specified date… and then blue thereafter.

The riddle of induction is another version of the narrative fallacy—you face an infinity of “stories” that explain what you have seen.  The severity of Goodman’s riddle of induction is as follows: if there is no longer even a single unique way to “generalize” from what you see, to make an inference about the unknown, then how should you operate?  The answer, clearly, will be that you should employ “common sense,” but your common sense may not be so well developed with respect to some Extremistan variables.

 

CHAPTER 12: EPISTEMOCRACY, A DREAM

Taleb defines an epistemocrat as someone who is keenly aware that his knowledge is suspect.  Epistemocracy is a place where the laws are made with human fallibility in mind.  Taleb says that the major modern epistemocrat is the French philosopher Michel de Montaigne.

Montaigne is quite refreshing to read after the strains of a modern education since he fully accepted human weaknesses and understood that no philosophy could be effective unless it took into account our deeply ingrained imperfections, the limitations of our rationality, the flaws that make us human.  It is not that he was ahead of his time; it would be better said that later scholars (advocating rationality) were backward.

Photo by Jacek Dudzinski

Montaigne was not just a thinker, but also a doer.  He had been a magistrate, a businessman, and the mayor of Bordeaux.  Taleb writes that Montaigne was a skeptic, an antidogmatist.

So what would an epistemocracy look like?

The Black Swan asymmetry allows you to be confident about what is wrong, not about what you believe is right.

Taleb adds:

The notion of future mixed with chance, not a deterministic extension of your perception of the past, is a mental operation that our mind cannot perform.  Chance is too fuzzy for us to be a category by itself.  There is an asymmetry between past and future, and it is too subtle for us to understanding naturally.

The first consequence of this asymmetry is that, in people’s minds, the relationship between the past and the future does not learn from the relationship between the past and the past previous to it.  There is a blind spot: when we think of tomorrow we do not frame it in terms of what we thought about yesterday or the day before yesterday.  Because of this introspective defect we fail to learn about the difference between our past predictions and the subsequent outcomes.  When we think of tomorrow, we just project it as another yesterday.

As yet another example of how we can’t predict, psychologists have shown that we can’t predict our future affective states in response to both pleasant and unpleasant events.  The point is that we don’t learn from our past errors in predicting our future affective states.  We continue to make the same mistake by overestimating the future impact of both pleasant and unpleasant events.  We persist in thinking that unpleasant events will make us more unhappy than they actually do.  We persist in thinking that pleasant events will make us happier than they actually do.  We simply don’t learn from the fact that we made these erroneous predictions in the past.

Next Taleb observes that it’s not only that we can’t predict the future.  We don’t know the past either.  Taleb gives this example:

    • Operation 1 (the melting ice cube): Imagine an ice cube and consider how it may melt over the next two hours while you play a few rounds of poker with your friends.  Try to envision the shape of the resulting puddle.
    • Operation 2 (where did the water come from?): Consider a puddle of water on the floor.  Now try to reconstruct in your mind’s eye the shape of the ice cube it may once have been.  Note that the puddle may not have necessarily originated from an ice cube.

It’s one thing to use physics and engineering to predict the forward process of an ice cube melting.  It’s quite another thing to try to reconstruct what it was that led to a puddle of water.  Taleb:

In a way, the limitations that prevent us from unfrying an egg also prevent us from reverse engineering history.

For these reasons, history should just be a collection of stories, argues Taleb.  History generally should not try to discover the causes of why things happened the way they did.

 

CHAPTER 13: APELLES THE PAINTER, OR WHAT DO YOU DO IF YOU CANNOT PREDICT?

Taleb writes about being a fool in the right places:

The lesson for the small is: be human!  Accept that being human involves some amount of epistemic arrogance in running your affairs.  Do not be ashamed of that.  Do not try to always withhold judgment—opinions are the stuff of life.  Do not try to avoid predicting—yes, after this diatribe about prediction I am not urging you to stop being a fool.  Just be a fool in the right places.

What you should avoid is unnecessary dependence on large-scale harmful predictions—those and only those.  Avoid the big subjects that may hurt your future: be fooled in small matters, not in the large.  Do not listen to economic forecasters or to predictors in social science (they are mere entertainers), but do make your own forecast for the picnic…

Know how to rank beliefs not according to their plausibility but by the harm they may cause.

Taleb advises us to maximize the serendipity around us.  You want to be exposed to the positive accident.  Taleb writes that Sextus Empiricus retold a story about Apelles the Painter.  Try as he might, Apelles was not able to paint the foam on a horse’s mouth.  He really tried hard but eventually gave up.  In irritation, he took a sponge and threw it at the painting.  The sponge left a pattern on the painting that perfectly depicted the foam.

(Illustration by Ileezhun)

Taleb recommends trial and error:

Indeed, we have psychological and intellectual difficulties with trial and error, and with accepting that series of small failures are necessary in life.  My colleague Mark Spitznagel understood that we humans have a mental hang-up about failures:  “You need to love to lose” was his motto.  In fact, the reason I felt immediately at home in America is precisely because American culture encourages the process of failure, unlike the cultures of Europe and Asia where failure is met with stigma and embarrassment.  America’s specialty is to take these small risks for the rest of the world, which explains this country’s disproportionate share in innovations.

Taleb then points out:

People are often ashamed of losses, so they engage in strategies that produce very little volatility but contain the risk of a large loss—like collecting nickles in front of steamrollers.

Taleb recommends a barbell strategy.  You put 85 to 90 percent of your money in extremely safe instruments like U.S. Treasury bills.

The remaining 10 to 15 percent you put in extremely speculative bets, as leveraged as possible (like options), preferably venture capital-style portfolios.

Taleb offers five tricks:

    • Make a distinction between positive contingencies and negative ones.  There are both positive and negative Black Swans.
    • Do not be narrow-minded.  Do not try to predict precise Black Swans because that tends to make you more vulnerable to the ones you didn’t predict.  Invest in preparedness, not in prediction.
    • Seize any opportunity, or anything that looks like opportunity.  They are rare, much rarer than you think.  Work hard, not in grunt work, but in chasing such opportunities and maximizing exposure to them.
    • Beware of precise plans by governments.
    • Do not waste your time trying to fight forecasters, stock analysts, economists, and social scientists.  People will continue to predict foolishly, especially if they are paid for it.

Taleb concludes:

All these recommendations have one point in common: asymmetry.  Put yourself in situations where favorable consequences are much larger than unfavorable ones.

Taleb explains:

This idea that in order to make a decision you need to focus on the consequences (which you can know) rather than the probability (which you can’t know) is the central idea of uncertainty.  Much of my life is based on it.

 

PART THREE: THOSE GRAY SWANS OF EXTREMISTAN

The final four items related to the Black Swan:

    • The world is moving deeper into Extremistan.
    • The Gaussian bell curve is a contagious and severe delusion.
    • Using Mandelbrotian, or fractal, randomness, some Black Swans can by turned into Gray Swans.
    • Some ideas about uncertainty.

 

CHAPTER 14: FROM MEDIOCRISTAN TO EXTREMISTAN, AND BACK

The economist Sherwin Rosen wrote in the early 1980s about “the economics of superstars.”  Think about some of the best professional athletes or actors earning hundreds of millions of dollars.

According to Rosen, this inequality comes from a tournament effect: someone who is marginally “better” can easily win the entire pot, leaving the others with nothing…

But the role of luck is missing in Rosen’s beautiful argument.  The problem here is the notion of “better,” this focus on skills as leading to success.  Random outcomes, or an arbitrary situation, can also explain success, and provide the initial push that leads to a winner-take-all result.  A person can get slightly ahead for entirely random reasons; because we like to imitate one another, we will flock to him.  The world of contagion is so underestimated!

As I am writing these lines, I am using a Macintosh, by Apple, after years of using Microsoft-based products.  The Apple technology is vastly better, yet the inferior software won the day.  How?  Luck.

Taleb next mentions the Matthew effect, according to which people take from the poor to give to the rich.  Robert K. Merton looked at the performance of scientists and found that an initial advantage would tend to follow someone through life.  The theory also can apply to companies, businessmen, actors, writers, and anyone else who benefits from past success.

Taleb observes that the vast majority of the largest five hundred U.S. corporations have eventually either shrunk significantly or gone out of business.  Why?  Luck plays a large role.

Photo by Pat Lalli

Taleb:

Capitalism is, among other things, the revitalization of the world thanks to the opportunity to be lucky.  Luck is the grand equalizer, because almost everyone can benefit from it…

Everything is transitory.  Luck both made and unmade Carthage; it both made and unmade Rome.

I said earlier that randomness is bad, but it is not always so.  Luck is far more egalitarian than even intelligence.  If people were rewarded strictly according to their abilities, things would still be unfair—people don’t choose their abilities.  Randomness has the beneficial effect of reshuffling society’s cards, knocking down the big guy.

 

CHAPTER 15: THE BELL CURVE, THAT GREAT INTELLECTUAL FRAUD

The Gaussian bell curve, also called the normal distribution, describes many things, including height.  Taleb presents the following data about height.  First, he assumes that the average height (men and women) is 1.67 meters, (about 5 feet 7 inches).  Then look at the following increments and consider the odds of someone being that tall:

  • 10 centimeters taller than the average (1.77 m, or 5 feet 10): 1 in 6.3
  • 20 centimeters taller than the average (1.87 m, or 6 feet 2): 1 in 44
  • 30 centimeters taller than the average (1.97 m, or 6 feet 6): 1 in 740
  • 40 centimeters taller than the average (2.07 m, or 6 feet 9): 1 in 32,000
  • 50 centimeters taller than the average (2.17 m, or 7 feet 1): 1 in 3,500,000
  • 60 centimeters taller than the average (2.27 m, or 7 feet 5): 1 in 1,000,000,000
  • 70 centimeters taller than the average (2.37 m, or 7 feet 9): 1 in 780,000,000,000
  • 80 centimeters taller than the average (2.47 m, or 8 feet 1): 1 in 1,600,000,000,000,000
  • 90 centimeters taller than the average (2.57 m, or 8 feet 5): 1 in 8,900,000,000,000,000,000
  • 100 centimeters taller than the average (2.67 m, or 8 feet 9): 1 in 130,000,000,000,000,000,000,000
  • 110 centimeters taller than the average (2.77 m, or 9 feet 1): 1 in 36,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000

Taleb comments that the super fast decline is what allows you to ignore outliers in a normal distribution (the bell curve).

By contrast, consider the odds of being rich in Europe:

    • People with a net worth higher than 1 million: 1 in 63
    • Higher than 2 million: 1 in 250
    • Higher than 4 million: 1 in 1,000
    • Higher than 8 million: 1 in 4,000
    • Higher than 16 million: 1 in 16,000
    • Higher than 32 million: 1 in 64,000
    • Higher than 320 million: 1 in 6,400,000

The important point is that for this Mandelbrotian distribution, the speed of the decrease remains constant.

(Power law graph, via Wikimedia Commons)

Taleb:

This, in a nutshell, illustrates the difference between Mediocristan and Extremistan.

Taleb writes:

Consider this effect.  Take a random sample of any two people from the U.S. population who jointly earn $1 million per annum.  What is the most likely breakdown of their respective incomes?  In Mediocristan, the most likely combination is half a million each.  In Extremistan, it would be $50,000 and $950,000.

The situation is even more lopsided with book sales.  If I told you that two authors sold a total of a million copies of their books, the most likely combination is 993,000 copies sold for one and 7,000 for the other.  This is far more likely than that the books each sold 500,000 copies…

Why is this so?  The height problem provides a comparison.  If I told you that the total height of two people is fourteen feet, you would identify the most likely breakdown as seven feet each, not two feet and twelve feet; not even eight feet and six feet!

Taleb summarizes:

Although unpredictable large deviations are rare, they cannot be dismissed as outliers because, cumulatively, their impact is so dramatic.

The traditional Gaussian way of looking at the world begins by focusing on the ordinary, and then deals with exceptions or so-called outliers as ancillaries.  But there is a second way, which takes the exceptional as a starting point and treats the ordinary as subordinate.

Taleb continues by noting that if there are strong forces bringing things back into equilibrium, then you can use the Gaussian approach.  (That’s why much of economics is based on equilibrium.)  If there is a rational reason for the largest not to be too far from the average, then again you can use the Gaussian approach.  If there are physical limitations preventing very large observations, once again the Gaussian approach works.

Another example of where the Gaussian approach works is a cup of coffee.  There are several trillion particles in a coffee cup.  But for the cup of coffee to jump off a table, all the particles would have to jump in the same direction.  That’s not going to happen in the lifetime of this universe, notes Taleb.

Taleb explains that the Gaussian family is the only class of distributions for which the average and the standard deviation are sufficient to describe.  Moreover, correlation and regression have little or no significance outside of the Gaussian.  Taleb observes that correlation and standard deviation can be very unstable and can depend largely on which historical periods you look at.

The French mathematician Poincare was suspicious of the Gaussian, writes Taleb.

Poincare wrote that one of his friends, an unnamed “eminent physicist,” complained to him that physicists tended to use the Gaussian curve because they thought mathematicians believed it a mathematical necessity; mathematicians used it because they believed that physicists found it to be an empirical fact.

Taleb adds:

If you’re dealing with qualitative inference, such as in psychology or medicine, looking for yes/no answers to which magnitudes don’t apply, then you can assume you’re in Mediocristan without serious problems.  The impact of the improbable cannot be too large.  You have cancer or you don’t, you are pregnant or you are not, et cetera… But if you are dealing with aggregates, where magnitudes do matter, such as income, your wealth, return on a portfolio, or book sales, then you will have a problem and get the wrong distribution if you use the Gaussian, as it does not belong there.  One single number can disrupt all your averages; one single loss can eradicate a century of profits.

 

CHAPTER 16: THE AESTHETICS OF RANDOMNESS

Taleb:

Fractality is the repetition of geometric patterns at different scales, revealing smaller and smaller versions of themselves.

Taleb explains:

There is no qualitative change when an object changes size.  If you look at the coast of Britain from an airplane, it resembles what you see when you look at it with a magnifying glass.  This character of self-affinity implies that one deceptively short and simple rule of iteration can be used, either by a computer or, more randomly, by Mother Nature, to build shapes of seemingly great complexity… Mandelbrot designed the mathematical object now known as the Mandelbrot set, the most famous object in the history of mathematics.  It became popular with followers of chaos theory because it generates pictures of ever increasing complexity by using a deceptively minuscule recursive rule; recursive means that something can be reapplied to itself infinitely.  You can look at the set at smaller and smaller resolutions without ever reaching the limit; you will continue to see recognizable shapes.  The shapes are never the same, yet they bear an affinity to one another, a strong family resemblance.

Taleb notes that most computer-generated objects are based on some version of the Mandelbrotian fractal.  Taleb writes of Benoit Mandelbrot:

His talks were invaded by all sorts of artists, earning him the nickname the Rock Star of Mathematics.  The computer age helped him become one of the most influential mathematicians in history, in terms of the applications of his work, way before his acceptance by the ivory tower.  We will see that, in addition to its universality, his work offers an unusual attribute: it is remarkably easy to understand.

Let’s consider again Mediocristan.  Taleb:

I am looking at the rug in my study.  If I examine it with a microscope, I will see a very rugged terrain.  If I look at it with a magnifying glass, the terrain will be smoother but still highly uneven.  But when I look at it from a standing position, it appears uniform—it is almost as smooth as a sheet of paper.  The rug at eye level corresponds to Mediocristan and the law of large numbers: I am seeing the sum of undulations, and these iron out.  This is like Gaussian randomness: the reason my cup of coffee does not jump is that the sum of all of its moving particles becomes smooth.  Likewise, you reach certainties by adding up small Gaussian uncertainties: this is the law of large numbers.

The Gaussian is not self-similar, and that is why my coffee cup does not jump on my desk.

How does fractal geometry relate to things like the distribution of wealth, the size of cities, returns in the financial markets, the number of casualties in war, or the size of planets?  Taleb:

The key here is that the fractal has numerical or statistical measures that are (somewhat) preserved across scales—the ratio is the same, unlike the Guassian.

Taleb argues that fractals can make Black Swans gray:

Fractals should be the default, the approximation, the framework.  They do not solve the Black Swan problem and do not turn all Black Swans into predictable events, but they significantly mitigate the Black Swan problem by making such large events conceivable.

Taleb continues:

I have shown in the wealth lists in Chapter 15 the logic of a fractal distribution: if wealth doubles from 1 million to 2 million, the incidence of people with at least that much money is cut in four, which is an exponent of two.  If the exponent were one, then the incidence of that wealth or more would be cut in two.  The exponent is called the “power” (which is why some people use the term power law).

Taleb presents the following table with the assumed exponents (powers) for various phenomena:

Phenomenon Assumed Exponent (vague approximation)
Frequency of use of words 1.2
Number of hits on websites 1.4
Number of books sold in the U.S. 1.5
Telephone calls received 1.22
Magnitude of earthquakes 2.8
Diameter of moon craters 2.14
Intensity of solar flares 0.8
Intensity of wars 0.8
Net worth of Americans 1.1
Number of persons per family name 1
Population of U.S. cities 1.3
Market moves 3 (or lower)
Company size 1.5
People killed in terrorist attacks 2 (but possibly a much lower exponent)

Note that these exponents (powers) are best guesses on the basis of statistical information.  It’s often hard to know the true parametersif they exist.  Also note that you will have a huge sampling error.  Finally, note that, because of the way the math works (you use the negative of the exponent), a lower power implies greater deviations.

Taleb observes:

My colleagues and I worked with around 20 million pieces of financial data.  We all had the same data set, yet we never agreed on exactly what the exponent was in our sets.  We knew the data revealed a fractal power law, but we learned that one could not produce a precise number.  But what we did knowthat the distribution is scalable and fractalwas sufficient for us to operate and make decisions.

 

CHAPTER 17: LOCKE’S MADMEN, OR BELL CURVES IN THE WRONG PLACES

Taleb laments the fact that Gaussian tools are still widely used even when they don’t apply to the phenomena in question:

The strangest thing is that people in business usually agree with me when they listen to me talk or hear me make my case.  But when they go to the office the next day they revert to the Gaussian tools so entrenched in their habits.  Their minds are domain-dependent, so they can exercise critical thinking at a conference while not doing so in the office.  Furthermore, the Gaussian tools give them numbers, which seem to be “better than nothing.”  The resulting measure of future uncertainty satisfies our ingrained desire to simplify even if that means squeezing into one single number matters that are too rich to be described that way.

Taleb later describes how various researchers disputed Taleb’s main points:

People would find data in which there were no jumps or extreme events, and show me a “proof” that one could use the Gaussian.  [This is like observing O.J. Simpson and concluding he’s not a killer because you never saw him kill someone while you were observing him.]  The entire statistical business confused absence of proof with proof of absence.  Furthermore, people did not understand the elementary asymmetry involved: you need one single observation to reject the Gaussian, but millions of observations will not fully confirm the validity of its application.  Why?  Because the Gaussian bell curve disallows large deviations, but tools of Extremistan, the alternative, do not disallow long quiet stretches.

The hedge fund Long-Term Capital Management, or LTCM, was founded by people considered to be geniuses, including Nobel winners Robert Merton, Jr., and Myron Scholes.  LTCM, using Gaussian methods, claimed that it had very sophisticated ways of measuring risk.  According to their Gaussian models, they had virtually no real risk.  Then LTCM blew up.  A Black Swan.

Taleb comments:

The magnitude of the losses was spectacular, too spectacular to allow us to ignore the intellectual comedy.  Many friends and I though that the portfolio theorists would suffer the fate of tobacco companies: they were endangering people’s savings and would soon be brought to account for the consequences of their Gaussian-inspired methods.

None of that happened.

Instead, MBAs in business schools went on learning portfolio theory.  And the option formula went on bearing the name Black-Scholes-Merton, instead of reverting to its true owners, Louis Bachelier, Ed Thorp, and others.

Despite the overwhelming evidence that Gaussian assumptions do not apply to past financial data, many researchers continue to make Gaussian assumptions.  Taleb says this resembles Locke’s definition of a madman: someone “reasoning correctly from erroneous premises.”

Taleb asserts that military people focus first on having realistic assumptions.  Only later do they focus on correct reasoning.

This is where you learn from the minds of military people and those who have responsibilities in security.  They do not care about “perfect” ludic reasoning; they want realistic ecological assumptions.  In the end, they care about lives.

 

CHAPTER 18: THE UNCERTAINTY OF THE PHONY

People like to refer to the uncertainty principle and then talk about the limits of our knowledge.  However, uncertainties about subatomic particles are very small and very numerous.  They average out, says Taleb: They obey the law of large numbers and they are Gaussian.

Taleb writes about trying to visit his ancestral village of Amioun, Lebanon:

Beirut’s airport is closed owing to the conflict between Israel and the Shiite militia Hezbollah.  There is no published airline schedule that will inform me when the war will end, if it ends.  I can’t figure out if my house will be standing, if Amioun will still be on the maprecall that the family house was destroyed once before.  I can’t figure out if the war is going to degenerate into something even more severe.  Looking into the outcome of the war, with all my relatives, friends, and property exposed to it, I face true limits of knowledge.  Can someone explain to me why I should care about subatomic particles that, anyway, converge to a Gaussian?  People can’t predict how long they will be happy with recently acquired objects, how long their marriages will last, how their new jobs will turn out, yet it’s subatomic particles that they cite as “limits of prediction.”  They’re ignoring a mammoth standing in front of them in favor of matter even a microscope would not allow them to see.

 

PART FOUR: THE END

CHAPTER 19: HALF AND HALF, OR HOW TO GET EVEN WITH THE BLACK SWAN

Taleb concludes:

Half the time I am a hyperskpetic; the other half I hold certainties and can be intransigent about them, with a very stubborn disposition… I am skeptical when I suspect wild randomness, gullible when I believe that randomness is mild.

Half the time I hate Black Swans, the other half I love them.  I like the randomness that produces the texture of life, the positive accidents, the success of Apelles the painter, the potential gifts you do not have to pay for.  Few understand the beauty in the story of Apelles; in fact, most people exercise their error avoidance by repressing the Apelles in them.

Taleb continues:

In the end this is a trivial decision making rule: I am very aggressive when I can gain exposure to positive Black Swanswhen a failure would be of small momentand very conservative when I am under threat from a negative Black Swan.  I am very aggressive when an error in a model can benefit me, and paranoid when the error can hurt.  This may not be too interesting except that it is exactly what other people do not do…

Half the time I am intellectual, the other half I am a no-nonsense practitioner.  I am no-nonsense and practical in academic matters, and intellectual when it comes to practice.

Half the time I am shallow, the other half I want to avoid shallowness.  I am shallow when it comes to aesthetics; I avoid shallowness in the context of risks and returns.  My aestheticism makes me put poetry before prose, Greeks before Romans, dignity before elegance, elegance before culture, culture before erudition, erudition before knowledge, knowledge before intellect, and intellect before truth.  But only for matters that are Black Swan free.  Our tendency is to be very rational, except when it comes to the Black Swan.

Taleb’s final points:

We are quick to forget that just being alive is an extraordinary piece of good luck, a remote event, a chance occurrence of monstrous proportions.

Imagine a speck of dust next to a planet a billion times the size of the earth.  The speck of dust represents the odds in favor of your being born; the huge planet would be the odds against it.  So stop sweating the small stuff… remember that you are a Black Swan.

 

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:  http://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.

Fooled by Randomness

(Image:  Zen Buddha Silence by Marilyn Barbone.)

October 20, 2019

Nassim Nicholas Taleb’s Fooled by Randomness: The Hidden Role of Chance in the Markets and in Life, is an excellent book.  Below I summarize the main points.

Here’s the outline:

    • Prologue

Part I: Solon’s Warning—Skewness, Asymmetry, and Induction

    • One: If You’re So Rich, Why Aren’t You So Smart?
    • Two: A Bizarre Accounting Method
    • Three: A Mathematical Meditation on History
    • Four: Randomness, Nonsense, and the Scientific Intellectual
    • Five: Survival of the Least Fit—Can Evolution Be Fooled By Randomness?
    • Six: Skewness and Asymmetry
    • Seven: The Problem of Induction

Part II: Monkeys on Typewriters—Survivorship and Other Biases

    • Eight: Too Many Millionaires Next Door
    • Nine: It Is Easier to Buy and Sell Than Fry an Egg
    • Ten: Loser Takes All—On the Nonlinearities of Life
    • Eleven: Randomness and Our Brain—We Are Probability Blind

Part III: Wax in my Ears—Living With Randomitis

    • Twelve: Gamblers’ Ticks and Pigeons in a Box
    • Thirteen: Carneades Comes to Rome—On Probability and Skepticism
    • Fourteen: Bacchus Abandons Antony

(Albrecht Durer’s Wheel of Fortune from Sebastien Brant’s Ship of Fools (1494) via Wikimedia Commons)

 

PROLOGUE

Taleb presents Table P.1 Table of Confusion, listing the central distinctions used in the book.

GENERAL

Luck Skills
Randomness Determinism
Probability Certainty
Belief, conjecture Knowledge, certitude
Theory Reality
Anecdote, coincidence Causality, law
Forecast Prophecy

MARKET PERFORMANCE

Lucky idiot Skilled investor
Survivorship bias Market outperformance

FINANCE

Volatility Return (or drift)
Stochastic variable Deterministic variable

PHYSICS AND ENGINEERING

Noise Signal

LITERARY CRITICISM

None Symbol

PHILOSOPHY OF SCIENCE

Epistemic probability Physical probability
Induction Deduction
Synthetic proposition Analytic proposition

 

ONE: IF YOU’RE SO RICH, WHY AREN’T YOU SO SMART?

Taleb introduces an options trader Nero Tulip.  He became convinced that being an options trader was even more interesting that being a pirate would be.

Nero is highly educated (like Taleb himself), with an undergraduate degree in ancient literature and mathematics from Cambridge University, a PhD. in philosophy from the University of Chicago, and a PhD. in mathematical statistics.  His thesis for the PhD. in philosophy had to do with the methodology of statistical inference in its application to the social sciences.  Taleb comments:

In fact, his thesis was indistinguishable from a thesis in mathematical statistics—it was just a bit more thoughtful (and twice as long).

Nero left philosophy because he became bored with academic debates, particularly over minor points.  Nero wanted action.

(Photo by Neil Lockhart)

Nero became a proprietary trader.  The firm provided the capital.  As long as Nero generated good results, he was free to work whenever he wanted.  Generally he was allowed to keep between 7% and 12% of his profits.

It is paradise for an intellectual like Nero who dislikes manual work and values unscheduled meditation.

Nero was an extremely conservative options trader.  Over his first decade, he had almost no bad years and his after-tax income averaged $500,000.  Due to his extreme risk aversion, Nero’s goal is not to maximize profits as much as it is to avoid having such a bad year that his “entertaining money machine called trading” would be taken away from him.  In other words, Nero’s goal was to avoid blowing up, or having such a bad year that he would have to leave the business.

Nero likes taking small losses as long as his profits are large.  Whereas most traders make money most of the time during a bull market and lose money during market panics or crashes, Nero would lose small amounts most of the time during a bull market and then make large profits during a market panic or crash.

Nero does not do as well as some other traders.  One reason is that his extreme risk aversion leads him to invest his own money in treasury bonds.  So he missed most of the bull market from 1982 to 2000.

Note: From a value investing point of view, Nero should at least have invested in undervalued stocks, since such a strategy will almost certainly do well after 10+ years.  But Nero wasn’t trained in value investing, and he was acutely aware of what can happen during market panics or crashes.

Also Note:  For a value investor, a market panic or crash is an opportunity to buy more stock at very cheap prices.  Thus bear markets benefit the value investor who can add to his or her positions.

Nero and his wife live across the street from John the High-Yield Trader and his wife.  John was doing much better than Nero.  John’s strategy was to maximize profits for as long as the bull market lasted.  Nero’s wife and even Nero himself would occasionally feel jealous when looking at the much larger house in which John and his wife lived.  However, one day there was a market panic and John blew up, losing virtually everything including his house.

Taleb writes:

…Nero’s merriment did not come from the fact that John went back to his place in life, so much as it was from the fact that Nero’s methods, beliefs, and track record had suddenly gained in credibility.  Nero would be able to raise public money on his track record precisely because such a thing could not possibly happen to him.  A repetition of such an event would pay off massively for him.  Part of Nero’s elation also came from the fact that he felt proud of his sticking to his strategy for so long, in spite of the pressure to be the alpha male.  It was also because he would no longer question his trading style when others were getting rich because they misunderstood the structure of randomness and market cycles.

Taleb then comments that lucky fools never have the slightest suspicion that they are lucky fools.  As long as they’re winning, they get puffed up from the release of the neurotransmitter serotonin into their systems.  Taleb notes that our hormonal system can’t distinguish between winning based on luck and winning based on skill.

(A lucky seven.  Photo by Eagleflying)

Furthermore, when serotonin is released into our system based on some success, we act like we deserve the success, regardless of whether it was based on luck or skill.  Our new behavior will often lead to a virtuous cycle during which, if we continue to win, we will rise in the pecking order.  Similarly, when we lose, whether that loss is due to bad luck or poor skill, our resulting behavior will often lead to a vicious cycle during which, if we continue to lose, we will fall in the pecking order.  Taleb points out that these virtuous and vicious cycles are exactly what happens with monkeys who have been injected with serotonin.

Taleb adds that you can always tell whether some trader has had a winning day or a losing day.  You just have to observe his or her gesture or gait.  It’s easy to tell whether the trader is full of serotonin or not.

Photo by Antoniodiaz

 

TWO: A BIZARRE ACCOUNTING METHOD

Taleb introduces the concept of alternative histories.  This concept applies to many areas of human life, including many different professions (war, politics, medicine, investments).  The main idea is that you cannot judge the quality of a decision based only on its outcome.  Rather, the quality of a decision can only be judged by considering all possible scenarios (outcomes) and their associated probabilities.

Once again, our brains deceive us unless we develop the habit of thinking probabilistically, in terms of alternative histories.  Without this habit, if a decision is successful, we get puffed up with serotonin and believe that the successful outcome is based on our skill.  By nature, we cannot account for luck or randomness.

Taleb offers Russian roulette as an analogy.  If you are offered $10 million to play Russian roulette, and if you play and you survive, then you were lucky even though you will get puffed up with serotonin.

Photo by Banjong Khanyai

Taleb argues that many (if not most) business successes have a large component of luck or randomness.  Again, though, successful businesspeople in general will be puffed up with serotonin and they will attribute their success primarily to skill.  Taleb:

…the public observes the external signs of wealth without even having a glimpse at the source (we call such source the generator).

Now, if the lucky Russian roulette player continues to play the game, eventually the bad histories will catch up with him or her.  Here’s an important point:  If you start out with thousands of people playing Russian roulette, then after the first round roughly 83.3% will be successful.  After the second round, roughly 83.3% of the survivors of round one will be successful.  After the third round, roughly 83.3% of the survivors of round two will be successful.  And on it goes…  After twenty rounds, there will be a small handful of extremely successful and wealthy Russian roulette players.  However, these cases of extreme success are due entirely to luck.

In the business world, of course, there are many cases where skill plays a large role.  The point is that our brains by nature are unable to see when luck has played a role in some successful outcome.  And luck almost always plays an important role in most areas of life.

Taleb points out that there are some areas where success is due mostly to skill and not luck.  Taleb likes to give the example of dentistry.  The success of a dentist will typically be due mostly to skill.

Taleb attributes some of his attitude towards risk to the fact that at one point he had a boss who forced him to consider every possible scenario, no matter how remote.

Interestingly, Taleb understands Homer’s The Iliad as presenting the following idea: heroes are heroes based on heroic behavior and not based on whether they won or lost.  Homer seems to have understood the role of chance (luck).

 

THREE: A MATHEMATICAL MEDITATION ON HISTORY

A Monte Carlo generator creates many alternative random sample paths.  Note that a sample path can be deterministic, but our concern here is with random sample paths.  Also note that some random sample paths can have higher probabilities than other random sample paths.  Each sample path represents just one sequence of events out of many possible sequences, ergo the word “sample”.

Taleb offers a few examples of random sample paths.  Consider the price of your favorite technology stock, he says.  It may start at $100, hit $220 along the way, and end up at $20.  Or it may start at $100 and reach $145, but only after touching $10.  Another example might be your wealth during at a night at the casino.  Say you begin with $1,000 in your pocket.  One possibility is that you end up with $2,200, while another possibility is that you end up with only $20.

Photo by Emily2k

Taleb says:

My Monta Carlo engine took me on a few interesting adventures.  While my colleagues were immersed in news stories, central bank announcements, earnings reports, economic forecasts, sports results and, not least, office politics, I started toying with it in fields bordering my home base of financial probability.  A natural field of expansion for the amateur is evolutionary biology… I started simulating populations of fast mutating animals called Zorglubs under climactic changes and witnessing the most unexpected of conclusions… My aim, as a pure amateur fleeing the boredom of business life, was merely to develop intuitions for these events… I also toyed with molecular biology, generating randomly occurring cancer cells and witnessing some surprising aspects to their evolution.

Taleb continues:

Naturally the analogue to fabricating populations of Zorglubs was to simulate a population of “idiotic bull”, “impetuous bear”, and “cautious” traders under different market regimes, say booms and busts, and to examine their short-term and long-term survival… My models showed almost nobody to really ultimately make money; bears dropped out like flies in the rally and bulls got ultimately slaughtered, as paper profits vanished when the music stopped.  But there was one exception; some of those who traded options (I called them option buyers) had remarkable staying power and I wanted to be one of those.  How?  Because they could buy insurance against the blowup; they could get anxiety-free sleep at night, thanks to the knowledge that if their careers were threatened, it would not be owing to the outcome of a single day.

Note from a value investing point of view

A value investor seeks to pay low prices for stock in individual businesses.  Stock prices can jump around in the short term.  But over time, if the business you invest in succeeds, then the stock will follow, assuming you bought the stock at relatively low prices.  Again, if there’s a bear market or a market crash, and if the stock prices of the businesses in which you’ve invested decline, then that presents a wonderful opportunity to buy more stock at attractively low prices.  Over time, the U.S. and global economy will grow, regardless of the occasional market panic or crash.  Because of this growth, one of the lowest risk ways to build wealth is to invest in businesses, either on an individual basis if you’re a value investor or via index funds.

Taleb’s methods of trying to make money during a market panic or crash will almost certainly do less well over the long term than simple index funds.

Taleb makes a further point: The vast majority of people learn only from their own mistakes, and rarely from the mistakes of others.  Children only learn that the stove is hot by getting burned.  Adults are largely the same way: We only learn from our own mistakes.  Rarely do we learn from the mistakes of others.  And rarely do we heed the warnings of others.  Taleb:

All of my colleagues whom I have known to denigrate history blew up spectacularly—and I have yet to encounter some such person who has not blown up.

Keep in mind that Taleb is talking about traders here.  For a regular investor who dollar cost averages into index funds and/or who uses value investing, Taleb’s warning does not apply.  As a long-term investor in index funds and/or in value investing techniques, you do have to be ready for a 50% decline at some point.  But if you buy more after such a decline, your long-term results will actually be helped, not hurt, by a 50% decline.

Taleb points out that aged traders and investors are likely better to use as role models precisely because they have been exposed to markets longer.  Taleb:

I toyed with Monte Carlo simulations of heterogeneous populations of traders under a variety of regimes (closely resembling historical ones), and found a significant advantage in selecting aged traders, using, as a selection criterion their cumulative years of experience rather than their absolute success (conditional on their having survived without blowing up).

Taleb also observes that there is a similar phenomenon in mate selection.  All else equal, women prefer to mate with healthy older men over healthy younger ones.  Healthy older men, by having survived longer, show some evidence of better genes.

 

FOUR: RANDOMNESS, NONSENSE, AND THE SCIENTIFIC INTELLECTUAL

Using a random generator of words, it’s possible to create rhetoric, but it’s not possible to generate genuine scientific knowledge.

 

FIVE: SURVIVAL OF THE LEAST FIT—CAN EVOLUTION BE FOOLED BY RANDOMNESS?

Taleb writes about Carlos “the emerging markets wizard.”  After excelling as an undergraduate, Carlos went for a PhD. in economics from Harvard.  Unable to find a decent thesis topic for his dissertation, he settled for a master’s degree and a career on Wall Street.

Carlos did well investing in emerging markets bonds.  One important reason for his success, beyond the fact that he bought emerging markets bonds that later went up in value, was that he bought the dips.  Whenever there was a momentary panic and emerging markets bonds dropped in value, Carlos bought more.  This dip buying improved his performance.  Taleb:

It was the summer of 1998 that undid Carlos—that last dip did not translate into a rally.  His track record today includes just one bad quarter—but bad it was.  He had earned close to $80 million cumulatively in his previous years.  He lost $300 million in just one summer.

When the market first started dipping, Carlos learned that a New Jersey hedge fund was liquidating, including its position in Russian bonds.  So when Russian bonds dropped to $52, Carlos was buying.  To those who questioned his buying, he yelled: “Read my lips: it’s li-qui-da-tion!”

Taleb continues:

By the end of June, his trading revenues for 1998 had dropped from up $60 million to up $20 million.  That made him angry.  But he calculated that should the market rise back to the pre-New Jersey selloff, then he would be up $100 million.  That was unavoidable, he asserted.  These bonds, he said, would never, ever trade below $48.  He was risking so little, to possibly make so much.

Then came July.  The market dropped a bit more.  The benchmark Russian bond was now $43.  His positions were under water, but he increased his stakes.  By now he was down $30 million for the year.  His bosses were starting to become nervous, but he kept telling them that, after all, Russia would not go under.  He repeated the cliche that it was too big to fail.  He estimated that bailing them out would cost so little and would benefit the world economy so much that it did not make sense to liquidate his inventory now.

Carlos asserted that the Russian bonds were trading near default value.  If Russia were to default, then Russian bonds would stay at the same prices they were at currently.  Carlos took the further step of investing half of his net worth, then $5,000,000, into Russian bonds.

Russian bond prices then dropped into the 30s, and then into the 20s.  Since Carlos thought the bonds could not be less than the default values he had calculated, and were probably worth much more, he was not alarmed.  He maintained that anyone who invested in Russian bonds at these levels would realize wonderful returns.  He claimed that stop losses “are for schmucks!  I am not going to buy high and sell low!”  He pointed out that in October 1997 they were way down, but that buying the dip ended up yielding excellent profits for 1997.  Furthermore, Carlos pointed out that other banks were showing even larger losses on their Russian bond positions.  Taleb:

Towards the end of August, the bellwether Russian Principal Bonds were trading below $10.  Carlos’s net worth was reduced by almost half.  He was dismissed.  So was his boss, the head of trading.  The president of the bank was demoted to a “newly created position”.  Board members could not understand why the bank had so much exposure to a government that was not paying its own employees—which, disturbingly, included armed soldiers.  This was one of the small points that emerging market economists around the globe, from talking to each other so much, forgot to take into account.

Taleb adds:

Louie, a veteran trader on the neighboring desk who suffered much humiliation by these rich emerging market traders, was there, vindicated.  Louie was then a 52-year-old Brooklyn-born-and-raised trader who over three decades survived every single conceivable market cycle.

Taleb concludes that Carlos is a gentleman, but a bad trader:

He has all of the traits of a thoughtful gentleman, and would be an ideal son-in-law.  But he has most of the attributes of the bad trader.  And, at any point in time, the richest traders are often the worst traders.  This, I will call the cross-sectional problem: at a given time in the market, the most profitable traders are likely to be those that are best fit to the latest cycle.

Taleb discusses John the high-yield trader, who was mentioned near the beginning of the book, as another bad trader.  What traits do bad traders, who may be lucky idiots for awhile, share?  Taleb:

    • An overestimation of the accuracy of their beliefs in some measure, either economic (Carlos) or statistical (John).  They don’t consider that what they view as economic or statistical truth may have been fit to past events and may no longer be true.
    • A tendency to get married to positions.
    • The tendency to change their story.
    • No precise game plan ahead of time as to what to do in the event of losses.
    • Absence of critical thinking expressed in absence of revision of their stance with “stop losses”.
    • Denial.

 

SIX: SKEWNESS AND ASYMMETRY

Taleb presents the following Table:

Event Probability Outcome Expectation
A 999/1000 $1 $.999
B 1/1000 -$10,000 -$10.00
Total -$9.001

The point is that the frequency of losing cannot be considered apart from the magnitude of the outcome.  If you play the game, you’re extremely likely to make $1.  But it’s not a good idea to play.  If you play this game millions of times, you’re virtually guaranteed to lose money.

Taleb comments that even professional investors misunderstand this bet:

How could people miss such a point?  Why do they confuse probability and expectation, that is, probability and probability times the payoff?  Mainly because much of people’s schooling comes from examples in symmetric environments, like a coin-toss, where such a difference does not matter.  In fact the so-called “Bell Curve” that seems to have found universal use in society is entirely symmetric.

(Coin toss.  Photo by Christian Delbert)

Taleb gives an example where he is shorting the S&P 500 Index.  He thought the market had a 70% chance of going up and a 30% chance of going down.  But he thought that if the market went down, it could go down a lot.  Therefore, it was profitable over time (by repeating the bet) to be short the S&P 500.

Note: From a value investing point of view, no one can predict what the market will do.  But you can predict what some individual businesses are likely to do.  The key is to invest in businesses when the price (stock) is low.

Rare Events

Taleb explains his trading strategy:

The best description of my lifelong business in the market is “skewed bets”, that is, I try to benefit from rare events, events that do not tend to repeat themselves frequently, but, accordingly, present a large payoff when they occur.  I try to make money infrequently, as infrequently as possible, simply because I believe that rare events are not fairly valued, and that the rarer the event, the more undervalued it will be in price.

Illustration by lqoncept

Taleb gives an example where his strategy paid off:

One such rare event is the stock market crash of 1987, which made me as a trader and allowed me the luxury of becoming involved in all manner of scholarship.

Taleb notes that in most areas of science, it is common practice to discard outliers when computing the average.  For instance, a professor calculating the average grade in his or her class might discard the highest and the lowest values.  In finance, however, it is often wrong to discard the extreme outcomes because, as Taleb has shown, the magnitude of an extreme outcome can matter.

Taleb advises studying market history.  But then again, you have to be careful, as Taleb explains:

Sometimes market data becomes a simple trap; it shows you the opposite of its nature, simply to get you to invest in the security or mismanage your risks.  Currencies that exhibit the largest historical stability, for example, are the most prone to crashes…

Taleb notes the following:

In other words history teaches us that things that never happened before do happen.

History does not always repeat.  Sometimes things change.  For instance, today the U.S. stock market seems high.  The S&P 500 Index is over 3,000.  Based on history, one might expect a bear market and/or a recession.  There hasn’t been a recession in the U.S. since 2009.

However, with interest rates low, and with the profit margins on many technology companies high, it’s possible that stocks will not decline much, even if there’s a recession.  It’s also possible that any recession could be delayed, partly because the Fed and other central banks remain very accommodative.  It’s possible that the business cycle itself may be less volatile because the fiscal and monetary authorities have gotten better at delaying recessions or at making recessions shallower than before.

Ironically, to the extent that Taleb seeks to profit from a market panic or crash, for the reasons just mentioned, Taleb’s strategy may not work as well going forward.

Taleb introduces the problem of stationarity.  To illustrate the problem, think of an urn with red balls and black balls in it.  Taleb:

Think of an urn that is hollow at the bottom.  As I am sampling from it, and without my being aware of it, some mischievous child is adding balls of one color or another.  My inference thus becomes insignificant.  I may infer that the red balls represent 50% of the urn while the mischievous child, hearing me, would swiftly replace all the red balls with black ones.  This makes much of our knowledge derived through statistics quite shaky.

The very same effect takes place in the market.  We take past history as a single homogeneous sample and believe that we have considerably increased our knowledge of the future from the observation of the sample of the past.  What if vicious children were changing the composition of the urn?  In other words, what if things have changed?

Taleb notes that there are many techniques that use past history in order to measure risks going forward.  But to the extent that past data are not stationary, depending upon these risk measurement techniques can be a serious mistake.  All of this leads to a more fundamental issue: the problem of induction.

 

SEVEN: THE PROBLEM OF INDUCTION

Taleb quotes the Scottish philosopher David Hume:

No amount of observations of white swans can allow the inference that all swans are white, but the observation of a single black swan is sufficient to refute that conclusion.

(Black swan.  Photo by Damithri)

Taleb came to believe that Sir Karl Popper had an important answer to the problem of induction.  According to Popper, there are only two types of scientific theories:

    • Theories that are known to be wrong, as they were tested and adequately rejected (i.e., falsified).
    • Theories that have not yet been known to be wrong, not falsified yet, but are exposed to be proved wrong.

It also follows that we should not always rely on statistics.  Taleb:

More practically to me, Popper had many problems with statistics and statisticians.  He refused to blindly accept the notion that knowledge can always increase with incremental information—which is the foundation for statistical inference.  It may in some instances, but we do not know which ones.  Many insightful people, such as John Maynard Keynes, independently reached the same conclusions.  Sir Karl’s detractors believe that favorably repeating the same experiment again and again should lead to an increased comfort with the notion that “it works”.

Taleb explains the concept of an open society:

Popper’s falsificationism is intimately connected to the notion of an open society.  An open society is one in which no permanent truth is held to exist; this would allow counterideas to emerge.

For Taleb, a successful trader or investor must have an open mind in which no permanent truth is held to exist.

Taleb concludes the chapter by applying the logic of Pascal’s wager to trading and investing:

…I will use statistics and inductive methods to make aggressive bets, but I will not use them to manage my risks and exposure.  Surprisingly, all the surviving traders I know seem to have done the same.  They trade on ideas based on some observation (that includes past history) but, like the Popperian scientists, they make sure that the costs of being wrong are limited (and their probability is not derived from past data).  Unlike Carlos and John, they know before getting involved in the trading strategy which events would prove their conjecture wrong and allow for it (recall the Carlos and John used past history both to make their bets and measure their risk).

 

PART II: MONKEYS ON TYPEWRITERS—SURVIVORSHIP AND OTHER BIASES

If you put an infinite number of monkeys in front of typewriters, it is certain that one of them will type an exact version of Homer’s The Iliad.  Taleb asks:

Now that we have found that hero among monkeys, would any reader invest his life’s savings on a bet that the monkey would write The Odyssey next?

Infinite number of monkeys on typewriters.  Illustration by Robert Adrian Hillman.

 

EIGHT: TOO MANY MILLIONAIRES NEXT DOOR

Taleb begins the chapter by describing a lawyer named Marc.  Marc makes $500,000 a year.  He attended Harvard as an undergraduate and then Yale Law School.  The problem is that some of Marc’s neighbors are much wealthier.  Taleb discusses Marc’s wife, Janet:

Every month or so, Janet has a crisis… Why isn’t her husband so successful?  Isn’t he smart and hard working?  Didn’t he get close to 1600 on the SAT?  Why is Ronald Something whose wife never even nods to Janet worth hundred of millions when her husband went to Harvard and Yale and has such a high I.Q., and has hardly any substantial savings?

Note: Warren Buffett and Charlie Munger have long made the point that envy is a massively stupid sin because, unlike other sins (e.g., gluttony), you can’t have any fun with it.  Granted, envy is a very human emotion.  But we can and must train ourselves not to fall into it.

Daniel Kahneman and others have demonstrated that the average person would rather make $70,000 as long as his neighbor makes $60,000 than make $80,000 if his neighbor makes $90,000.  How stupid to compare ourselves to people who happen to be doing better!  There will always be someone doing better.

Taleb mentions the book, The Millionaire Next Door.  One idea from the book is that the wealthy often do not look wealthy because they’re focused on saving and investing, rather than on spending.  However, Taleb finds two problems with the book.  First, the book does not adjust for survivorship bias.  In other words, for at least some of the wealthy, there is some luck involved.  Second, there’s the problem of induction.  If you measure someone’s wealth in the year 2000 (Taleb was writing in 2001), at the end of one of the biggest bull markets in modern history (from 1982 to 2000), then in many cases a large degree of that wealth came as a result of the prolonged bull market.  By contrast, if you measure people’s wealth in 1982, there would be fewer people who are millionaires, even after adjusting for inflation.

 

NINE: IT IS EASIER TO BUY AND SELL THAN FRY AN EGG

Taleb writes about going to the dentist and being confident that his dentist knows something about teeth.  Later, Taleb goes to Carnegie Hall.  Before the pianist begins her performance, Taleb has zero doubt that she knows how to play the piano and is not about to produce cacophony.  Later still, Taleb is in London and ends up looking at some of his favorite marble statues.  Once again, he knows they weren’t produced by luck.

However, in many areas of business and even more so when it comes to investing, luck does tend to play a large role.  Taleb is supposed to meet with a fund manager who has a good track record and who is looking for investors.  Taleb comments that buying and selling, which is what the fund manager does, is easier than frying an egg.  The problem is that luck plays such a large role in almost any good investment track record.

Photo by Alhovik

In order to study the role luck plays for investors, Taleb suggests a hypothetical game.  There are 10,000 investors at the beginning.  In the first round, a fair coin is tossed for each investor.  Heads, and the investor makes $10,000, tails, and the investor loses $10,000.  (Any investor who has a losing year is not allowed to continue to play the game.)  After the first round, there will be about 5,000 successful investors.  In the second round, a fair coin is again tossed.  After the second round, there will be 2,500 successful investors.  Another round, and 1,250 will remain.  A fourth round, and 625 successful investors will remain.  A fifth round, and 313 successful investors will remain.  Based on luck alone, after five years there will be approximately 313 investors with winning track records.  No doubt these 313 winners will be puffed up with serotonin.

Taleb then observes that you can play the same hypothetical game with bad investors.  You assume each year that there’s a 45% chance of winning and a 55% chance of losing.  After one year, 4,500 successful (but bad) investors will remain.  After two years, 2,025.  After three years, 911.  After four years, 410.  After five years, there will be 184 bad investors who have successful track records.

Taleb makes two counterintuitive points:

    • First, even starting with only bad investors, you will end up with a small number of great track records.
    • Second, how many great track records you end up with depends more on the size of the initial sample—how many investors you started with—than it does on the individual odds per investor.  Applied to the real world, this means that if there are more investors who start in 1997 than in 1993, then you will see a greater number of successful track records in 2002 than you will see in 1998.

Taleb concludes:

Recall that the survivorship bias depends on the size of the initial population.  The information that a person made money in the past, just by itself, is neither meaningful nor relevant.  We need to know that size of the population from which he came.  In other words, without knowing how many managers out there have tried and failed, we will not be able to assess the validity of the track record.  If the initial population includes ten managers, then I would give the performer half my savings without a blink.  If the initial population is composed of 10,000 managers, I would ignore the results.

The mysterious letter

Taleb tells a story.  You get a letter on Jan. 2 informing you that the market will go up during the month.  It does.  Then you get a letter on Feb. 1 saying the market will go down during the month.  It does.  You get another letter on Mar. 1.  Same story.  Again for April and for May.  You’ve now gotten five letters in a row predicting what the market would do during the ensuing month, and all five letters were correct.  Next you are asked to invest in a special fund.  The fund blows up.  What happened?

The trick is as follows.  The con operator gets 10,000 random names.  On Jan. 2, he mails 5,000 letters predicting that the market will go up and 5,000 letters predicting that the market will go down.  The next month, he focuses only on the 5,000 names who were just mailed a correct prediction.  He sends 2,500 letters predicting that the market will go up and 2,500 letters predicting that the market will go down.  Of course, next he focuses on the 2,500 letters which gave correct predictions.  He mails 1,250 letters predicting a market rise and 1,250 predicting a market fall.  After five months of this, there will be approximately 200 people who received five straight correct predictions.

Taleb suggests the birthday paradox as an intuitive way to explain the data mining problem.  If you encounter a random person, there is a one in 365.25 chance that you have the same birthday.  But if you have 23 random people in a room, the odds are close to 50 percent that you can find two people who share a birthday.

Similarly, what are the odds that you’ll run into someone you know in a totally random place?  The odds are quite high because you are testing for any encounter, with any person you know, in any place you will visit.

Taleb continues:

What is your probability of winning the New Jersey lottery twice?  One in 17 trillion.  Yet it happened to Evelyn Adams, whom the reader might guess should feel particularly chosen by destiny.  Using the method we developed above, Harvard’s Percy Diaconis and Frederick Mosteller estimated at 30 to 1 the probability the someone, somewhere, in a totally unspecified way, gets so lucky!

What is data snooping?  It’s looking  at historical data to determine the hypothetical performance of a large number of trading rules.  The more trading rules you examine, the more likely you are to find trading rules that would have worked in the past and that one might expect to work in the future.  However, many such trading rules would have worked in the past based on luck alone.

Taleb next writes about companies that increase their earnings.  The same logic can be applied.  If you start out with 10,000 companies, then by luck 5,000 will increase their profits after the first year.  After three years, there will be 1,250 “stars” that increased their profits for three years in a row.  Analysts will rate these companies a “strong buy”.  The point is not that profit increases are entirely due to luck.  The poin, rather, is that luck often plays a significant role in business results, usually far more than is commonly supposed.

 

TEN: LOSER TAKES ALL—ONE THE NONLINEARITIES OF LIFE

Taleb writes:

This chapter is about how a small advantage in life can translate into a highly disproportionate payoff, or, more viciously, how no advantage at all, but a very, very small help from randomness, can lead to a bonanza.

Nonlinearity is when a small input can lead to a disproportionate response.  Consider a sandpile.  You can add many grains of sand with nothing happening.  Then suddenly one grain of sand causes an avalanche.

(Photo by Maocheng)

Taleb mentions actors auditioning for parts.  A handful of actors get certain parts, and a few of them become famous.  The most famous actors are not always the best actors (although they often are).  Rather, there could have been random (lucky) reasons why a handful of actors got certain parts and why a few of them became famous.

The QWERTY keyboard is not optimal.  But so many people were trained on it, and so many QWERTY keyboards were manufactured, that it has come to dominate.  This is called a path dependent outcome.  Taleb comments:

Such ideas go against classical economic models, in which results either come from a precise reason (there is no account for uncertainty) or the good guy wins (the good guy is the one who is more skilled and has some technical superiority)… Brian Arthur, an economist concerned with nonlinearities at the Santa Fe Institute, wrote that chance events coupled with positive feedback rather than technological superiority will determine economic superiority—not some abstrusely defined edge in a given area of expertise.  While early economic models excluded randomness, Arthur explained how “unexpected orders, chance meetings with lawyers, managerial whims… would help determine which ones achieved early sales and, over time, which firms dominated”.

Taleb continues by noting that Arthur suggests a mathematical model called the Polya process:

The Polya process can be presented as follows: assume an urn initially containing equal quantities of black and red balls.  You are to guess each time which color you will pull out before you make the draw.  Here the game is rigged.  Unlike a conventional urn, the probability of guessing correctly depends on past success, as you get better or worse at guessing depending on past performance.  Thus the probability of winning increases after past wins, that of losing increases after past losses.  Simulating such a process, one can see a huge variance of outcomes, with astonishing successes and a large number of failures (what we called skewness).

 

ELEVEN: RANDOMNESS AND OUR BRAIN—WE ARE PROBABILITY BLIND

Our genes have not yet evolved to the point where our brains can naturally compute probabilities.  Computing probabilities is not something we even needed to do until very recently.

Here’s a diagram of how to compute the probability of A, conditional on B having happened:

(Diagram by Oleg Alexandrov, via Wikimedia Commons)

Taleb:

We are capable of sending a spacecraft to Mars, but we are incapable of having criminal trials managed by the basic laws of probability—yet evidence is clearly a probabilistic notion…

People who are as close to being criminal as probability laws can allow us to infer (that is with a confidence that exceeds the shadow of a doubt) are walking free because of our misunderstanding of basic concepts of the odds… I was in a dealing room with a TV set turned on when I saw one of the lawyers arguing that there were at least four people in Los Angeles capable of carrying O.J. Simpson’s DNA characteristics (thus ignoring the joint set of events…).  I then switched off the television set in disgust, causing an uproar among the traders.  I was under the impression until then that sophistry had been eliminated from legal cases thanks to the high standards of republican Rome.  Worse, one Harvard lawyer used the specious argument that only 10% of men who brutalize their wives go on to murder them, which is a probability unconditional on the murder… Isn’t the law devoted to the truth?  The correct way to look at it is to determine the percentage of murder cases where women were killed by their husband and had previously been battered by him (that is, 50%)—for we are dealing with what is called conditional probabilities; the probability that O.J. killed his wife conditional on the information of her having been killed, rather than the unconditional probability of O.J. killing his wife.  How can we expect the untrained person to understand randomness when a Harvard professor who deals and teaches the concept of probabilistic evidence can make such an incorrect statement?

Speaking of people misunderstanding probabilities, Daniel Kahneman and Amos Tversky have asked groups to answer the following question:

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 is more probable?

    1. Linda is a bank teller.
    2. Linda is a bank teller and is active in the feminist movement.

The majority of people believe that 2. is more probable the 1.  But that’s an obvious fallacy.  Bank tellers who are also feminists is a subset of all bank tellers, therefore 1. is more probable than 2.  To see why, consider the following diagram:

(By svjo, via Wikimedia Commons)

B represents ALL bank tellers.  Out of ALL bank tellers, some are feminists and some are not.  Those bank tellers that are also feminists is represented by A.

Here’s a probability question that was presented to doctors:

A test of a disease presents a rate of 5% false positives.  The disease strikes 1/1,000 of the population.  People are tested at random, regardless of whether they are suspected of having the disease.  A patient’s test is positive.  What is the probability of the patient being stricken with the disease?

Many doctors answer 95%, which is wildly incorrect.  The answer is close to 2%.  Less than one in five doctors get the question right.

To see the right answer, assume that there are no false negatives.  Out of 1,000 patients, one will have the disease.  Consider the remaining 999.  50 of them will test positive.  The probability of being afflicted with the disease for someone selected at random who tested positive is the following ratio:

Number of afflicted persons  /  Number of true and false positives

So the answer is 1/51, about 2%.

Another example where people misunderstand probabilities is when it comes to valuing options.  (Recall that Taleb is an options trader.)  Taleb gives an example.  Say that the stock price is $100 today.  You can buy a call option for $1 that gives you the right to buy the stock at $110 any time during the next month.  Note that the option is out-of-the-money because you would not gain if you exercised your right to buy now, given that the stock is $100, below the exercise price of $110.

Now, what is the expected value of the option?  About 90 percent of out-of-the-money options expire worthless, that is, they end up being worth $0.  But the expected value is not $0 because there is a 10 percent chance that the option could be worth, say $10, because the stock went to $120.  So even though it is 90 percent likely that the option will end up being worth $0, the expected value is not $0.  The actual expected value in this example is:

(90% x $0) + (10% x $10) = $0 + $1 = $1

The expected value of the option is $1, which means you would have paid a fair price if you had bought it for $1.  Taleb notes:

I discovered very few people who accepted losing $1 for most expirations and making $10 once in a while, even if the game were fair (i.e., they made the $10 more than 10% of the time).

“Fair” is not the right term here.  If you make $10 more than 10% of the time, then the game has a positive expected value.  That means if you play the game repeatedly, then eventually over time you will make money.  Taleb’s point is that even if the game has a positive expected value, very few people would like to play it because on your way to making money, you have to accept small losses most of the time.

Taleb distinguishes between premium sellers, who sell options, and premium buyers, who buy options.  Following the same logic as above, premium sellers make small amounts of money roughly 90% of the time, and then take a big loss roughly 10% of the time.  Premium buyers lose small amounts about 90% of the time, and then have a big gain about 10% of the time.

Is it better to be an option seller or an option buyer?  It depends on whether you can find favorable odds.  It also depends on your temperament.  Most people do not like taking small losses most of the time.  Taleb:

Alas, most option traders I encountered in my career are premium sellers—when they blow up it is generally other people’s money.

 

PART III: WAX IN MY EARS—LIVING WITH RANDOMITIS

Taleb writes that when Odysseus and his crew encountered the sirens, Odysseus had his crew put wax in their ears.  He also instructed his crew to tie him to the mast.  With these steps, Odysseus and crew managed to survive the sirens’ songs.  Taleb notes that he would be not Odysseus, but one of the sailors who needed to have wax in his ears.

(Odysseus and crew at the sirens.  Illustration by Mr1805)

Taleb admits that he is dominated by his emotions:

The epiphany I had in my career in randomness came when I understood that I was not intelligent enough, nor strong enough, to even try to fight my emotions.  Besides, I believe that I need my emotions to formulate my ideas and get the energy to execute them.

I am just intelligent enough to understand that I have a predisposition to be fooled by randomness—and to accept the fact that I am rather emotional.  I am dominated by my emotions—but as an aesthete, I am happy about that fact.  I am just like every single character whom I ridiculed in this book… The difference between myself and those I ridicule is that I try to be aware of it.  No matter how long I study and try to understand probability, my emotions will respond to a different set of calculations, those that my unintelligent genes want me to handle.

Taleb says he has developed tricks in order to handle his emotions.  For instance, if he has financial news playing on the television, he keeps the volume off.  Without volume, a babbling person looks ridiculous.  This trick helps Taleb stay free of news that is not rationally presented.

 

TWELVE: GAMBLERS’ TICKS AND PIGEONS IN A BOX

Early in his career as a trader, Taleb says he had a particularly profitable day.  It just so happens that the morning of this day, Taleb’s cab driver dropped him off in the wrong location.  Taleb admits that he was superstitious.  So the next day, he not only wore the same tie, but he had his cab driver drop him off in the same wrong location.

(Skinner boxes.  Photo by Luis Dantas, via Wikimedia Commons)

B.F. Skinner did an experiment with famished pigeons.  There was a mechanism that would deliver food to the box in which the hungry pigeon was kept.  But Skinner programmed the mechanism to deliver the food randomly.  Taleb:

He saw quite astonishing behavior on the part of the birds; they developed an extremely sophisticated rain-dance type of behavior in response to their ingrained statistical machinery.  One bird swung its head rhythmically against a specific corner of the box, others spun their heads anti-clockwise; literally all of the birds developed a specific ritual that progressively became hard-wired into their mind as linked to their feeding.

Taleb observes that whenever we experience two events, A and B, our mind automatically looks for a causal link even though there often is none.  Note: Even if B always follows A, that doesn’t prove a causal link, as Hume pointed out.

Taleb again admits that after he has calculated the probabilities in some situation, he finds it hard to modify his own conduct accordingly.  He gives an example of trading.  Taleb says if he is up $100,000, there is a 98% chance that it’s just noise.  But if he is up $1,000,000, there is a 1% chance that it’s noise and a 99% chance that his strategy is profitable.  Taleb:

A rational person would act accordingly in the selection of strategies, and set his emotions in accordance with his results.  Yet I have experienced leaps of joy over results that I knew were mere noise, and bouts of unhappiness over results that did not carry the slightest degree of statistical significance.  I cannot help it…

Taleb uses another trick to deal with this.  He denies himself access to his performance report unless it hits a predetermined threshold.

 

THIRTEEN: CARNEADES COMES TO ROME—ON PROBABILITY AND SKEPTICISM

Taleb writes:

Carneades was not merely a skeptic; he was a dialectician, someone who never committed himself to any of the premises from which he argued, or to any of the conclusions he drew from them.  He stood all his life against arrogant dogma and belief in one sole truth.  Few credible thinkers rival Carneades in their rigorous skepticism (a class that would include the medieval Arab philosopher Al Gazali, Hume, and Kant—but only Popper came to elevate his skepticism to an all-encompassing scientific methodology).  As the skeptics’ main teaching was that nothing could be accepted with certainty, conclusions of various degrees of probability could be formed, and these supplied a guide to conduct.

Taleb holds that Cicero engaged in probabilistic reasoning:

He preferred to be guided by probability than allege with certainty—very handy, some said, because it allowed him to contradict himself.  This may be a reason for us, who have learned from Popper how to remain self critical, to respect him more, as he did not hew stubbornly to an opinion for the mere fact that he had voiced it in the past.

Taleb asserts that the speculator George Soros has a wonderful ability to change his opinions rather quickly.  In fact, without this ability, Soros could not have become so successful as a speculator.  There are many stories about Soros holding one view strongly, only to abandon it very quickly and take the opposite view, leading to a large profit where there otherwise would have been a large loss.

Most of us tend to become married to our favorite ideas.  Most of us are not like George Soros.  Especially after we have invested time and energy into developing some idea.

At the extreme, just imagine a scientist who spent years developing some idea.  Many scientists in that situation have a hard time abandoning their idea, even after there is good evidence that they’re wrong.  That’s why it is said that science evolves from funeral to funeral.

 

FOURTEEN: BACCHUS ABANDONS ANTONY

Taleb refers to C.P. Cavafy’s poem, Apoleipein o Theos Antonion (The God Abandons Antony).  The poem addresses Antony after he has been defeated.  Taleb comments:

There is nothing wrong and undignified with emotions—we are cut to have them.  What is wrong is not following the heroic, or at least, the dignified path.  That is what stoicism means.  It is the attempt by man to get even with probability.

Seneca 4 BC-65 AD Roman stoic philosopher, statesman, and tutor to the future Emperor Nero.  Photo by Bashta.

Taleb concludes with some advice (stoicism):

Dress at your best on your execution day (shave carefully); try to leave a good impression on the death squad by standing erect and proud.  Try not to play victim when diagnosed with cancer (hide it from others and only share the information with the doctor—it will avert the platitudes and nobody will treat you like  a victim worthy of their pity; in addition the dignified attitude will make both defeat and victory feel equally heroic).  Be extremely courteous to your assistant when you lose money (instead of taking it out on him as many of the traders whom I scorn routinely do).  Try not to blame others for your fate, even if they deserve blame.  Never exhibit any self pity, even if your significant other bolts with the handsome ski instructor or the younger aspiring model.  Do not complain… The only article Lady Fortuna has no control over is your behavior.

 

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:  http://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.

Heads, I win; tails, I don’t lose much!

(Image:  Zen Buddha Silence by Marilyn Barbone.)

October 13, 2019

Value investor Mohnish Pabrai wrote The Dhandho Investor: The Low-Risk Value Method to High Returns (Wiley, 2007).  It’s an excellent book that captures the essence of value investing:

The lower the price you pay relative to the probable intrinsic value of the business, the higher your returns will likely be if you’re right and the lower your losses will likely be if you’re wrong.

If you have a good investment process as a value investor—whether it’s quantitative and statistical, or it involves stock-picking—then typically you’ll be right on about 60 percent of the positions.  Because losses are minimized on the other 40 percent, the portfolio is likely to do well over time.

Mohnish sums up the Dhandho approach as:

Heads, I win;  tails, I don’t lose much!

There is one very important additional idea that Mohnish focused on in his recent (October 2016) lecture at Peking University (Guanghua School of Management):

10-BAGGERS TO 100-BAGGERS

A 10-bagger is an investment that goes up 10x after you buy it.  A 100-bagger is an investment that goes up 100x after you buy it.  Mohnish gives many examples of stocks—a few of which he kept holding and many of which he sold—that later became 10-baggers, 20-baggers, up to a few 100-baggers.  If you own a stock that has already been a 2-bagger, 3-bagger, 5-bagger, etc., and you sell and the stock later turns out to be a 20-bagger, 50-bagger, or 100-bagger, often you have made a huge mistake by selling too soon.

Link to Mohnish’ lecture at Peking University:  https://www.youtube.com/watch?v=Jo1XgDJCkh4

Here’s the outline for this blog post:

    • Patel Motel Dhandho
    • Manilal Dhandho
    • Virgin Dhandho
    • Mittal Dhandho
    • The Dhandho Framework
    • Dhandho 101: Invest in Existing Businesses
    • Dhandho 102: Invest in Simple Businesses
    • Dhandho 201: Invest in Distressed Businesses in Distressed Industries
    • Dhandho 202: Invest in Businesses with Durable Moats
    • Dhandho 301: Few Bets, Big Bets, Infrequent Bets
    • Dhandho 302: Fixate on Arbitrage
    • Dhandho 401: Margin of Safety—Always!
    • Dhandho 402: Invest in Low-Risk, High-Uncertainty Businesses
    • Dhandho 403: Invest in the Copycats rather than the Innovators
    • A Short Checklist
    • Be Generous

 

PATEL MOTEL DHANDHO

(Mohnish published the book in 2007.  I will use the present tense in this blog post.)

Mohnish notes that Asian Indians make up about 1 percent of the population of the United States.  Of these three million, a small subsection hails from the Indian state of Gujarat—the birthplace of Mahatma Gandhi.  The Patels are from a tiny area in Southern Gujarat.  Mohnish:

Less than one in five hundred Americans is a Patel.  It is thus amazing that over half of all the motels in the entire country are owned and operated by Patels… What is even more stunning is that there were virtually no Patels in the United States just 35 years ago.  They started arriving as refugees in the early 1970s without much in the way of capital or education.  Their heavily accented, broken-English speaking skills didn’t improve their prospects either.  From that severely handicapped beginning, with all the odds stacked against them, the Patels triumphed.  Patels, as a group, today own over $40 billion in motel assets in the United States, pay over $725 million a year in taxes, and employ nearly a million people.  How did this small, impoverished ethnic group come out of nowhere and end up controlling such vast resources?  There is a one word explanation:  Dhandho.

Dhandho means a low-risk, high-return approach to business.  It means the upside is much larger than the downside, which is the essence of value investing.

Dhandho is all about the minimization of risk while maximizing the reward… Dhandho is thus best described as endeavors that create wealth while taking virtually no risk.

Mohnish gives a brief history of the Patels.  Some Patels had gone to Uganda and were doing well there as entrepreneurs.  But when General Idi Amin came to power as a dictator in 1972, things changed.  The Ugandan state seized all of the businesses held by Patels and other non-natives.  These businesses were nationalized, and the previous owners were paid nothing.

Because India was already dealing with a severe refugee crisis in 1972-1973, the Indian-origin population that had been tossed out of Uganda was not allowed back into India.  Many Patels settled in England and Canada, and a few thousand were accepted in the United States.

In 1973, many nondescript motels were being foreclosed and then sold at distressed prices.  “Papa Patel” realized that a motivated seller or bank might finance 90% of the purchase.  If Papa Patel could put $5,000 down, he could get a motel on the cheap.  The Patel family would run things and also live there.  So they had no salaries to pay, and no rent to pay.  With rock-bottom expenses, they could then offer the lowest nightly rates.  This would lead to higher occupancy and high profits over time, given the very low cost structure.

As long as the motel didn’t fail, it would likely be a highly profitable venture relative to the initial $5,000 investment.  If the motel did fail, Papa Patel reasoned that he and his wife could bag groceries and save close to $5,000 in a couple of years.  Then Papa Patel could find another cheap motel and make the same bet.  If the probability of failure is 10%, then the odds of two failures in a row would be 1%, while nearly every other scenario would involve a high return on investment.  Once the first motel was solidly profitable, Papa Patel could let his oldest son take over and look for the next one to buy.

The Patels kept repeating this basic approach until they owned over half the motels in the United States.

 

MANILAL DHANDHO

The Patel formula is repeatable.  It’s not just a one-time opportunity based on unique circumstances.  Consider Manilal Chaudhari, also from Gujarat, says Mohnish.

Manilal had worked hard as an accountant in India.  In 1991, with sponsorship from his brother, he migrated to the United States.  His English was not good, and he couldn’t find a job in accounting.

His first job was working 112 hours a week at a gas station at minimum wage.  Later, he got a job at a power supply manufacturing company, Cherokee International, owned by a Patel.  Manilal worked full-time at Cherokee, and kept working at the gas station as much as possible.  The Persian owner of the gas station, recognizing Manilal’s hard work, gave him a 10 percent stake in the business.

In 1998, Manilal decided he wanted to buy a business.  One of the employees at Cherokee (a Patel) told Manilal that he wanted to invest with him in whatever business he found.  In 2001, the travel industry went into a slump and motel occupancy and prices plummeted.  Manilal found a Best Western motel on sale at a terrific location.  Since everyone in the extended family had been working non-stop and saving, Manilal – along with a few Patels from Cherokee – were able to buy the Best Western.

Four years later, the Best Western had doubled in value to $9 million.  The $1.4 million invested by Manilal and a few Patels was now worth $6.7 million, an annualized return of 48 percent.  This doesn’t include annual free cash flow.  Mohnish concludes:

Now, that’s what I’d call Manilal Dhandho.  He worked hard, saved all he could, and then bet it all on a single no-brainer bet.  Reeling from the severe impact of 9/11 on travel, the motel industry was on its knees.  As prices and occupancy collapsed, Manilal stepped in and made his play.  He was on the hunt for three years.  He patiently waited for the right deal to materialize.  Classically, his story is all about Few Bets, Big Bets, Infrequent Bets.  And it’s all about only participating in coin tosses where:

Heads, I win;  tails, I don’t lose much!

 

VIRGIN DHANDHO

The year was 1984 and Richard Branson knew nothing about the airline business.  He started his entrepreneurial journey at 15 and was very successful in building an amazing music recording and distribution business.

Somebody sent Branson a business plan about starting an all business class airline flying between London and New York.  Branson noted that when an executive in the music business received a business plan to start an airline involving a 747 jumbo jet, he knew that the business plan had been turned down in at least three thousand other places before landing on his desk…

Branson decided to offer a unique dual-class service.  But when he presented the idea to his partners and senior executives at the music business, they told him he was crazy.  Branson persisted and discovered that he could lease a 747 jumbo jet from Boeing.  Branson calculated that Virgin Atlantic Airlines, if it failed, would cost $2 million.  His record company was going to earn $12 million that year and about $20 million the following year.

Branson also realized that tickets get paid about 20 days before the plane takes off.  But fuel is paid 30 days after the plane lands.  Staff wages are paid 15 to 20 days after the plane lands.  So the working capital needs of the business would be fairly low.

Branson had found a service gap and Virgin Atlantic ended up doing well.  Branson would repeat this formula in many other business opportunities:

Heads, I win;  tails, I don’t lose much!

 

MITTAL DHANDHO

Mohnish says Rajasthan is the most colorful state of India.  Marwar is a small district in the state, and the Marwaris are seen as excellent businesspeople.  Lakshmi Mittal, a Marwari entrepreneur, went from zero to a $20 billion net worth in about 30 years.  And he did it in an industry with terrible economics:  steel mills.

Take the example of the deal he created to take over the gigantic Karmet Steel Works in Kazakhstan.  The company had stopped paying its workforce because it was bleeding red ink and had no cash.  The plant was on the verge of closure with its Soviet-era managers forced to barter for steel food for its workers.  The Kazakh government was glad to hand Mr. Mittal the keys to the plant for nothing.  Not only did Mr. Mittal retain the entire workforce and run the plant, he paid all the outstanding wages and within five years had turned it into a thriving business that was gushing cash.  The workers and townsfolk literally worship Mittal as the person who saved their town from collapse.

…The same story was repeated with the Sidek Steel plant in Romania, and the Mexican government handed him the keys to the Sibalsa Mill for $220 million in 1992.  It had cost the Mexicans over $2 billion to build the plant.  Getting dollar bills at 10 cents—or less—is Dhandho on steroids.  Mittal’s approach has always been to get a dollar’s worth of assets for far less than a dollar.  And then he has applied his secret sauce of getting these monolith mills to run extremely efficiently.

Mohnish recounts a dinner he had with a Marwari friend.  Mohnish asked how Marwari businesspeople think about business.  The friend replied that they expect their entire investment to be returned as dividends within three years, with the principal still being worth at least the initial amount invested.

 

THE DHANDHO FRAMEWORK

Mohnish lays out the Dhando framework, including:

  • Invest in existing businesses.
  • Invest in simple businesses.
  • Invested in distressed businesses in distressed industries.
  • Invest in businesses with durable moats.
  • Few bets, big bets, and infrequent bets.
  • Fixate on arbitrage.
  • Margin of safety—always.
  • Invest in low-risk, high-uncertainty businesses.
  • Invest in the Copycats rather than the Innovators.

Let’s look at each point.

 

DHANDHO 101: INVEST IN EXISTING BUSINESSES

Over a long period of time, owning parts of good businesses via the stock market has been shown to be one of the best ways to preserve and grow wealth.  Mohnish writes that there are six big advantages to investing in stocks:

  • When you buy stock, you become a part owner of an existing business. You don’t have to do anything to create the business or to make the business run.
  • You can get part ownership of a compounding machine. It is simple to buy your stake, and the business is already fully staffed and running.
  • When people buy or sell entire businesses, both buyer and seller typically have a good idea of what the business is worth. It’s hard to find a bargain unless the industry is highly distressed.  In the public stock market, however, there are thousands and thousands of businesses.  Many stock prices change by 50% or more in any given year, but the intrinsic value of most businesses does not change by 50% in a given year.  So a patient investor can often find opportunities.
  • Buying an entire business usually takes serious capital. But buying part ownership via stock costs very little by comparison.  In stocks, you can get started with a tiny pool of capital.
  • There are likely over 100,000 different businesses in the world with public stock available.
  • For a long-term value investor, the transaction costs are very low (especially at a discount broker) over time.

 

DHANDHO 102: INVEST IN SIMPLE BUSINESSES

As Warren Buffett has noted, you generally do not get paid extra for degree of difficulty in investing.  There is no reason, especially for smaller investors, not to focus on simple businesses.  By patiently looking at hundreds and hundreds of micro-cap stocks, eventually you can find a 10-bagger, 20-bagger, or even a 100-bagger.  And the small business in question is likely to be quite simple.  With such a large potential upside, there is no reason, if you’re a small investor, to look at larger or more complicated businesses.  (The Boole Microcap Fund that I manage focuses exclusively on micro caps.)

It’s much easier to value a simple business because it usually is easier to estimate the future free cash flows.  The intrinsic value of any business—what the business is worth—is the sum of all future free cash flows discounted back to the present.  This is called the discounted cash flow (DCF) approach.  (Intrinsic value could also mean liquidation value in some cases.)

You may need to have several scenarios in your DCF analysis—a low case, a mid case, and a high case.  (What you’re really looking for is a high case that involves a 10-bagger, 20-bagger, or 100-bagger.)  But you’re still nearly always better off limiting your investments to simple businesses.

Only invest in businesses that are simple—ones where conservative assumptions about future cash flows are easy to figure out.

 

DHANDHO 201: INVEST IN DISTRESSED BUSINESSES IN DISTRESSED INDUSTRIES

The stock market is usually efficient, meaning that stock prices are usually accurate representations of what businesses are worth.  It is very difficult for an investor to do better than the overall stock market, as represented by the S&P 500 Index or another similar index.

Stock prices, in most instances, do reflect the underlying fundamentals.  Trying to figure out the variance between prices and underlying intrinsic value, for most businesses, is usually a waste of time.  The market is mostly efficient.  However, there is a huge difference between mostly and fully efficient.

Because the market is not always efficient, value investors who patiently examine hundreds of different stocks eventually will find a few that are undervalued.  Because public stock markets are highly liquid, if an owner of shares becomes fearful, he or she can quickly sell those shares.  For a privately held business, however, it usually takes months for an owner to sell the position.  Thus, a fearful owner of public stock is often more likely to sell at an irrationally low price because the sale can be completed right away.

Where can you find distressed businesses or industries?  Mohnish offers some suggestions:

  • Business headlines often include articles about distressed businesses or industries.
  • You can look at prices that have dropped the most in the past 52 weeks. You can also look at stocks trading at low price-to-earnings ratios (P/Es), low price-to-book ratios (P/Bs), high dividend yields, and so on.  Not every quantitatively cheap stock is undervalued, but some are.  There are various services that offer screening such as Value Line.
  • You can follow top value investors by reading 13-F Forms or through different services. I would only note that the vast majority of top value investors are not looking at micro-cap stocks.  If you’re a small investor, your best opportunities are very likely to be found among micro caps.  Very few professional investors ever look there, causing micro-cap stocks to be much more inefficiently priced than larger stocks.  Also, micro caps tend to be relatively simple, and they often have far more room to grow.  Most 100-baggers start out as micro caps.
  • Value Investors Club (valueinvestorsclub.com) is a club for top value investors. You can get free guest access to all ideas that are 45 days old or older.  Many cheap stocks stay cheap for a long time.  Often good ideas are still available after 45 days have elapsed.

 

DHANDHO 202: INVEST IN BUSINESSES WITH DURABLE MOATS

A moat is a sustainable competitive advantage.  Moats are often associated with capital-light businesses.  Such businesses (if successful) tend to have sustainably high ROIC (return on invested capital)—the key attribute of a sustainable competitive advantage.  Yet sometimes moats exist elsewhere and sometimes they are hidden.

Sometimes the moat is hidden.  Take a look at Tesoro Corporation.  It is in the oil refining business—which is a commodity.  Tesoro has no control over the price of its principle raw material, crude oil.  It has no control [of the price] over its principal finished good, gasoline.  Nonetheless, it has a fine moat.  Tesoro’s refineries are primarily on the West Coast and Hawaii.  Refining on the West Coast is a great business with a good moat.  There hasn’t been a refinery built in the United States for the past 20 years.  Over that period, the number of refineries has gone down from 220 to 150, while oil demand has gone up about 2 percent a year.  The average U.S. refinery is operating at well over 90 percent of capacity.  Anytime you have a surge in demand, refining margins escalate because there is just not enough capacity.

…How do we know when a business has a hidden moat and what that moat is?  The answer is usually visible from looking at its financial statements.  Good businesses with good moats… generate high returns on capital deployed in the business.  (my emphasis)

But the nature of capitalism is that any company that is earning a high return on invested capital will come under attack by other businesses that want to earn a high return on invested capital.

It is virtually a law of nature that no matter how well fortified and defended a castle is, no matter how wide or deep its moat is, no matter how many sharks or piranhas are in that moat, eventually it is going to fall to the marauding invaders.

Mohnish quotes Charlie Munger:

Of the fifty most important stocks on the NYSE in 1911, today only one, General Electric, remains in business… That’s how powerful the forces of competitive destruction are.  Over the very long term, history shows that the chances of any business surviving in a manner agreeable to a company’s owners are slim at best.

Mohnish adds:

There is no such thing as a permanent moat.  Even such invincible businesses today like eBay, Google, Microsoft, Toyota, and American Express will all eventually decline and disappear.

…It takes about 25 to 30 years from formation for a highly successful company to earn a spot on the Fortune 500… it typically takes many blue chips less than 20 years after they get on the list to cease to exist.  The average Fortune 500 business is already past its prime by the time it gets on the list.

If you’re a small investor, searching for potential 10-baggers or 100-baggers among micro-cap stocks makes excellent sense.  You want to find tiny companies that much later reach the Fortune 500.  You don’t want to look at companies that are already on the Fortune 500 because the potential returns are far more likely to be mediocre going forward.

 

DHANDHO 301: FEW BETS, BIG BETS, INFREQUENT BETS

Claude Shannon was a fascinating character—he often rode a unicycle while juggling, and his house was filled with gadgets.  Shannon’s master’s thesis was arguably the most important and famous master’s thesis of the twentieth century.  In it, he proposed binary digit or bit, as the basic unit of information.  A bit could have only two values—0 or 1, which could mean true or false, yes or no, or on or off.  This allowed Boolean algebra to represent any logical relationship.  This meant that the electrical switch could perform logic functions, which was the practical foundation for all digital circuits and computers.

The mathematician Ed Thorp, a colleague of Shannon’s at MIT, had discovered a way to beat the casinos at blackjack.  But Thorp was trying to figure out how to size his blackjack bets as a function of how favorable the odds were.  Someone suggested to Thorp that he talk to Shannon about it.  Shannon recalled a paper written by a Bell Labs colleague of his, John Kelly, that dealt with this question.

The Kelly criterion can be written as follows:

  • F = p – [q/o]

where

  • F = Kelly criterion fraction of current capital to bet
  • o = Net odds, or dollars won per $1 bet if the bet wins (e.g., the bet may pay 5 to 1, meaning you win $5 per each $1 bet if the bet wins)
  • p = probability of winning
  • q = probability of losing = 1 – p

The Kelly criterion has a unique mathematical property: if you know the probability of winning and the net odds (payoff), then betting exactly the percentage determined by the Kelly criterion leads to the maximum long-term compounding of capital, assuming that you’re going to make a long series of bets.  Betting any percentage that is not equal to that given by the Kelly criterion will inevitably lead to lower compound growth over a long period of time.

Thorp proceeded to use the Kelly criterion to win quite a bit of money at blackjack, at least until the casinos began taking countermeasures such as cheating dealers, frequent reshuffling, and outright banning.  But Thorp realized that the stock market was also partly inefficient, and it was a far larger game.

Thorp launched a hedge fund that searched for little arbitrage situations (pricing discrepancies) involving warrants, options, and convertible bonds.  In order to size his positions, Thorp used the Kelly criterion.  Thorp evolved his approach over the years as previously profitable strategies were copied.  His multi-decade track record was terrific.

Ed Thorp examined Buffett’s career and concluded that Buffett has used the essential logic of the Kelly criterion by concentrating his capital into his best ideas.  Buffett’s concentrated value approach has produced an outstanding, unparalleled 65-year track record.

Thorp has made several important points about the Kelly criterion as it applies to long-term value investing.  The Kelly criterion was invented to apply to a very long series of bets.  Value investing differs because even a concentrated value investing approach will usually have at least 5-8 positions in the portfolio at the same time.  Thorp argues that, in this situation, the investor must compare all the current and prospective investments simultaneously on the basis of the Kelly criterion.

Mohnish gives an example showing how you can use the Kelly criterion on your top 8 ideas, and then normalize the position sizes.

Say you look at your top 8 investment ideas.  You use the Kelly criterion on each idea separately to figure out how large the position should be, and this is what you conclude about the ideal bet sizes:

  • Bet 1 – 80%
  • Bet 2 – 70%
  • Bet 3 – 60%
  • Bet 4 – 55%
  • Bet 5 – 45%
  • Bet 6 – 35%
  • Bet 7 – 30%
  • Bet 8 – 25%

Of course, that adds up to 400%.  Yet for a value investor, especially running a concentrated portfolio of 5-8 positions, it virtually never makes sense to buy stocks on margin.  Leverage cannot make a bad investment into a good investment, but it can turn a good investment into a bad investment.  So you don’t need any leverage.  It’s better to compound at a slightly lower rate than to risk turning a good investment into a bad investment because you lack staying power.

So the next step is simply to normalize the position sizes so that they add up to 100%.  Since the original portfolio adds up to 400%, you just divide each position by 4:

  • Bet 1 – 20%
  • Bet 2 – 17%
  • Bet 3 – 15%
  • Bet 4 – 14%
  • Bet 5 – 11%
  • Bet 6 – 9%
  • Bet 7 – 8%
  • Bet 8 – 6%

(These percentages are rounded for simplicity.)

As mentioned earlier, if you truly know the odds of each bet in a long series of bets, the Kelly criterion tells you exactly how much to bet on each bet in order to maximize your long-term compounded rate of return.  Betting any other amount will lead to lower compound returns.  In particular, if you repeatedly bet more than what the Kelly criterion indicates, you eventually will destroy your capital.

It’s nearly always true when investing in a stock that you won’t know the true odds or the true future scenarios.  You usually have to make an estimate.  Because you never want to bet more than what the Kelly criterion says, it is wise to bet one half or one quarter of what the Kelly criterion says.  This is called half-Kelly or quarter-Kelly betting.  What is nice about half-Kelly betting is that you will earn three-quarters of the long-term returns of what full Kelly betting would deliver, but with only half the volatility.

So in practice, if there is any uncertainty in your estimates, you want to bet half-Kelly or quarter-Kelly.  In the case of a concentrated portfolio of 5-8 stocks, you will frequently end up betting half-Kelly or quarter-Kelly because you are making 5-8 bets at the same time.  In Mohnish’s example, you end up betting quarter-Kelly in each position once you’ve normalized the portfolio.

Mohnish quotes Charlie Munger again:

The wise ones bet heavily when the world offers them that opportunity.  They bet big when they have the odds.  And the rest of the time, they don’t.  It’s just that simple.

When running the Buffett Partnership, Warren Buffett invested 40% of the partnership in American Express after the stock had been cut in half following the salad oil scandal.  American Express had to announce a $60 million loss, a huge hit given its total market capitalization of roughly $150 million at the time.  But Buffett determined that the essential business of American Express—travelers’ checks and charge cards—had not been permanently damaged.  American Express still had a very valuable moat.

Buffett explained his reasoning in several letters to limited partners, as quoted by Mohnish here:

We might invest up to 40% of our net worth 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 change the underlying value of the investment.

We are obviously only going to go to 40% in very rare situations—this rarity, of course, is what makes it necessary that we concentrate so heavily, when we see such an opportunity.  We probably have had only five or six situations in the nine-year history of the partnerships where we have exceeded 25%.  Any such situations are going to have to promise very significant superior performance… They are also going to have to possess such superior qualitative and/or quantitative factors that the chance of serious permanent loss is minimal…

There’s virtually no such thing as a sure bet in the stock market.  But there are situations where the odds of winning are very high or where the potential upside is substantial.

One final note:  In constructing a concentrated portfolio of 5-8 stocks, if at least some of the positions are non-correlated or even negatively correlated, then the volatility of the overall portfolio can be reduced.  Some top investors prefer to have about 15 positions with low correlations.

Once you get to at least 25 positions, specific correlations typically tend not to be an issue, although some investors may end up concentrating on specific industries.  In fact, it often may make sense to concentrate on industries that are deeply out-of-favor.

For instance, oil touched $26 a barrel (WTI) a couple of years ago.  Currently it’s a bit over $54 a barrel.  Due to cost-cutting, many oil projects make economic sense at prices well under $40 per barrel.  Moreover, over the next 3 to 5 years at least, the price of oil is likely to be roughly $60-70.  On the one hand, prices are held down somewhat due to the recent surge in U.S. tight oil production.  On the other hand, Saudi Arabia has some control over supply and thus prices, and they need oil closer to $65 in order to minimize unrest.

Under these conditions, it may make sense to concentrate on oil-related companies (some producers, some drillers, etc.).  That’s not to say that there is no risk in such a strategy.  Specific companies may encounter issues.  Or perhaps there will be a sudden wide adoption of electric vehicles, rather than a slow, gradual adoption.  But on the whole, many oil-related companies probably represent good value at their current prices.

On the topic of industry concentration, value investor Steven Romick—who has been overweight energy before—remarked:

We don’t benchmark at all…We’ll go where we think the value is and let the weightings fall where they may.

Mohnish concludes:

…It’s all about the odds.  Looking out for mispriced betting opportunities and betting heavily when the odds are overwhelmingly in your favor is the ticket to wealth.  It’s all about letting the Kelly Formula dictate the upper bounds of these large bets.  Further, because of multiple favorable betting opportunities available in equity markets, the volatility surrounding the Kelly Formula can be naturally tamed while still running a very concentrated portfolio.

In sum, top value investors like Warren Buffett, Charlie Munger, and Mohnish Pabrai—to name just a few out of many—naturally concentrate on their best 5-8 ideas, at least when they’re managing a small enough amount of money.  (These days, Berkshire’s portfolio is massive, which makes it much more difficult to concentrate, let alone to find hidden gems among micro caps.)

You have to take a humble look at your strategy and your ability before deciding on your level of concentration.  The Boole Microcap Fund that I manage is designed to focus on the top 15-25 ideas.  This is concentrated enough so that the best performers—whichever stocks they turn out to be—can make a difference to the portfolio.  But it is not so concentrated that it misses the best performers.  In practice, the best performers very often turn out to be idea #9 or idea #17, rather than idea #1 or idea #2.  Many top value investors—including Peter Cundill, Joel Greenblatt, and Mohnish Pabrai—have found this to be true.

 

DHANDHO 302: FIXATE ON ARBITRAGE

The example often given for traditional commodity arbitrage is that gold is selling for $1,500 in London and $1,490 in New York.  By buying gold in New York and selling it in London, the arbitrageur can make an almost risk-free profit.

In merger arbitrage, Company A offers to buy Company B at, say, $20 per share.  The stock of Company B may move from $15 to $19.  Now the arbitrageur can buy the stock in Company B at $19 in order to capture the eventual move to $20.  By doing several such deals, the arbitrageur can probably make a nice profit, although there is a risk for each individual deal.

In what Mohnish calls Dhandho arbitrage, the entrepreneur risks a relatively small amount of capital relative to the potential upside.  Just look at the earlier examples, including Patel Motel Dhandho, Virgin Dhandho, and Mittal Dhandho.

Heads, I win;  tails, I don’t lose much!

 

DHANDHO 401: MARGIN OF SAFETY—ALWAYS!

Nearly every year, Buffett has hosted over 30 groups of business students from various universities.  The students get to ask questions for over an hour before going to have lunch with Buffett.  Mohnish notes that students nearly always ask for book or reading recommendations, and Buffett’s best recommendation is always Ben Graham’s The Intelligent Investor.  As Buffett told students from Columbia Business School on March 24, 2006:

The Intelligent Investor is still the best book on investing.  It has the only three ideas you really need:

  • Chapter 8—The Mr. Market analogy.  Make the stock market serve you.  The C section of the Wall Street Journal is my business broker—it quotes me prices every day that I can take or leave, and there are no called strikes.
  • Chapter 8—A stock is a piece of a business.  Never forget that you are buying a business which has an underlying value based on how much cash goes in and out.
  • Chapter 20—Margin of Safety.  Make sure that you are buying a business for way less than you think it is conservatively worth.

The heart of value investing is an idea that is directly contrary to economic and financial theory:

  • The bigger the discount to intrinsic value, the lower the risk.
  • The bigger the discount to intrinsic value, the higher the return.

Economic and financial theory teaches that higher returns always require higher risk.  But Ben Graham, the father of value investing, taught just the opposite:  The lower the price you pay below intrinsic value, the lower your risk and the higher your potential return.

Mohnish argues that the Dhandho framework embodies Graham’s margin of safety idea.  Papa Patel, Manilal, and Branson all have tried to minimize the downside while maximizing the upside.  Again, most business schools, relying on accepted theory, teach that low returns come from low risk, while high returns require high risk.

Mohnish quotes Buffett’s observations about Berkshire’s purchase of Washington Post stock in 1973:

We bought all of our [Washington Post (WPC)] holdings in mid-1973 at a price of not more than one-fourth of the then per-share business value of the enterprise.  Calculating the price/value ratio required no unusual insights.  Most security analysts, media brokers, and media executives would have estimated WPC’s intrinsic business value at $400 to $500 million just as we did.  And its $100 million stock market valuation was published daily for all to see.  Our advantage, rather, was attitude:  we had learned from Ben Graham that the key to successful investing was the purchase of shares in good businesses when market prices were at a large discount from underlying business value.

…Through 1973 and 1974, WPC continued to do fine as a business, and intrinsic value grew.  Nevertheless, by year-end 1974 our WPC holding showed a loss of about 25%, with a market value of $8 million against our cost of $10.6 million.  What we had bought ridiculously cheap a year earlier had become a good bit cheaper as the market, in its infinite wisdom, marked WPC stock down to well below 20 cents on the dollar of intrinsic value.

As of 2007 (when Mohnish wrote his book), Berkshire’s stake in the Washington post had grown over 33 years from the original $10.6 million to a market value of over $1.3 billion—more than 124 times the original investment.  Moreover, as of 2007, the Washington Post was paying a modest dividend (not included in the 124 times figure).  The dividend alone (in 2007) was higher than what Berkshire originally paid for its entire position.  Buffett:

Most institutional investors in the early 1970s, on the other hand, regarded business value as of only minor relevance when they were deciding the prices at which they would buy or sell.  This now seems hard to believe.  However, these institutions were then under the spell of academics at prestigious business schools who were preaching a newly-fashioned theory:  the stock market was totally efficient, and therefore calculations of business value—and even thought, itself—were of no importance in investment activities.  (We are enormously indebted to those academics:  what could be more advantageous in an intellectual contest—whether it be bridge, chess, or stock selection—than to have opponents who have been taught that thinking is a waste of energy?)

At any given time, a business is in either of two states:  it has problems or it will have problems.  Virtually every week there are companies or whole industries where stock prices collapse.  Many business problems are temporary and not permanent.  But stock investors on the whole tend to view business problems as permanent, and they mark down the stock prices accordingly.

You may be wondering:  Due to capitalist competition, nearly all businesses eventually fail, so how can many business problems be temporary?  When we look at businesses experiencing problems right now, many of those problems will be solved over the next three to five years.  Thus, considering the next three to five years, many business problems are temporary.  But the fate of a given business over several decades is a different matter entirely.

 

DHANDHO 402: INVEST IN LOW-RISK, HIGH-UNCERTAINTY BUSINESSES

The future is always uncertain.  And that’s even more true for some businesses.  Yet if the stock price is low enough, high uncertainty can create a good opportunity.

Papa Patel, Manilal, Branson, and Mittal are all about investing in low-risk businesses.  Nonetheless, most of the businesses they invested in had a very wide range of possible outcomes.  The future performance of these businesses was very uncertain.  However, these savvy Dhandho entrepreneurs had thought through the range of possibilities and drew comfort from the fact that very little capital was invested and/or the odds of a permanent loss of capital were extremely low… Their businesses had a common unifying characteristic—they were all low-risk, high-uncertainty businesses.

In essence, says Mohnish, these were all simple bets:

Heads, I win;  tails, I don’t lose much!

Wall Street usually hates high uncertainty, and often does not distinguish between high uncertainty and high risk.  But there are several distinct situations, observes Mohnish, where Wall Street tends to cause the stock price to collapse:

  • High risk, low uncertainty
  • High risk, high uncertainty
  • Low risk, high uncertainty

Wall Street loves the combination of low risk and low uncertainty, but these stocks nearly always trade at high multiples.  On the other hand, Dhandho entrepreneurs and value investors are only interested in low risk and high uncertainty.

Mohnish discusses an example of a company he was looking at in the year 2000:  Stewart Enterprises (STEI), a funeral service business.  Leading companies such as Stewart Enterprises, Loewen, Service Corp. (SRV), and Carriage Services (CSV) had gone on buying sprees in the 1990s, acquiring mom-and-pop businesses in their industry.  These companies all ended up with high debt as a result of the acquisitions.  They made the mistake of buying for cash—using debt—rather than buying using stock.

Loewen ended up going bankrupt.  Stewart had $930 million of long-term debt with $500 million due in 2002.  Wall Street priced all the funeral service giants as if they were going bankrupt.  Stewart’s price went from $28 to $2 in two years.  Stewart kept coming up on the Value Line screen for lowest price-to-earnings (P/E) ratios.  Stewart had a P/E of less than three, a rarity.  Mohnish thought that funeral services must be a fairly simple business to understand, so he started doing research.

Mohnish recalled reading an article in the mid-1990s in the Chicago Tribune about the rate of business failure in various industries.  The lowest rate of failure for any type of business was funeral homes.  This made sense, thought Mohnish.  It’s not the type of business that aspiring entrepreneurs would dream about, and pre-need sales often make up about 25 percent of total revenue.  It’s a steady business that doesn’t change much over time.

Stewart had roughly $700 million in annual revenue and owned around 700 cemeteries and funeral homes.  Most of its business was in the United States.  Stewart’s tangible book value was $4 per share, and book value was probably understated because hard assets like land were carried at cost.  At less than $2 per share, Stewart was trading at less than half of stated tangible book value.  By the time the debt was due, the company would generate over $155 million in free cash flow, leaving a shortfall of under $350 million.

Mohnish thought through some scenarios and estimated the probability for each scenario:

  • 25% probability: The company could sell some funeral homes.  Selling 100 to 200 might take care of the debt.  Equity value > $4 per share.
  • 35% probability: Based on the company’s solid and predictable cash flow, Stewart’s lenders or bankers might decide to extend the maturities or refinance the debt—especially if the company offered to pay a higher interest rate.  Equity value > $4 per share.
  • 20% probability: Based on Stewart’s strong cash flows, the company might find another lender—especially if it offered to pay a higher interest rate.  Equity value > $4 per share.
  • 19% probability: Stewart enters bankruptcy.  Even assuming distressed asset sales, equity value > $2 per share.
  • 1% probability: A 50-mile meteor comes in or Yellowstone blows or some other extreme event takes place that destroys the company.  Equity value = $0.

The bottom line, as Mohnish saw it, was that the odds were less than 1% that he would end up losing money if he invested in Stewart at just under $2 per share.  Moreover, there was an 80% chance that the equity would be worth at least $4 per share.  So Mohnish invested 10 percent of Pabrai Funds in Stewart Enterprises at under $2 per share.

A few months later, Stewart announced that it had begun exploring sales of its international funeral homes.  Stewart expected to generate $300 to $500 million in cash from this move.  Mohnish:

The amazing thing was that management had come up with a better option than I had envisioned.  They were going to be able to eliminate the debt without any reduction in their cash flow.  The lesson here is that we always have a free upside option on most equity investments when competent management comes up with actions that make the bet all the more favorable.

Soon the stock hit $4 and Mohnish exited the position with more than 100% profit.

It’s worth repeating what investor Lee Ainslee has said:  Good management tends to surprise on the upside, while bad management tends to surprise on the downside.

Frontline

In 2001, Mohnish noticed two companies with a dividend yield of more than 15 percent.  Both were crude oil shippers:  Knightsbridge (VLCC) and Frontline (FRO).  Mohnish started reading about this industry.

Knightsbridge had been formed a few years earlier when it ordered several tankers from a Korean shipyard.  A very large crude carrier (VLCC) or Suezmax at the time cost $60 to $80 million and would take two to three years to be built and delivered.  Knightsbridge would then lease the ships to Shell Oil under long-term leases.  Shell would pay Knightsbridge a base lease rate (perhaps $10,000 a day per tanker) regardless of whether it used the ships or not.  On top of that, Shell paid Knightsbridge a percentage of the difference between a base rate and the spot market price for VLCC rentals, notes Mohnish.  So if the spot price for a VLCC was $30,000 per day, Knightsbridge might receive $20,000 a day.  If the spot was $50,000, it would get perhaps $35,000 a day.  Mohnish:

At the base rate, Knightbridge pretty much covered its principal and interest payments for the debt it took on to pay for the tankers.  As the rates went above $10,000, there was positive cash flow;  the company was set up to just dividend all the excess cash out to shareholders, which is marvelous…

Because of this unusual structure and contract, when tanker rates go up dramatically, this company’s dividends go through the roof.

Mohnish continues:

In investing, all knowledge is cumulative.  I didn’t invest in Knightsbridge, but I did get a decent handle on the crude oil shipping business.  In 2001, we had an interesting situation take place with one of these oil shipping companies called Frontline.  Frontline is the exact opposite business model of Knightsbridge.  It has the largest oil tanker fleet in the world, among all the public companies.  The entire fleet is on the spot market.  There are very few long-term leases.

Because it rides on the spot market on these tankers, there is no such thing as earnings forecasts or guidance.  The company’s CEO himself doesn’t know what the income will be quarter to quarter.  This is great, because whenever Wall Street gets confused, it means we likely can make some money.  This is a company that has widely gyrating earnings.

Oil tanker rates have ranged historically from $6,000 a day to $100,000 a day.  The company needs about $18,000 a day to breakeven… Once [rates] go above $30,000 to $35,000, it is making huge profits.  In the third quarter of 2002, oil tanker rates collapsed.  A recession in the United States and a few other factors caused a drop in crude oil shipping volume.  Rates went down to $6,000 a day.  At $6,000 a day Frontline was bleeding red ink, badly.  The stock went from $11 a share to around $3, in about three months.

Mohnish notes the net asset value of Frontline:

Frontline had about 70 VLCCs at the time.  While the daily rental rates collapsed, the price per ship hadn’t changed much, dropping about 10 percent or 15 percent.  There is a fairly active market in buying and selling oil tankers.  Frontline had a tangible book value of about $16.50 per share.  Even factoring in the distressed market for ships, you would still get a liquidation value north of $11 per share.  The stock price had gone from $15 to $3… Frontline was trading at less than one-third of liquidation value.

Keep in mind that Frontline could sell a ship for about $60 million, and the company had 70 ships.  Frontline’s annual interest payments were $150 million.  If it sold two to three ships a year, Frontline could sustain the business at the rate of $6,000 a day for several years.

Mohnish also discovered that Frontline’s entire fleet was double hull tankers.  All new tankers had to be double hull after 2006 due to regulations following the Exxon Valdez spill.  Usually single hull tankers were available at cheaper day rates than double hull tankers.  But this wasn’t true when rates dropped to $6,000 a day.  Both types of ship were available at the same rate.  In this situation, everyone would rent the double hull ships and no one rented the single hull ships.

Owners of the single hull ships were likely get jittery and to sell the ships as long as rates stayed at $6,000 a day.  If they waited until 2006, Mohnish explains, the ability to rent single hull ships would be much lower.  And by 2006, scrap rates might be quite low if a large number of single hull ships were scrapped at the same time.  The net result is that there is a big jump in scrapping for single hulled tankers whenever rates go down.  Mohnish:

It takes two to three years to get delivery of a new tanker.  When demand comes back up again, inventory is very tight because capacity has been taken out and it can’t be added back instantaneously.  There is a definitive cycle.  When rates go as low as $6,000 and stay there for a few weeks, they can rise to astronomically high levels, say $60,000 a day, very quickly.  With Frontline, for about seven or eight weeks, the rates stayed under $10,000 a day and then spiked to $80,000 a day in fourth quarter 2002.  The worldwide fleet of VLCCs in 2002 was about 400 ships.  Over the past several decades, worldwide oil consumption has increased by 2 percent to 4 percent on average annually.  This 2 percent to 4 percent is generally tied to GDP growth.  Usually there are 10 to 12 new ships added each year to absorb this added demand.  When scrapping increases beyond normal levels, the fleet is no longer increasing by 2 percent to 4 percent.  When the demand for oil rises, there just aren’t enough ships.  The only thing that’s adjustable is the price, which skyrockets.

Pabrai Funds bought Frontline stock in the fall of 2002 at $5.90 a share, about half of liquidation value of $11 to $12.  When the stock moved up to $9 to $10, Mohnish sold the shares.  Because he bought the stock at roughly half liquidation value, this was a near risk-free bet:  Heads, I win a lot;  tails, I win a little!

Mohnish gives a final piece of advice:

Read voraciously and wait patiently, and from time to time amazing bets will present themselves.

Important Note:  Had Mohnish kept the shares of Frontline, they would have increased dramatically.  The shares approached $120 within a few years, so Mohnish would have made 20x his initial investment at $5.90 per share had he simply held on for a few years.

As noted earlier, Mohnish recently gave a lecture at Peking University (Guanghua School of Management) about 10-baggers to 100-baggers, giving many examples of stocks like Frontline that he had actually owned but sold way too soon.  Link:  https://www.youtube.com/watch?v=Jo1XgDJCkh4

 

DHANDHO 403: INVEST IN THE COPYCATS RATHER THAN THE INNOVATORS

What Mohnish calls copycats are businesses that simply copy proven innovations.  The first few Patels figured out the economics of motel ownership.  The vast majority of Patels who came later simply copied what the first Patels had already done successfully.

Mohnish writes:

Most entrepreneurs lift their business ideas from other existing businesses or from their last employer.  Ray Kroc loved the business model of the McDonald brothers’ hamburger restaurant in San Bernadino, California.  In 1954, he bought the rights to the name and know-how, and he scaled it, with minimal change.  Many of the subsequent changes or innovations did not come from within the company with its formidable resources—they came from street-smart franchisees and competitors.  The company was smart enough to adopt them, just as they adopted the entire concept at the outset.

 

A SHORT CHECKLIST

Mohnish gives a list of good questions to ask before buying a stock:

  • Is it a business I understand very well—squarely within my circle of competence?
  • Do I know the intrinsic value of the business today and, with a high degree of confidence, how it is likely to change over the next few years?
  • Is the business priced at a large discount to its intrinsic value today and in two to three years?  Over 50 percent?
  • Would I be willing to invest a large part of my net worth into this business?
  • Is the downside minimal?
  • Does the business have a moat?
  • Is it run by able and honest managers?

If the answers to these questions are yes, buy the stock.  Furthermore, writes Mohnish, hold the stock for at least two to three years before you think about selling.  This gives enough time for conditions to normalize and thus for the stock to approach intrinsic value.  One exception:  If the stock increases materially in less than two years, you can sell, but only after you have updated your estimate of intrinsic value.

In any scenario, you should always update your estimate of intrinsic value.  If intrinsic value is much higher than the current price, then continuing to hold is almost always the best decision.  One huge mistake to avoid is selling a stock that later becomes a 10-bagger, 20-bagger, or 100-bagger.  That’s why you must always update your estimate of intrinsic value.  And don’t get jittery just because a stock is hitting new highs.

A few more points:

  • If you have a good investment process, then about 2/3 of the time the stock will approach intrinsic value over two to three years.  1/3 of the time, the investment won’t work as planned—whether due to error, bad luck, or unforeseeable events—but losses should be limited due to a large margin of safety having been present at the time of purchase.
  • In the case of distressed equities, there may be much greater potential upside as well as much greater potential downside.  A few value investors can use this approach, but it’s quite difficult and typically requires greater diversification.
  • For most value investors, it’s best to stick with companies with low or no debt.  You may grow wealth a bit more slowly this way, but as Buffett and Munger always ask, what’s the rush?  Buffett and Munger had a friend Rick Guerin who owned a huge number of Berkshire Hathaway shares, but many of the shares were on margin.  When Berkshire stock got cut in half—which will happen occasionally to almost any stock, no matter how good the company—Guerin was forced to sell much of his position.  Had Guerin not been on margin, his non-margined shares in Berkshire would later have been worth a fortune (approaching $1 billion).
  • Your own mistakes are your best teachers, explains Mohnish.  You’ll get better over time by studying your own mistakes:

While it is always best to learn vicariously form the mistakes of others, the lessons that really stick are ones we’ve stumbled through ourselves.

 

BE GENEROUS

Warren Buffett and Bill Gates are giving away most of their fortune to help many people who are less fortunate.  Bill and Melinda Gates devote much of their time and energy (via the Gates Foundation) to saving or improving as many human lives as possible.

Mohnish Pabrai and his wife started the Dakshana Foundation in 2005.  Mohnish:

I do urge you to leverage Dhandho techniques fully to maximize your wealth.  But I also hope that… you’ll use some time and some of that Dhandho money to leave this world a little better place than you found it.  We cannot change the world, but we can improve this world for one person, ten people, a hundred people, and maybe even a few thousand people.

 

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:  http://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

(Image:  Zen Buddha Silence by Marilyn Barbone)

October 6, 2019

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: http://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:  http://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

Walter Schloss: Cigar-Butt Specialist

September 29, 2019

Walter Schloss generated one of the best investment track records of all time—close to 21% (gross) annually over 47 years—by investing exclusively in cigar butts (deep value stocks).  Cigar-butt investing usually means buying stock at a discount to book value, i.e., a P/B < 1 (price-to-book ratio below 1).

The highest returning cigar butt strategy comes from Ben Graham, the father of value investing.  It’s called the net-net strategy whereby you take current assets minus all liabilities, and then invest at 2/3 of that level or less.

  • The main trouble with net nets today is that many of them are tiny microcap stocks—below $50 million in market cap—that are too small even for most microcap funds.
  • Also, many net nets exist in markets outside the United States.  Some of these markets have had problems periodically related to the rule of law.

Schloss used net nets in the early part of his career (1955 to 1960).  When net nets became too scarce (1960), Schloss started buying stocks at half of book value.  When those became too scarce, he went to buying stocks at two-thirds of book value.  Eventually he had to adjust again and buy stocks at book value.  Though his cigar-butt method evolved, Schloss was always using a low P/B to find cheap stocks.

(Photo by Sky Sirasitwattana)

One extraordinary aspect to Schloss’s track record is that he invested in roughly 1,000 stocks over the course of his career.  (At any given time, his portfolio had about 100 stocks.)  Warren Buffett commented:

Following a strategy that involved no real risk—defined as permanent loss of capital—Walter produced results over his 47 partnership years that dramatically surpassed those of the S&P 500.  It’s particularly noteworthy that he built this record by investing in about 1,000 securities, mostly of a lackluster type.  A few big winners did not account for his success.  It’s safe to say that had millions of investment managers made trades by a) drawing stock names from a hat; b) purchasing these stocks in comparable amounts when Walter made a purchase; and then c) selling when Walter sold his pick, the luckiest of them would not have come close to equaling his record. There is simply no possibility that what Walter achieved over 47 years was due to chance.

Schloss was aware that a concentrated portfolio—e.g., 10 to 20 stocks—could generate better long-term returns.  However, this requires unusual insight on a repeated basis, which Schloss humbly admitted he didn’t have.

Most investors are best off investing in low-cost index funds or in quantitative value funds.  For investors who truly enjoy looking for undervalued stocks, Schloss offered this advice:

It is important to know what you like and what you are good at and not worry that someone else can do it better.  If you are honest, hardworking, reasonably intelligent and have good common sense, you can do well in the investment field as long as you are not too greedy and don’t get too emotional when things go against you.

I found a few articles I hadn’t seen before on The Walter Schloss Archive, a great resource page created by Elevation Capital: https://www.walterschloss.com/

Here’s the outline for this blog post:

  • Stock is Part Ownership;  Keep It Simple
  • Have Patience;  Don’t Sell on Bad News
  • Have Courage
  • Buy Assets Not Earnings
  • Buy Based on Cheapness Now, Not Cheapness Later
  • Boeing:  Asset Play
  • Less Downside Means More Upside
  • Multiple Ways to Win
  • History;  Honesty;  Insider Ownership
  • You Must Be Willing to Make Mistakes
  • Don’t Try to Time the Market
  • When to Sell
  • The First 10 Years Are Probably the Worst
  • Stay Informed About Current Events
  • Control Your Emotions;  Be Careful of Leverage
  • Ride Coattails;  Diversify

 

STOCK IS PART OWNERSHIP;  KEEP IT SIMPLE

A share of stock represents part ownership of a business and is not just a piece of paper or a blip on the computer screen.

Try to establish the value of the company.  Use book value as a starting point.  There are many businesses, both public and private, for which book value is a reasonable estimate of intrinsic value.  Intrinsic value is what a company is worth—i.e., what a private buyer would pay for it.  Book value—assets minus liabilities—is also called “net worth.”

Follow Buffett’s advice: keep it simple and don’t use higher mathematics.

(Illustration by Ileezhun)

Some kinds of stocks are easier to analyze than others.  As Buffett has said, usually you don’t get paid for degree of difficulty in investing.  Therefore, stay focused on businesses that you can fully understand.

  • There are thousands of microcap companies that are completed neglected by most professional investors.  Many of these small businesses are simple and easy to understand.

 

HAVE PATIENCE;  DON’T SELL ON BAD NEWS

Hold for 3 to 5 years.  Schloss:

Have patience.  Stocks don’t go up immediately.

Schloss again:

Things usually take longer to work out but they work out better than you expect.

(Illustration by Marek)

Don’t sell on bad news unless intrinsic value has dropped materially.  When the stock drops significantly, buy more as long as the investment thesis is intact.

Schloss’s average holding period was 4 years.  It was less than 4 years in good markets when stocks went up more than usual.  It was greater than 4 years in bad markets when stocks stayed flat or went down more than usual.

 

HAVE COURAGE

Have the courage of your convictions once you have made a decision.

(Courage concept by Travelling-light)

Investors shun companies with depressed earnings and cash flows.  It’s painful to own stocks that are widely hated.  It can also be frightening.  As John Mihaljevic explains in The Manual of Ideas (Wiley, 2013):

Playing into the psychological discomfort of Graham-style equities is the tendency of such investments to exhibit strong asset value but inferior earnings or cash flows.  In a stressed situation, investors may doubt their investment theses to such an extent that they disregard the objectively appraised asset values.  After all—the reasoning of a scared investor might go—what is an asset really worth if it produces no cash flow?

A related worry is that if a company is burning through its cash, it will gradually destroy net asset value.  Ben Graham:

If the profits had been increasing steadily it is obvious that the shares would not sell at so low a price.  The objection to buying these issues lies in the probability, or at least the possibility, that earnings will decline or losses continue, and that the resources will be dissipated and the intrinsic value ultimately become less than the price paid.

It’s true that an individual cigar butt (deep value stock) is more likely to underperform than an average stock.  But because the potential upside for a typical cigar butt is greater than the potential downside, a basket of cigar butts (portfolio of at least 30) does better than the market over time and also has less downside during bad states of the world—such as bear markets and recessions.

Schloss discussed an example: Cleveland Cliffs, an iron ore producer.  Buffett owned the stock at $18 but then sold at about that level.  The steel industry went into decline.  The largest shareholder sold out because he thought the industry wouldn’t recover.

Schloss bought a lot of stock at $6.  Nobody wanted it.  There was talk of bankruptcy.  Schloss noted that if he had lived in Cleveland, he probably wouldn’t have been able to buy the stock because all the bad news would have been too close.

Soon thereafter, the company sold some assets and bought back some stock.  After the stock increased a great deal from the lows, then it started getting attention from analysts.

In sum, often when an industry is doing terribly, that’s the best time to find cheap stocks.  Investors avoid stocks when they’re having problems, which is why they get so cheap.  Investors overreact to negative news.

 

BUY ASSETS NOT EARNINGS

(Illustration by Teguh Jati Prasetyo)

Schloss:

Try to buy assets at a discount [rather] than to buy earnings.  Earnings can change dramatically in a short time.  Usually assets change slowly.  One has to know much more about a company if one buys earnings.

Not only can earnings change dramatically; earnings can easily be manipulated—often legally.  Schloss:

Ben made the point in one of his articles that if U.S. Steel wrote down their plants to a dollar, they would show very large earnings because they would not have to depreciate them anymore.

 

BUY BASED ON CHEAPNESS NOW, NOT CHEAPNESS LATER

Buy things based on cheapness now.  Don’t buy based on cheapness relative to future earnings, which are hard to predict.

Graham developed two ways of estimating intrinsic value that don’t depend on predicting the future:

  • Net asset value
  • Current and past earnings

Professor Bruce Greenwald, in Value Investing (Wiley, 2004), has expanded on these two approaches.

  • As Greenwald explains, book value is a good estimate of intrinsic value if book value is close to the replacement cost of the assets.  The true economic value of the assets is the cost of reproducing them at current prices.
  • Another way to determine intrinsic value is to figure out earnings power—also called normalized earnings—or how much the company should earn on average over the business cycle.  Earnings power typically corresponds to a market level return on the reproduction value of the assets.  In this case, your intrinsic value estimate based on normalized earnings should equal your intrinsic value estimate based on the reproduction value of the assets.

In some cases, earnings power may exceed a market level return on the reproduction value of the assets.  This means that the ROIC (return on invested capital) exceeds the cost of capital.  It can be exceedingly difficult, however, to determine by how much and for how long earnings power will exceed a market level return.  Often it’s a question of how long some competitive advantage can be maintained.  How long can a high ROIC be sustained?

As Buffett remarked:

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.

A moat is a sustainable competitive advantage.  Schloss readily admits he can’t determine which competitive advantages are sustainable.  That requires unusual insight.  Buffett can do it, but very few investors can.

As far as franchises or good businesses—companies worth more than adjusted book value—Schloss says he likes these companies, but rarely considers buying them unless the stock is close to book value.  As a result, Schloss usually buys mediocre and bad businesses at book value or below.  Schloss buys “difficult businesses” at clearly cheap prices.

Buying a high-growing company on the expectation that growth will continue can be quite dangerous.  First, growth only creates value if the ROIC exceeds the cost of capital.  Second, expectations for the typical growth stock are so high that even a small slowdown can cause the stock to drop noticeably.  Schloss:

If observers are expecting the earnings to grow from $1.00 to $1.50 to $2.00 and then $2.50, an earnings disappointment can knock a $40 stock down to $20.  You can lose half your money just because the earnings fell out of bed.

If you buy a debt-free stock with a $15 book selling at $10, it can go down to $8.  It’s not great, but it’s not terrible either.  On the other hand, if things turn around, that stock can sell at $25 if it develops its earnings.

Basically, we like protection on the downside.  A $10 stock with a $15 book can offer pretty good protection.  By using book value as a parameter, we can protect ourselves on the downside and not get hurt too badly.

Also, I think the person who buys earnings has got to follow it all the darn time.  They’re constantly driven by earnings, they’re driven by timing.  I’m amazed.

 

BOEING:  ASSET PLAY

(Boeing 377 Stratocruiser, San Diego Air & Space Museum Archives, via Wikimedia Commons)

Cigar butts—deep value stocks—are characterized by two things:

  • Poor past performance;
  • Low expectations for future performance, i.e., low multiples (low P/B, low P/E, etc.)

Schloss has pointed out that Graham would often compare two companies.  Here’s an example:

One was a very popular company with a book value of $10 selling at $45.  The second was exactly the reverse—it had a book value of $40 and was selling for $25.

In fact, it was exactly the same company, Boeing, in two very different periods of time.  In 1939, Boeing was selling at $45 with a book of $10 and earning very little.  But the outlook was great.  In 1947, after World War II, investors saw no future for Boeing, thinking no one was going to buy all these airplanes.

If you’d bought Boeing in 1939 at $45, you would have done rather badly.  But if you’d bought Boeing in 1947 when the outlook was bad, you would have done very well.

Because a cigar butt is defined by poor recent performance and low expectations, there can be a great deal of upside if performance improves.  For instance, if a stock is at a P/E (price-to-earnings ratio) of 5 and if earnings are 33% of normal, then if earnings return to normal and if the P/E moves to 15, you’ll make 900% on your investment.  If the initial purchase is below true book value—based on the replacement cost of the assets—then you have downside protection in case earnings don’t recover.

 

LESS DOWNSIDE MEANS MORE UPSIDE

If you buy stocks that are protected on the downside, the upside takes care of itself.

The main way to get protection on the downside is by paying a low price relative to book value.  If in addition to quantitative cheapness you focus on companies with low debt, that adds additional downside protection.

If the stock is well below probable intrinsic value, then you should buy more on the way down.  The lower the price relative to intrinsic value, the less downside and the more upside.  As risk decreases, potential return increases.  This is the opposite of what modern finance theory teaches.  According to theory, your expected return only increases if your risk also increases.

In The Superinvestors of Graham-and-Doddsville, Warren Buffett discusses the relationship between risk and reward.  Sometimes risk and reward are positively correlated.  Buffett gives the example of Russian roulette.  Suppose a gun contains one cartridge and someone offers to pay you $1 million if you pull the trigger once and survive.  Say you decline the bet as too risky, but then the person offers to pay you $5 million if you pull the trigger twice and survive.  Clearly that would be a positive correlation between risk and reward.  Buffett continues:

The exact opposite is true with value investing.  If you buy a dollar bill for 60 cents, it’s riskier than if you buy a dollar bill for 40 cents, but the expectation of reward is greater in the latter case.  The greater the potential for reward in the value portfolio, the less risk there is.

One quick example:  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.  The company owned the Post, Newsweek, plus several television stations in major markets.  Those same properties are worth $2 billion now, so the person who would have paid $400 million would not have been crazy.

Now, if the stock had declined even further to a price that made the valuation $40 million instead of $80 million, its beta would have been greater.  And to people that think beta 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.

Link: https://bit.ly/2jBezdv

Most brokers don’t recommend buying more on the way down because most people (including brokers’ clients) don’t like to buy when the price keeps falling.  In other words, most investors focus on price instead of intrinsic value.

 

MULTIPLE WAYS TO WIN

A stock trading at a low price relative to book value—a low P/B stock—is usually distressed and is experiencing problems.  But there are several ways for a cigar-butt investor to win, as Schloss explains:

The thing about buying depressed stocks is that you really have three strings to your bow:  1) Earnings will improve and the stocks will go up;  2) somebody will come in and buy control of the company;  or 3) the company will start buying its own stock and ask for tenders.

Schloss again:

But lots of times when you buy a cheap stock for one reason, that reason doesn’t pan out but another reason does—because it’s cheap.

 

HISTORY;  HONESTY;  INSIDER OWNERSHIP

Look at the history of the company.  Value line is helpful for looking at history 10-15 years back.  Also, read the annual reports.  Learn about the ownership, what the company has done, when business they’re in, and what’s happened with dividends, sales, earnings, etc.

It’s usually better not to talk with management because it’s easy to be blinded by their charisma or sales skill:

When we buy into a company that has problems, we find it difficult talking to management as they tend to be optimistic.

That said, try to ensure that management is honest.  Honesty is more important than brilliance, says Schloss:

…we try to get in with people we feel are honest.  That doesn’t mean they’re necessarily smart—they may be dumb.

But in a choice between a smart guy with a bad reputation or a dumb guy, I think I’d go with the dumb guy who’s honest.

Finally, insider ownership is important.  Management should own a fair amount of stock, which helps to align their incentives with the interests of the stockholders.

Speaking of insider ownership, Walter and Edwin Schloss had a good chunk of their own money invested in the fund they managed.  You should prefer investment managers who, like the Schlosses, eat their own cooking.

 

YOU MUST BE WILLING TO MAKE MISTAKES

(Illustration by Lkeskinen0)

You have to be willing to make mistakes if you want to succeed as an investor.  Even the best value investors tend to be right about 60% of the time and wrong 40% of the time.  That’s the nature of the game.

You can’t do well unless you accept that you’ll make plenty of mistakes.  The key, again, is to try to limit your downside by buying well below probable intrinsic value.  The lower the price you pay (relative to estimated intrinsic value), the less you can lose when you’re wrong and the more you can make when you’re right.

 

DON’T TRY TO TIME THE MARKET

No one can predict the stock market.  Ben Graham observed:

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.

(Illustration by Maxim Popov)

Or as value investor Seth Klarman has put it:

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.

Perhaps the best quote comes from Henry Singleton, a business genius (100 points from being a chess grandmaster) who was easily one of the best capital allocators in American business history:

I don’t believe all this nonsense about market timing.  Just buy very good value and when the market is ready that value will be recognized.

Singleton built Teledyne using extraordinary capital allocation skills over the course of more than three decades, from 1960 to the early 1990’s.  Fourteen of these years—1968 to 1982—were a secular bear market during which stocks were relatively flat and also experienced a few large downward moves (especially 1973-1974).  But this long flat period punctuated by bear markets didn’t slow down or change Singleton’s approach.  Because he consistently bought very good value, on the whole his acquisitions grew significantly in worth over time regardless of whether the broader market was down, flat, or up.

Of course, it’s true that if you buy an undervalued stock and then there’s a bear market, it may take longer for your investment to work.  However, bear markets create many bargains.  As long as you maintain a focus on the next 3 to 5 years, bear markets are wonderful times to buy cheap stocks (including more of what you already own).

In 1955, Buffett was advised by his two heroes, his father and Ben Graham, not to start a career in investing because the market was too high.  Similarly, Graham told Schloss in 1955 that it wasn’t a good time to start.

Both Buffett and Schloss ignored the advice.  In hindsight, both Buffett and Schloss made great decisions.  Of course, Singleton would have made the same decision as Buffett and Schloss.  Even if the market is high, there are invariably individual stocks hidden somewhere that are cheap.

Schloss always remained fully invested because he knew that virtually no one can time the market except by luck.

 

WHEN TO SELL

Don’t be in too much of a hurry to sell… Before selling try to reevaluate the company again and see where the stock sells in relation to its book value.

Selling is hard.  Schloss readily admits that many stocks he sold later increased a great deal.  But he doesn’t dwell on that.

The basic criterion for selling is whether the stock price is close to estimated intrinsic value.  For a cigar butt investor like Schloss, if he paid a price that was half book, then if the stock price approaches book value, it’s probably time to start selling.  (Unless it’s a rare stock that is clearly worth more than book value, assuming the investor was able to buy it low in the first place.)

If stock A is cheaper than stock B, some value investors will sell A and buy B.  Schloss doesn’t do that.  It often takes four years for one of Schloss’s investments to work.  If he already has been waiting for 1-3 years with stock A, he is not inclined to switch out of it because he might have to wait another 1-3 years before stock B starts to move.  Also, it’s very difficult to compare the relative cheapness of stocks in different industries.

Instead, Schloss makes an independent buy or sell decision for every stock.  If B is cheap, Schloss simply buys B without selling anything else.  If A is no longer cheap, Schloss sells A without buying anything else.

 

THE FIRST 10 YEARS ARE PROBABLY THE WORST

John Templeton’s worst ten years as an investor were his first ten years.  The same was true for Schloss, who commented that it takes about ten years to get the hang of value investing.

 

STAY INFORMED ABOUT CURRENT EVENTS

(Photo by Juan Moyano)

Walter Schloss and his son Edwin sometimes would spend a whole day discussing current events, social trends, etc.  Edwin Schloss said:

If you’re not in touch with what’s going on or you don’t see what’s going on around you, you can miss out on a lot of investment opportunities. So we try to be aware of everything around us—like John Templeton says in his book about being open to new ideas and new experiences.

 

CONTROL YOUR EMOTIONS;  BE CAREFUL OF LEVERAGE

Try not to let your emotions affect your judgment.  Fear and greed are probably the worst emotions to have in connection with the purchase and sale of stocks.

Quantitative investing is a good way to control emotion.  This is what Graham suggested and practiced.  Graham just looked at the numbers to make sure they were below some threshold—like 2/3 of current assets minus all liabilities (the net-net method).  Graham typically was not interested in what the business did.

On the topic of discipline and controlling your emotions, Schloss told a great story about when Warren Buffett was playing golf with some buddies:

One of them proposed, “Warren, if you shoot a hole-in-one on this 18-hole course, we’ll give you $10,000 bucks.  If you don’t shoot a hole-in-one, you owe us $10.”

Warren thought about it and said, “I’m not taking the bet.”

The others said, “Why don’t you?  The most you can lose is $10. You can make $10,000.”

Warren replied, If you’re not disciplined in the little things, you won’t be disciplined in the big things.”

Be careful of leverage.  It can go against you.  Schloss acknowledges that sometimes he has gotten too greedy by buying highly leveraged stocks because they seemed really cheap.  Companies with high leverage can occasionally become especially cheap compared to book value.  But often the risk of bankruptcy is too high.

Still, as conservative value investor Seth Klarman has remarked, there’s room in the portfolio occasionally for a super cheap, highly indebted company.  If the probability of success is high enough and if the upside is great enough, it may not be a difficult decision.  Often the upside can be 10x or 20x your investment, which implies a positive expected return even when the odds of success are 10%.

 

RIDE COATTAILS;  DIVERSIFY

Sometimes you can get good ideas from other investors you know or respect.  Even Buffett did this.  Buffett called it “coattail riding.”

Schloss, like Graham and Buffett, recommends a diversified approach if you’re doing cigar butt (deep value) investing.  Have at least 15-20 stocks in your portfolio.  A few investors can do better by being more concentrated.  But most investors will do better over time by using a quantitative, diversified approach.

Schloss tended to have about 100 stocks in his portfolio:

…And my argument was, and I made it to Warren, we can’t project the earnings of these companies, they’re secondary companies, but somewhere along the line some of them will work out.  Now I can’t tell you which ones, so I buy a hundred of them.  Of course, it doesn’t mean you own the same amount of each stock.  If we like a stock we put more money in it.  Positions we are less sure about we put less in… We then buy the stock on the way down and try to sell it on the way up.

Even though Schloss was quite diversified, he still took larger positions in the stocks he liked best and smaller positions in the stocks about which he was less sure.

Schloss emphasized that it’s important to know what you know and what you don’t know.  Warren Buffett and Charlie Munger call this a circle of competence.  Even if a value investor is far from being the smartest, there are hundreds of microcap companies that are easy to understand with enough work.

(Image by Wilma64)

The main trouble in investing is overconfidence: having more confidence than is warranted by the evidence.  Overconfidence is arguably the most widespread cognitive bias suffered by humans, as Nobel Laureate Daniel Kahneman details in Thinking, Fast and Slow.  By humbly defining your circle of competence, you can limit the impact of overconfidence.  Part of this humility comes from making mistakes.

The best choice for most investors is either an index fund or a quantitative value fund.  It’s the best bet for getting solid long-term returns, while minimizing or removing entirely the negative influence of overconfidence.

 

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:  http://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

(Image:  Zen Buddha Silence by Marilyn Barbone.)

September 22, 2019

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: http://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:  http://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 Microcap Value

(Image:  Zen Buddha Silence by Marilyn Barbone.)

September 8, 2019

Jack Bogle and Warren Buffett correctly maintain that most investors should invest in an S&P 500 index fund.  An index fund will allow you to outpace 90-95% of all active investors—net of costs—over the course of 4-5 decades.  This is purely a function of cost.  Active investors as a group will do the same as the S&P 500, but that is before costs.  After costs, active investors will do about 2.5% worse per year than the index.

An index fund is a wise choice.  But you can do much better if you invest in a quantitative microcap value strategy—focused on undervalued microcap stocks with improving fundamentals.  If you adopt such an approach, you can outperform the S&P 500 by roughly 7% per year.  For details, see: http://boolefund.com/cheap-solid-microcaps-far-outperform-sp-500/

But this can only work if you have the ability to ignore volatility and stay focused on the very long term.

“Investing is simple but not easy.” — Warren Buffett

(Photo by USA International Trade Administration)

Assume the S&P 500 index will return 8% per year over the coming decades.  The average active approach will produce roughly 5.5% per year.  A quantitative microcap approach—cheap micro caps with improving fundamentals—will generate about 15% per year.

What would happen if you invested $50,000 for the next 30 years in one of these approaches?

Investment Strategy Beginning Value Ending Value
Active $50,000 $249,198
S&P 500 Index $50,000 $503,133
Quantitative Microcap $50,000 $3,310,589

As you can see, investing $50,000 in an index fund will produce $503,133, which is more than ten times what you started with.  Furthermore, $503,133 is more than twice $249,198, which would be the result from the average active fund.

However, if you invested $50,000 in a quantitative microcap strategy, you would end up with $3,310,589.  This is more than 66 times what you started with, and it’s more than 6.5 times greater than the result from the index fund.

You could either invest in a quantitative microcap approach or you could invest in an index fund.  You’ll do fine either way.  Or you could invest part of your portfolio in the microcap strategy and part in an index fund.

What’s the catch?

For most of us as investors, our biggest enemy is ourselves.  Let me explain.  Since 1945, there have been 27 corrections where stocks dropped 10% to 20%, and there have been 11 bear markets where stocks dropped more than 20%.  The stock market has always recovered and gone on to new highs.  However, many investors have gotten scared and sold their investments after stocks have dropped 10-20%+.

Edgar Wachenheim, in the great book Common Stocks and Common Sense, gives the following example:

The financial crisis during the fall of 2008 and the winter of 2009 is an extreme (and outlier) example of volatility.  During the six months between the end of August 2008 and end of February 2009, the [S&P] 500 Index fell by 42 percent from 1,282.83 to 735.09.  Yet by early 2011 the S&P 500 had recovered to the 1,280 level, and by August 2014 it had appreciated to the 2000 level.  An investor who purchased the S&P 500 Index on August 31, 2008, and then sold the Index six years later, lived through the worst financial crisis and recession since the Great Depression, but still earned a 56 percent profit on his investment before including dividends—and 69 percent including the dividends… During the six-year period August 2008 through August 2014, the stock market provided an average annual return of 11.1 percent—above the range of normalcy in spite of the abnormal horrors and consequences of the financial crisis and resulting deep recession.

If you can stay the course through a 25% drop and even through a 40%+ drop, and remain focused on the very long term, then you should invest primarily in stocks, whether via an index fund, a quantitative microcap value fund, or some other investment vehicle.

The best way to stay focused on the very long term is simply to ignore the stock market entirely.  All you need to know or believe is:

  • The U. S. and global economies will continue to grow, mainly due to improvements in technology.
  • After every correction or bear market—no matter how severe—the stock market has always recovered and gone on to new highs.

If you’re unable to ignore the stock market, and if you might get scared and sell during a correction or bear market—don’t worry if you’re in this category since many investors are—then you should try to invest a manageable portion of your liquid assets in stocks.  Perhaps investing 50% or 25% of your liquid assets in stocks will allow you to stay the course through the inevitable corrections and bear markets.

The best-performing investors will be those who can invest for the very long term—several decades or more—and who don’t worry about (or even pay any attention to) the inevitable corrections and bear markets along the way.  In fact, Fidelity did a study of its many retail accounts.  It found that the best-performing accounts were owned by investors who literally forgot that they had an account!

  • Note: If you were to buy and hold twenty large-cap stocks chosen at random, your long-term performance would be very close to the S&P 500 Index.  (The Dow Jones Industrial Average is a basket of thirty large-cap stocks.)

Bottom Line

If you’re going to be investing for a few decades or more, and if you can basically ignore the stock market in the meantime, then you should invest fully in stocks.  Your best long-term investment is an index fund, a quantitative microcap value fund, or a combination of the two.

If you can largely ignore volatility, then you should consider investing primarily in a quantitative microcap value fund.  This is very likely to produce far better long-term performance than an S&P 500 index fund.

Many top investors—including Warren Buffett, perhaps the greatest investors of all time—earned the highest returns of their career when they could invest in microcap stocks.  Buffett has said that he’d still be investing in micro caps if he were managing small sums.

To learn more about Buffett getting his highest returns mainly from undervalued microcaps, here’s a link to my favorite blog post: http://boolefund.com/buffetts-best-microcap-cigar-butts/

The Boole Microcap Fund that I manage is a quantitative microcap value fund.  For details on the quantitative investment process, see: http://boolefund.com/why-invest-in-boole-microcap/

Although the S&P 500 index appears rather high—a bear market in the next year or two wouldn’t be a surprise—the positions in the Boole Fund are quite undervalued.  When looking at the next 3 to 5 years, I’ve never been more excited about the prospects of the Boole Fund relative to the S&P 500—regardless of whether the index is up, down, or flat.

(The S&P 500 may be flat for 5 years or even 10 years, but after that, as you move further into the future, eventually there’s more than a 99% chance that the index will be in positive territory.  The longer your time horizon, the less risky stocks are.)

 

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:  http://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 Success Equation: Untangling Skill and Luck

(Image:  Zen Buddha Silence by Marilyn Barbone.)

August 25, 2019

Michael Mauboussin wrote a great book called The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing (Harvard Business Press, 2012).

Here’s an outline for this blog post:

  • Understand were you are on the skill-luck continuum
  • Assess sample size, significance, and swans
  • Always consider a null hypothesis
  • Think carefully about feedback and rewards
  • Make use of counterfactuals
  • Develop aids to guide and improve your skill
  • Have a plan for strategic interactions
  • Make reversion to the mean work for you
  • Know your limitations

 

UNDERSTAND WHERE YOU ARE ON THE SKILL-LUCK CONTINUUM

If an activity is mostly skill or mostly luck, it’s generally easy to classify it as such.  But many activities are somewhere in-between the two extremes, and it’s often hard to say where it falls on the continuum between pure skill and pure luck.

An activity dominated by skill means that results can be predicted reasonably well.  (You do need to consider the rate at which skill changes, though.)  A useful statistic is one that is persistent—the current outcome is highly correlated with the previous outcome.

An activity dominated by luck means you need a very large sample to detect the influence of skill.  The current outcome is not correlated with the previous outcome.

Obviously the location of an activity on the continuum gives us guidance on how much reversion to the mean is needed in making a prediction.  In an activity that is mostly skill, the best estimate for the next outcome is the current outcome.  In an activity that is mostly luck, the best guess for the next outcome is close to the base rate (the long-term average), i.e., nearly a full reversion to the mean.

Our minds by nature usually fail to regress to the mean as much as we should.  That’s because System 1—the automatic, intuitive part of our brain—invents coherent stories based on causality.  This worked fine during most of our evolutionary history.  But when luck plays a significant role, there has to be substantial reversion to the mean when predicting the next outcome.

 

ASSESS SAMPLE SIZE, SIGNIFICANCE, AND SWANS

Even trained scientists have a tendency to believe that a small sample of a population is representative of the whole population.  But a small sample can deviate meaningfully from the larger population.

If an activity is mostly skill, then a small sample will be representative of the larger population from which it is drawn.  If an activity is mostly luck, then a small sample can be significantly different from the larger population.  A small sample is not reliable when an activity is mostly luck—we need a large sample in this case in order to glean information.

In business, it would be an error to create a sample of all the companies that used a risky strategy and won, without also taking into account all the companies that used the same strategy and lost.  A narrow sample of just the winners would obviously be a biased view of the strategy’s quality.

Also be careful not to confuse statistical significance with economic significance.  Mauboussin quotes Deirdre McCloskey and Stephen Ziliak: “Tell me the oomph of your coefficient; and do not confuse it with mere statistical significance.”

Lastly, it’s important to keep in mind that some business strategies can produce a long series of small gains, followed by a huge loss.  Most of the large U.S. banks pursued such a strategy from 2003-2007.  It would obviously be a big mistake to conclude that a long series of small gains is safe if in reality it is not.

Another example of ignoring black swans is Long-Term Capital Management.  The fund’s actual trades were making about 1% per year.  But LTCM argued that these trades had infintessimally small risk, and so they levered the trades at approximately 40:1.  Many banks didn’t charge LTCM anything for the loan because LTCM was so highly regarded at the time, having a couple of Nobel Prize winners, etc.  Then a black swan arrived—the Asian financial crisis in 1998.  LTCM’s trades went against them, and because of the astronomically high leverage, the fund imploded.

 

ALWAYS CONSIDER A NULL HYPOTHESIS

Always compare the outcomes to what would have been generated under the null hypothesis.  Many streaks can easily be explained by luck alone.

Mauboussin gives the example of various streaks of funds beating the market.  Andrew Mauboussin and Sam Arbesman did a study on this.  They assumed that the probability a given fund would beat the S&P 500 Index was equal to the fraction of active funds that beat the index during a given year.  For example, 52 percent of funds beat the S&P 500 in 1993, so the null model assigns the same percentage probability that any given fund would beat the market in that year.  Mauboussin and Arbesman then ran ten thousand random simulations.

They determined that, under the null model—pure luck and no skill—146.9 funds would have a 5-year market-beating streak, 53.6 funds would have a 6-year streak, 21.4 funds would have a 7-year streak, 7.6 funds would have an 8-year streak, and 3.0 funds would have a 9-year streak.  They compared these figures to the actual empirical frequencies:  206 funds had 5-year streaks, 119 had 6-year streaks, 75 had 7-year streaks, 23 had 8-year streaks, and 28 had 9-year streaks.

So there were many more streaks in the empirical data than the null model generated.  This meant that some of those streaks involved the existence of skill.

 

THINK CAREFULLY ABOUT FEEDBACK AND REWARDS

Everybody wants to improve.  The keys to improving performance include high-quality feedback and proper rewards.

Only a small percentage of people achieve expertise through deliberate practice.  Most people hit a performance plateau and are satisfied to stay there.  Of course, for many activities—like driving—that’s perfectly fine.

The deliberate practice required to develop true expertise involves a great deal of hard and tedious work.  It is not pleasant.  It requires thousands of hours of very focused effort.  And there must be a lot of timely and accurate feedback in order for someone to keep improving and eventually attain expertise.

Even if you’re not pursuing expertise, the keys to improvement are still focused practice and high-quality feedback.

In activities where skill plays a significant role, actual performance is a reasonable measure of progress.  Where luck plays a strong role, the focus must be on the process.  Over shorter periods of time—more specifically, over a relatively small number of trials—a good process can lead to bad outcomes, and a bad process can lead to good outcomes.  But over time, with a large number of trials, a good process will yield good outcomes overall.

The investment industry struggles in this area.  When a strategy does well over a short period of time, quite often it is marketed and new investors flood in.  When a strategy does poorly over a short period of time, very often investors leave.  Most of the time, these strategies mean revert, so that the funds that just did well do poorly and the funds that just did poorly do well.

Another area that’s gone off-track is rewards for executives.  Stock options have become a primary means of rewarding executives.  But the payoff from a stock option involves a huge amount of randomness.  In the decade of the 1990’s, even poor-performing companies saw their stocks increase a great deal.  In the decade of the 2000’s, many high-performing companies saw their stocks stay relatively flat.  So stock options on the whole have not distinguished between skill and luck.

A solution would involve having the stock be measured relative to an index or relative to an appropriate peer group.  Also, the payoff from options could happen over longer periods of time.

Lastly, although executives—like the CEO—are much more skillful than their junior colleagues, often executive success depends to a large extent on luck while the success of those lower down can be attributed almost entirely to skill.  For instance, the overall success of a company may only have a 0.3 correlation with the skill of the CEO.  And yet the CEO would be paid as if the company’s success was highly correlated with his or her skill.

 

MAKE USE OF COUNTERFACTUALS

Once we know what happened in history, hindsight bias naturally overcomes us and we forget how unpredictable the world looked beforehand.  We come up with reasons to explain past outcomes.  The reasons we invent typically make it seem as if the outcomes were inevitable when they may have been anything but.

Mauboussin says a good way to avoid hindsight bias is to engage in counterfactual thinking—a careful consideration of what could have happened but didn’t.

Mauboussin gives an example in Chapter 6 of the book: MusicLab.  Fourteen thousand people were randomly divided into 8 groups—each 10% of the total number of people—and one independent group—20% of the total number of people.  There were forty-eight songs from unknown bands.  In the independent group, each person could listen to each song and then decide to download it based on that alone.  In the other 8 groups, for each song, a person would see how many other people in his or her group had already downloaded the song.

You could get a reasonable estimate for the “objective quality” of a song by looking at how the independent group rated them.

But in the 8 “social influence” groups, strange things happened based purely on luck—or which songs were downloaded early on and which were not.  For instance, a song “Lockdown” was rated twenty-sixth in the independent group.  But it was the number-one hit in one of the social influence worlds and number forty in another.

In brief, to maintain an open mind about the future, it is very helpful to maintain an open mind about the past.  We have to work hard to overcome our natural tendency to view what happened as having been inevitable.  System 1 always creates a story based on causality—System 1 wants to explain simply what happened and close the case.

If we do the Rain Dance and it rains, then to the human brain, it looks like the dance caused the rain.

But when we engage System 2 (the logical, mathematical part of our brain)—which requires conscious effort—we can come to realize that the Rain Dance didn’t cause the rain.

 

DEVELOP AIDS TO GUIDE AND IMPROVE YOUR SKILL

Depending on where an activity lies on the pure luck to pure skill continuum, there are different ways to improve skill.

When luck predominates, to improve our skill we have to focus on learning the process for making good decisions.  A good process must be well grounded in three areas:

  • analytical
  • psychological
  • organizational

In picking an undervalued stock, the analytical part means finding a discrepancy between price and value.

The psychological part of a good process entails an identification of the chief cognitive biases, and techniques to mitigate the influence of these cognitive biases.  For example, we all tend to be wildly overconfident when we make predictions.  System 1 automatically makes predictions all the time.  Usually this is fine.  But when the prediction involves a probabilistic area of life—such as an economy, a stock market, or a political situation—System 1 makes errors systematically.  In these cases, it is essential to engage System 2 in careful statistical thinking.

The organizational part of a good process should align the interests of principals and agents—for instance, shareholders (principals) and executives (agents).  If the executives own a large chunk of stock, then their interests are much more aligned with shareholder interests.

Now consider the middle of the continuum between luck and skill.  In this area, a checklist can be very useful.  A doctor caring for a patient is focused on the primary problem and can easily forget about the simple steps required to minimize infections.  Following the suggestion of Peter Pronovost, many hospitals have introduced simple checklists.  Thousands of lives and hundreds of millions of dollars have been saved, as the checklists have significantly reduced infections and deaths related to infections.

A checklist can also help in a stressful situation.  The chemicals of stress disrupt the functioning of the frontal lobes—the seat of reason.  So a READ-DO checklist gets you to take the concrete, important steps even when you’re not thinking clearly.

Writes Mauboussin:

Checklists have never been shown to hurt performance in any field, and they have helped results in a great many instances.

Finally, anyone serious about improving their performance should write down—if possible—the basis for every decision and then measure honestly how each decision turned out.  This careful measurement is the foundation for continual improvement.

The last category involves activities that are mostly skill.  The key to improvement is deliberate practice and good feedback.  A good coach can be a great help.

Even experts benefit from a good coach.  Feedback is the single most powerful way to improve skill.  Being open to honest feedback is difficult because it means being willing to admit where we need to change.

Mauboussin concludes:

One simple and inexpensive technique for getting feedback is to keep a journal that tracks your decisions.  Whenever you make a decision, write down what you decided, how you came to that decision, and what you expect to happen.  Then, when the results of that decision are clear, write them down and compare them with what you thought would happen.  The journal won’t lie.  You’ll see when you’re wrong.  Change your behavior accordingly.

 

HAVE A PLAN FOR STRATEGIC INTERACTIONS

The weaker side won more conflicts in the twentieth century than in the nineteenth.  This is because the underdogs learned not to go toe-to-toe with a stronger foe.  Instead, the underdogs pursued alternative tactics, like guerrilla warfare.  If you’re an underdog, complicate the game by injecting more luck.

Initially weaker companies almost never succeed by taking on established companies in their core markets.  But, by pursuing disruptive innovation—as described by Professor Clayton Christensen—weaker companies can overcome stronger companies.  The weaker companies pursue what is initially a low-margin part of the market.  The stronger companies have no incentive to invest in low-margin innovation when they have healthy margins in more established areas.  But over time, the low-margin technology improves to the point where demand for it increases and profit margins typically follow.  By then, the younger companies are already ahead by a few of years, and the more established companies usually are unable to catch up.

 

MAKE REVERSION TO THE MEAN WORK FOR YOU

Mauboussin writes:

We are all in the business of forecasting.

Reversion to the mean is difficult for our brains to understand.  As noted, System 1 always invents a cause for everything that happens.  But often there is no specific cause.

Mauboussin cites an example given by Daniel Kahneman: Julie is a senior in college who read fluently when she was four years old.  Estimate her GPA.

People often guess a GPA of around 3.7.  Most people assume that being precocious is correlated with doing well in college.  But it turns out that reading at a young age is not related to doing well in college.  That means the best guess for the GPA would be much closer to the average.

Mauboussin adds:

Reversion to the mean is most pronounced at the extremes, so the first lesson is to recognize that when you see extremely good or bad results, they are unlikely to continue that way.  This doesn’t mean that good results will necessarily be followed by bad results, or vice versa, but rather that the next thing that happens will probably be closer to the average of all things that happen.

 

KNOW YOUR LIMITATIONS

There is always a great deal that we simply don’t know and can’t know.  We must develop and maintain a healthy sense of humility.

Predictions are often difficult in many situations.  The sample size and the length of time over which you measure are essential.  And you need valid data.

Moreover, things can change.  If fiscal policy has become much more stimulative than it used to be, then bear markets may—or may not—be shallower and shorter.  And stocks may generally be higher than previously, as Ben Graham pointed out in a 1963 lecture, “Securities in an Insecure World”: http://jasonzweig.com/wp-content/uploads/2015/04/BG-speech-SF-1963.pdf

If monetary policy is much more stimulative than before—including a great deal of money-printing and zero or negative interest rates—then the long-term average of stock prices could conceivably make another jump higher.

The two fundamental changes just mentioned are part of why most great value investors never try to time the market.  As Buffett has said:

  • Forecasts may tell you a great deal about the forecaster;  they tell you nothing about the future.
  • I make no effort to predict the course of general business or the stock market.  Period.
  • I don’t invest a dime based on macro forecasts.

Henry Singleton—who has one of the best capital allocation records of all time—perhaps put it best:

I don’t believe all this nonsense about market timing.  Just buy very good value and when the market is ready that value will be recognized.

 

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:  http://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.