Learning Makes Us Better People

(Image: Zen Buddha Silence, by Marilyn Barbone)

January 14, 2018

To boost our productivity—including our ability to think and make decisions—nothing beats continuous learning.  Broad study makes us better people.  See: http://boolefund.com/lifelong-learning/

Michael Mauboussin is a leading expert in the multidisciplinary study of businesses and markets.  His book—More Than You Know: Finding Financial Wisdom in Unconventional Places—has been translated into eight languages.

Each chapter in Mauboussin’s book is meant to stand on its own.  I’ve summarized most of the chapters below.

Here’s an outline:

  • Process and Outcome in Investing
  • Risky Business
  • Are You an Expert?
  • The Hot Hand in Investing
  • Time is on my Side
  • The Low Down on the Top Brass
  • Six Psychological Tendencies
  • Emotion and Intuition in Decision Making
  • Beware of Behavioral Finance
  • Importance of a Decision Journal
  • Right from the Gut
  • Weighted Watcher
  • Why Innovation is Inevitable
  • Accelerating Rate of Industry Change
  • How to Balance the Long Term with the Short Term
  • Fitness Landscapes and Competitive Advantage
  • The Folly of Using Average P/E’s
  • Mean Reversion and Turnarounds
  • Considering Cooperation and Competition Through Game Theory
  • The Wisdom and Whim of the Collective
  • Vox Populi
  • Complex Adaptive Systems
  • The Future of Consilience in Investing

(Photo: Statue of Leonardo da Vinci in Italy, by Raluca Tudor)

 

PROCESS AND OUTCOME IN INVESTING

(Image by Amir Zukanovic)

Individual decisions can be badly thought through, and yet be successful, or exceedingly well thought through, but be unsuccessful, because the recognized possibility of failure in fact occurs.  But over time, more thoughtful decision-making will lead to better overall results, and more thoughtful decision-making can be encouraged by evaluating decisions on how well they were made rather than on outcome.

Robert Rubin made this remark in his Harvard Commencement Address in 2001.  Mauboussin points out that the best long-term performers in any probabilistic field—such as investing, bridge, sports-team management, and pari-mutuel betting—all emphasize process over outcome.

Mauboussin also writes:

Perhaps the single greatest error in the investment business is a failure to distinguish between the knowledge of a company’s fundamentals and the expectations implied by the market price.

If you don’t understand why your view differs from the consensus, and why the consensus is likely to be wrong, then you cannot reasonably expect to beat the market.  Mauboussin quotes horse-race handicapper Steven Crist:

The issue is not which horse in the race is the most likely winner, but which horse or horses are offering odds that exceed their actual chances of victory… This may sound elementary, and many players may think that they are following this principle, but few actually do.  Under this mindset, everything but the odds fades from view.  There is no such thing as “liking” a horse to win a race, only an attractive discrepancy between his chances and his price.

Robert Rubin’s four rules for probabilistic decision-making:

  • The only certainty is that there is no certainty.  It’s crucial not to be overconfident, because inevitably that leads to big mistakes.  Many of the biggest hedge fund blowups resulted when people were overconfident about particular bets.
  • Decisions are a matter of weighing probabilities.  Moreover, you also have to consider payoffs.  Probabilities alone are not enough if the payoffs are skewed.  A high probability of winning does not guarantee that it’s a positive expected value bet if the potential loss is far greater than the potential gain.
  • Despite uncertainty, we must act.  Often in investing and in life, we have to make decisions based in imperfect or incomplete information.
  • Judge decisions not only on results, but also on how they were made.  If you’re making decisions under uncertainty—probabilistic decisions—you have to focus on developing the best process you can.  Also, you must accept that some good decisions will have bad outcomes, while some bad decisions will have good outcomes.

Rubin again:

It’s not that results don’t matter.  They do.  But judging solely on results is a serious deterrent to taking risks that may be necessary to making the right decision.  Simply put, the way decisions are evaluated affects the way decisions are made.

 

RISKY BUSINESS

(Photo by Shawn Hempel)

Mauboussin:

So how should we think about risk and uncertainty?  A logical starting place is Frank Knight’s distinction: Risk has an unknown outcome, but we know what the underlying outcome distribution looks like.  Uncertainty also implies an unknown outcome, but we don’t know what the underlying distribution looks like.  So games of chance like roulette or blackjack are risky, while the outcome of a war is uncertain.  Knight said that objective probability is the basis for risk, while subjective probability underlies uncertainty.

Mauboussin highlights three ways to get a probability, as suggested by Gerd Gigerenzer in Calculated Risks:

  • Degrees of belief.  Degrees of belief are subjective probabilities and are the most liberal means to translate uncertainty into a probability.  The point here is that investors can translate even onetime events into probabilities provided they satisfy the laws of probability—the exhaustive and exclusive set of alternatives adds up to one.  Also, investors can frequently update probabilities based on degrees of belief when new, relevant information becomes available.
  • Propensities.  Propensity-based probabilities reflect the properties of the object or system.  For example, if a die is symmetrical and balanced, then you have a one-in-six probability of rolling any particular side… This method of probability assessment does not always consider all the factors that may shape an outcome (such as human error).
  • Frequencies.  Here the probability is based on a large number of observations in an appropriate reference class.  Without an appropriate reference class, there can be no frequency-based probability assessment.  So frequency users would not care what someone believes the outcome of a die roll will be, nor would they care about the design of the die.  They would focus only on the yield of repeated die rolls.

When investing in a stock, we try to figure out the expected value by delineating possible scenarios along with a probability for each scenario.  This is the essence of what top value investors like Warren Buffett strive to do.

 

ARE YOU AN EXPERT?

In 1996, Lars Edenbrandt, a Lund University researcher, set up a contest between an expert cardiologist and a computer.  The task was to sort a large number of electrocardiograms (EKGs) into two piles—heart attack and no heart attack.

(Image by Johannes Gerhardus Swanepoel)

The human expert was Dr. Hans Ohlin, a leading Swedish cardiologist who regularly evaluated as many as 10,000 EKGs per year.  Edenbrandt, an artificial intelligence expert, trained his computer by feeding it thousands of EKGs.  Mauboussin describes:

Edenbrandt chose a sample of over 10,000 EKGs, exactly half of which showed confirmed heart attacks, and gave them to machine and man.  Ohlin took his time evaluating the charts, spending a week carefully separating the stack into heart-attack and no-heart-attack piles.  The battle was reminiscent of Garry Kasparov versus Deep Blue, and Ohlin was fully aware of the stakes.

As Edenbrandt tallied the results, a clear-cut winner emerged: the computer correctly identified the heart attacks in 66 percent of the cases, Ohlin only in 55 percent.  The computer proved 20 percent more accurate than a leading cardiologist in a routine task that can mean the difference between life and death.

Mauboussin presents a table illustrating that expert performance depends on the problem type:

Domain Description (Column) Expert Performance Expert Agreement Examples
Rules based: Limited Degrees of Freedom Worse than computers High (70-90%)
  • Credit scoring
  • Simple medical diagnosis
Rules based: High Degrees of Freedom Generally better than computers Moderate (50-60%)
  • Chess
  • Go
Probabilistic: Limited Degrees of Freedom Equal to or worse than collectives Moderate/ Low (30-40%)
  • Admissions officers
  • Poker
Probabilistic: High Degrees of Freedom Collectives outperform experts Low (<20%)
  • Stock market
  • Economy

For rules-based systems with limited degrees of freedom, computers consistently outperform individual humans; humans perform well, but computers are better and often cheaper, says Mauboussin.  Humans underperform computers because humans are influenced by suggestion, recent experience, and how information is framed.  Also, humans fail to weigh variables well.  Thus, while experts tend to agree in this domain, computers outperform experts, as illustrated by the EKG-reading example.

In the next domain—rules-based systems with high degrees of freedom—experts tend to add the most value.  However, as computing power continues to increase, eventually computers will outperform experts even here, as illustrated by Chess and Go.  Eventually, games like Chess and Go are “solvable.”  Once the computer can check every single possible move within a reasonable amount of time—which is inevitable as long as computing power continues to increase—no human will be able to match such a computer.

In probabilistic domains with limited degrees of freedom, experts are equal to or worse than collectives.  Overall, the value of experts declines compared to rules-based domains.

(Image by Marrishuanna)

In probabilistic domains with high degrees of freedom, experts do worse than collectives.  For instance, stock market prices aggregate many guesses from individual investors.  Stock market prices typically are more accurate than experts.

 

THE HOT HAND IN INVESTING

Sports fans and athletes believe in the hot hand in basketball.  A player on a streak is thought to be “hot,” or more likely to make his or her shots.  However, statistical analysis of streaks shows that the hot hand does not exist.

(Illustration by lbreakstock)

Long success streaks happen to the most skillful players in basketball, baseball, and other sports.  To illustrate this, Mauboussin asks us to consider two basketball players, Sally Swish and Allen Airball.  Sally makes 60 percent of her shot attempts, while Allen only makes 30 percent of his shot attempts.

What are the probabilities that Sally and Allen make five shots in a row?  For Sally, the likelihood is (0.6)^5, or 7.8 percent.  Sally will hit five in a row about every thirteen sequences.  For Allen, the likelihood is (0.3)^5, or 0.24 percent.  Allen will hit five straight once every 412 sequences.  Sally will have far more streaks than Allen.

In sum, long streaks in sports or in money management indicate extraordinary luck imposed on great skill.

 

TIME IS ON MY SIDE

The longer you’re willing to hold a stock, the more attractive the investment.  For the average stock, the chance that it will be higher is (almost) 100 percent for one decade, 72 percent for one year, 56 percent for one month, and 51 percent for one day.

(Illustration by Marek)

The problem is loss aversion.  We feel the pain of a loss 2 to 2.5 times more than the pleasure of an equivalent gain.  If we check a stock price daily, there’s nearly a 50 percent chance of seeing a loss.  So checking stock prices daily is a losing proposition.  By contrast, if we only check the price once a year or once every few years, then investing in a stock is much more attractive.

A fund with a high turnover ratio is much more short-term oriented than a fund with a low turnover ratio.  Unfortunately, most institutional investors have a much shorter time horizon than what is needed for the typical good strategy to pay off.  If portfolio managers lag over shorter periods of time, they may lose their jobs even if their strategy works quite well over the long term.

 

THE LOW DOWN ON THE TOP BRASS

(Illustration by Travelling-light)

It’s difficult to judge leadership, but Mauboussin identifies four things worth considering:

  • Learning
  • Teaching
  • Self-awareness
  • Capital allocation

Mauboussin asserts:

A consistent thirst to learn marks a great leader.  On one level, this is about intellectual curiosity—a constant desire to build mental models that can help in decision making.  A quality manager can absorb and weigh contradictory ideas and information as well as think probabilistically…

Another critical facet of learning is a true desire to understand what’s going on in the organization and to confront the facts with brutal honesty.  The only way to understand what’s going on is to get out there, visit employees and customers, and ask questions and listen to responses.  In almost all organizations, there is much more information at the edge of the network—the employees in the trenches dealing with the day-to-day issues—than in the middle of the network, where the CEO sits.  CEOs who surround themselves with managers seeking to please, rather than prod, are unlikely to make great decisions.

A final dimension of learning is creating an environment where everyone in the organization feels they can voice their thoughts and opinions without the risk of being rebuffed, ignored, or humiliated.  The idea here is not that management should entertain all half-baked ideas but rather that management should encourage and reward intellectual risk taking.

Teaching involves communicating a clear vision to the organization.  Mauboussin points out that teaching comes most naturally to those leaders who are passionate.  Passion is a key driver of success.

Self-awareness implies a balance between confidence and humility.  We all have strengths and weaknesses.  Self-aware leaders know their weaknesses and find colleagues who are strong in those areas.

Finally, capital allocation is a vital leadership skill.  Regrettably, many consultants and investment bankers give poor advice on this topic.  Most acquisitions destroy value for the acquirer, regardless of whether they are guided by professional advice.

Mauboussin quotes Warren Buffett:

The heads of many companies are not skilled in capital allocation.  Their inadequacy is not surprising.  Most bosses rise to the top because they have excelled in an area such as marketing, production, engineering, administration or, sometimes, institutional politics.

Once they become CEOs, they face new responsibilities.  They now must make capital allocation decisions, a critical job that they may have never tackled and that is not easily mastered.  To stretch the point, it’s as if the final step for a highly talented musician was not to perform at Carnegie Hall but, instead, to be named Chairman of the Federal Reserve.

The lack of skill that many CEOs have at capital allocation is no small matter: After ten years on the job, a CEO whose company annually retains earnings equal to 10% of net worth will have been responsible for the deployment of more than 60% of all the capital at work in the business.  CEOs who recognize their lack of capital-allocation skills (which not all do) will often try to compensate by turning to their staffs, management consultants, or investment bankers.  Charlie and I have frequently observed the consequences of such “help.”  On balance, we feel it is more likely to accentuate the capital-allocation problem than to solve it.

In the end, plenty of unintelligent capital allocation takes place in corporate America.  (That’s why you hear so much about “restructing.”)

 

SIX PSYCHOLOGICAL TENDENCIES

(Image by Andreykuzmin)

The psychologist Robert Cialdini, in his book Influence: The Psychology of Persuasion, mentions six psychological tendencies that cause people to comply with requests:

  • Reciprocation.  There is no human society where people do not feel the obligation to reciprocate favors or gifts.  That’s why charitable organizations send free address labels and why real estate companies offer free house appraisals.  Sam Walton was smart to forbid all of his employees from accepting gifts from suppliers, etc.
  • Commitment and consistency.  Once we’ve made a decision, and especially if we’ve publicly committed to that decision, we’re highly unlikely to change.  Consistency allows us to stop thinking and also to avoid further action.
  • Social validation.  One of the chief ways we make decisions is by observing the decisions of others.  In an experiment by Solomon Asch, eight people in a room are shown three lines of clearly unequal lengths.  Then they are shown a fourth line that has the same length as one of the three lines.  They are asked to match the fourth line to the one with equal length.  The catch is that only one of the eight people in the room is the actual subject of the experiment.  The other seven people are shills who have been instructed to choose an obviously incorrect answer.  About 33 percent of the time, the subject of the experiment ignores the obviously right answer and goes along with the group instead.
  • Liking.  We all prefer to say yes to people we like—people who are similar to us, who compliment us, who cooperate with us, and who we find attractive.
  • Authority.  Stanley Milgram wanted to understand why many seemingly decent people—including believing Lutherans and Catholics—went along with the great evils perpetrated by the Nazis.  Milgram did a famous experiment.  A person in a white lab coat stands behind the subject of the experiment.  The subject is asked to give increasingly severe electric shocks to a “learner” in another room whenever the learner gives an incorrect answer to a question.  (Unknown to the subject, the learner in the other room is an actor and the electric shocks are not really given.)  Roughly 60 percent of the time, the subject of the experiment gives a fatal shock of 450 volts to the learner.  This is a terrifying result.  See: https://en.wikipedia.org/wiki/Milgram_experiment
  • Scarcity.  Items or data that are scarce or perceived to be scarce automatically are viewed as more attractive.  That’s why companies frequently offer services or products for a limited time only.

These innate psychological tendencies are especially powerful when they operate in combination.  Charlie Munger calls this lollapalooza effects.

Mauboussin writes that investors are often influenced by commitment and consistency, social validation, and scarcity.

Psychologists discovered that after bettors at a racetrack put down their money, they are more confident in the prospects of their horses winning than immediately before they placed their bets.  After making a decision, we feel both internal and external pressure to remain consistent to that view even if subsequent evidence questions the validity of the initial decision.

So an investor who has taken a position in a particular stock, recommended it publicly, or encouraged colleagues to participate, will feel the need to stick with the call.  Related to this tendency is the confirmation trap: postdecision openness to confirming data coupled with disavowal or denial of disconfirming data.  One useful technique to mitigate consistency is to think about the world in ranges of values with associated probabilities instead of as a series of single points.  Acknowledging multiple scenarios provides psychological shelter to change views when appropriate.

There is a large body of work about the role of social validation in investing.  Investing is an inherently social activity, and investors periodically act in concert…

Finally, scarcity has an important role in investing (and certainly plays a large role in the minds of corporate executives).  Investors in particular seek informational scarcity.  The challenge is to distinguish between what is truly scarce information and what is not.  One means to do this is to reverse-engineer market expectations—in other words, figure out what the market already thinks.

 

EMOTION AND INTUITION IN DECISION MAKING

(Photo by Marek Uliasz)

Humans need to be able to experience emotions in order to make good decisions.  Mauboussin writes about an experiment conducted by Antonio Damasio:

…In one experiment, he harnessed subjects to a skin-conductance-response machine and asked them to flip over cards from one of four decks; two of the decks generated gains (in play money) and the other two were losers.  As the subjects turned cards, Damasio asked them what they thought was going on.  After about ten turns, the subjects started showing physical reactions when they reached for a losing deck.  About fifty cards into the experiment, the subjects articulated a hunch that two of the four decks were riskier.  And it took another thirty cards for the subjects to explain why their hunch was right.

This experiment provided two remarkable decision-making lessons.  First, the unconscious knew what was going on before the conscious did.  Second, even the subjects who never articulated what was going on had unconscious physical reactions that guided their decisions.

 

BEWARE OF BEHAVIORAL FINANCE

Individual agents can behave irrationally but the market can still be rational.

…Collective behavior addresses the potentially irrational actions of groups.  Individual behavior dwells on the fact that we all consistently fall into psychological traps, including overconfidence, anchoring and adjustment, improper framing, irrational commitment escalation, and the confirmation trap.

Here’s my main point: markets can still be rational when investors are individually irrational.  Sufficient investor diversity is the essential feature in efficient price formation.  Provided the decision rules of investors are diverse—even if they are suboptimal—errors tend to cancel out and markets arrive at appropriate prices.  Similarly, if these decision rules lose diversity, markets become fragile and susceptible to inefficiency.

Mauboussin continues:

In case after case, the collective outperforms the individual.  A full ecology of investors is generally sufficient to assure that there is no systematic way to beat the market.  Diversity is the default assumption, and diversity breakdowns are the notable (and potentially profitable) exceptions.

(Illustration by Trueffelpix)

Mauboussin writes about an interesting example of how the collective can outperform individuals (including experts).

On January 17, 1966, a B-52 bomber and a refueling plane collided in midair while crossing the Spanish coastline.  The bomber was carrying four nuclear bombs.  Three were immediately recovered.  But the fourth was lost and its recovery became a national security priority.

Assistant Security of Defense Jack Howard called a young naval officer, John Craven, to find the bomb.  Craven assembled a diverse group of experts and asked them to place bets on where the bomb was.  Shortly thereafter, using the probabilities that resulted from all the bets, the bomb was located.  The collective intelligence in this example was superior to the intelligence of any individual expert.

 

IMPORTANCE OF A DECISION JOURNAL

In investing and in general, it’s wise to keep a journal of our decisions and the reasoning behind them.

(Photo by Leerobin)

We all suffer from hindsight bias.  We are unable to recall what we actually thought before making a decision or judgment.

  • If we decide to do something and it works out, we tend to underestimate the uncertainty that was present when we actually made the decision.  “I knew I made the right decision.”
  • If we decide to do something and it doesn’t work, we tend to overestimate the uncertainty that was present when we actually made the decision.  “I suspected that it wouldn’t work.”
  • If we judge that event X will happen, and then it does, we underestimate the uncertainty that was present when we made the judgment.  “I knew that would happen.”
  • If we judge that event X will happen, and it doesn’t, we overestimate the uncertainty that was present when we made the judgment.  “I was fully aware that it was unlikely.”

See: https://en.wikipedia.org/wiki/Hindsight_bias

As Mauboussin notes, keeping a decision journal gives us a valuable source of objective feedback.  Otherwise, we won’t recall with any accuracy the uncertainty we faced or the reasoning we used.

 

RIGHT FROM THE GUT

Robert Olsen has singled out five conditions that are present in the context of naturalistic decision making.

  • Ill-structured and complex problems.  No obvious best procedure exists to solve a problem.
  • Information is incomplete, ambiguous, and changing.  Because stock picking relates to future financial performance, there is no way to consider all information.
  • Ill-defined, shifting, and competing goals.  Although long-term goals may be clearer, goals can change over shorter horizons.
  • Stress because of time constraints, high stakes, or both.  Stress is clearly a feature of investing.
  • Decisions may involve multiple participants.  

Mauboussin describes three key characteristics of naturalistic decision makers.  First, they rely heavily on mental imagery and simulation in order to assess a situation and possible alternatives.  Second, they excel at pattern matching.  (For instance, chess masters can glance at a board and quickly recognize a pattern.)

(Photo by lbreakstock)

Third, naturalistic decision makers reason through analogy.  They can see how seemingly different situations are in fact similar.

 

WEIGHTED WATCHER

Mauboussin describes how we develop a “degree of belief” in a specific hypothesis:

Our degree of belief in a particular hypothesis typically integrates two kinds of evidence: the strength, or extremeness, of the evidence and the weight, or predictive validity.  For instance, say you want to test the hypothesis that a coin in biased in favor of heads.  The proportion of heads in the sample reflects the strength, while the sample size determines the weight.

Probability theory describes rules for how to combine strength and weight correctly.  But substantial experimental data show that people do not follow the theory.  Specifically, the strength of evidence seems to dominate the weight of evidence in people’s minds.

This bias leads to a distinctive pattern of over- and underconfidence.  When the strength of evidence is high and the weight is low—which accurately describes the outcome of many Wall Street-sponsored surveys—people tend to be overconfident.  In contrast, when the strength is low and the evidence is high, people tend to be underconfident.

(Photo by Michele Lombardo)

Does survey-based research lead to superior stock selection?  Mauboussin responds that the answer is ambiguous.  First, the market adjusts to new information rapidly.  It’s difficult to gain an informational edge, especially when it comes to what is happening now or what will happen in the near future.  In contrast, it’s possible to gain an informational edge if you focus on the longer term.  That’s because many investors don’t focus on the longer term.

The second issue is that understanding the fundamentals about a company or industry is very different from understanding the expectations built into a stock price.  The question is not just whether the information is new to you, but whether the information is also new to the market.  In the vast majority of cases, the information is already reflected in the current stock price.

Mauboussin sums it up:

Seeking new information is a worthy goal for an investor.  My fear is that much of what passes as incremental information adds little or no value, because investors don’t properly weight new information, rely on unsound samples, and fail to recognize what the market already knows.  In contrast, I find that thoughtful discussions about a firm’s or an industry’s medium- to long-term competitive outlook extremely rare.

 

WHY INNOVATION IS INEVITABLE

(Image: Innovation concept, by Daniil Peshkov)

Mauboussin quotes Andrew Hargadon’s How Breakthroughs Happen:

All innovations represent some break from the past—the lightbulb replaced the gas lamp, the automobile replaced the horse and cart, the steamship replaced the sailing ship.  By the same token, however, all innovations are built from pieces of the past—Edison’s system drew its organizing principles from the gas industry, the early automobiles were built by cart makers, and the first steam ships added steam engines to existing sailing ships.

Mauboussin adds:

Investors need to appreciate the innovation process for a couple of reasons.  First, our overall level of material well-being relies heavily on innovation.  Second, innovation lies at the root of creative destruction—the process by which new technologies and businesses supersede others.  More rapid innovation means more rapid success and failure for companies.

Mauboussin draws attention to three interrelated factors that continue to drive innovation at an accelerating rate:

  • Scientific advances
  • Information storage capacity
  • Gains in computing power

 

ACCELERATING RATE OF INDUSTRY CHANGE

(Photo: Drosophila Melanogaster, by Tomatito26)

Mauboussin mentions the common fruit fly, Drosophila melanogaster, which geneticists and other scientists like to study because its life cycle is only two weeks.

Why should businesspeople care about Drosophila?  A sound body of evidence now suggests that the average speed of evolution is accelerating in the business world.  Just as scientists have learned a great deal about evolutionary change from fruit flies, investors can benefit from understanding the sources and implications of accelerated business evolution.

The most direct consequence of more rapid business evolution is that the time an average company can sustain a competitive advantage—that is, generate an economic return in excess of its cost of capital—is shorter than it was in the past.  This trend has potentially important implications for investors in areas such as valuation, portfolio turnover, and diversification.

Mauboussin refers to research by Robert Wiggins and Timothy Ruefli on the sustainability of economic returns.  They put forth four hypotheses.  The first three were supported by the data, while the fourth one was not:

  • Periods of persistent superior economic performance are decreasing in duration over time.
  • Hypercompetition is not limited to high-technology industries but will occur through most industries.
  • Over time, firms increasingly seek to sustain competitive advantage by concatenating a series of short-term competitive advantages.
  • Industry concentration, large market share, or both are negatively correlated with chance of loss of persistent superior economic performance in an industry.

Mauboussin points out that faster product and process life cycles means that historical multiples are less useful for comparison.  Also, the terminal valuation in discounted cash-flow models in many cases has to be adjusted to reflect shorter periods of sustainable competitive advantage.

(Image by Marek Uliasz)

Furthermore, while portfolio turnover on average is too high, portfolio turnover could be increased for those investors who have historically had a portfolio turnover of 20 percent (implying a holding period of 5 years).  Similarly, shorter periods of competitive advantage imply that some portfolios should be more diversified.  Lastly, faster business evolution means that investors must spend more time understanding the dynamics of organizational change.

 

HOW TO BALANCE THE LONG TERM WITH THE SHORT TERM

(Photo by Michael Maggs, via Wikimedia Commons)

Mauboussin notes the lessons emphasized by chess master Bruce Pandolfini:

  • Don’t look too far ahead.  Most people believe that great players strategize by thinking far into the future, by thinking 10 or 15 moves ahead.  That’s just not true.  Chess players look only as far into the future as they need to, and that usually means looking just a few moves ahead.  Thinking too far ahead is a waste of time: The information is uncertain.
  • Develop options and continuously revise them based on the changing conditions: Great players consider their next move without playing it.  You should never play the first good move that comes into your head.  Put that move on your list, and then ask yourself if there’s an even better move.  If you see a good idea, look for a better one—that’s my motto.  Good thinking is a matter of making comparisons.
  • Know your competition: Being good chess also requires being good at reading people.  Few people think of chess as an intimate, personal game.  But that’s what it is.  Players learn a lot about their opponents, and exceptional chess players learn to interpret every gesture that their opponents make.
  • Seek small advantages: You play for seemingly insignificant advantages—advantages that your opponent doesn’t notice or that he dismisses, thinking, “Big deal, you can have that.”  It could be slightly better development, or a slightly safer king’s position.  Slightly, slightly, slightly.  None of those “slightlys” mean anything on their own, but add up seven or eight of them, and you have control.

Mauboussin argues that companies should adopt simple, flexible long-term decision rules.  This is the “strategy as simple rules” approach, which helps us from getting caught in the short term versus long term debate.

Moreover, simple decision rules help us to be consistent.  Otherwise we will often reach different conclusions from the same data based on moods, suggestion, recency bias, availability bias, framing effects, etc.

 

FITNESS LANDSCAPES AND COMPETITIVE ADVANTAGE

(Image: Fitness Landscape, by Randy Olsen, via Wikimedia Commons)

Mauboussin:

What does a fitness landscape look like?  Envision a large grid, with each point representing a different strategy that a species (or a company) can pursue.  Further imagine that the height of each point depicts fitness.  Peaks represent high fitness, and valleys represent low fitness.  From a company’s perspective, fitness equals value-creation potential.  Each company operates in a landscape full of high-return peaks and value-destructive valleys.  The topology of the landscape depends on the industry characteristics.

As Darwin noted, improving fitness is not about strength or smarts, but rather about becoming more and more suited to your environment—in a word, adaptability.  Better fitness requires generating options and “choosing” the “best” ones.  In nature, recombination and mutation generate species diversity, and natural selection assures that the most suitable options survive.  For companies, adaptability is about formulating and executing value-creating strategies with a goal of generating the highest possible long-term returns.

Since a fitness landscape can have lots of peaks and valleys, even if a species reaches a peak (a local optimum), it may not be at the highest peak (a global optimum).  To get a higher altitude, a species may have reduce its fitness in the near term to improve its fitness in the long term.  We can say the same about companies…

Mauboussin remarks that there are three types of fitness landscape:

  • Stable.  These are industries where the fitness landscape is reasonably stable.  In many cases, the landscape is relatively flat, and companies generate excess economic returns only when cyclical forces are favorable.  Examples include electric and telephone utilities, commodity producers (energy, paper, metals), capital goods, consumer nondurables, and real estate investment trusts.  Companies within these sectors primarily improve their fitness at the expense of their competitors.  These are businesses that tend to have structural predictability (i.e., you’ll know what they look like in the future) at the expense of limited opportunities for growth and new businesses.
  • Coarse.  The fitness landscape is in flux for these industries, but the changes are not so rapid as to lack predictability.  The landscape here is rougher.  Some companies deliver much better economic performance than do others.  Financial services, retail, health care, and more established parts of technology are illustrations.  These industries run a clear risk of being unseated (losing fitness) by a disruptive technology.
  • Roiling.  This group contains businesses that are very dynamic, with evolving business models, substantial uncertainty, and ever-changing product offerings.  The peaks and valleys are constantly changing, ever spastic.  Included in this type are many software companies, the genomics industry, fashion-related sectors, and most start-ups.  Economic returns in this group can be (or can promise to be) significant but are generally fleeting.

Mauboussin indicates that innovation, deregulation, and globalization are probably causing the global fitness landscape to become even more contorted.

Companies can make short, incremental jumps towards a local maximum.  Or they can make long, discontinuous jumps that may lead to a higher peak or a lower valley.  Long jumps include investing in new potential products or making meaningful acquisitions in unrelated fields.  The proper balance between short jumps and long jumps depends on a company’s fitness landscape.

Mauboussin adds that the financial tool for valuing a given business depends on the fitness landscape that the business is in.  A business in a stable landscape can be valued using discounted cash-flow (DCF).  A business in a course landscape can be valued using DCF plus strategic options.  A business in a roiling landscape can be valued using strategic options.

 

THE FOLLY OF USING AVERAGE P/E’S

Bradford Cornell:

For past averages to be meaningful, the data being averaged have to be drawn from the same population.  If this is not the case—if the data come from populations that are different—the data are said to be nonstationary.  When data are nonstationary, projecting past averages typically produces nonsensical results.

Nonstationarity is a key concept in time-series analysis, such as the study of past data in business and finance.  If the underlying population changes, then the data are nonstationary and you can’t compare past averages to today’s population.

(Image: Time Series, by Mike Toews via Wikimedia Commons)

Mauboussin gives three reasons why past P/E data are nonstationary:

  • Inflation and taxes
  • Changes in the composition of the economy
  • Shifts in the equity-risk premium

Higher taxes mean lower multiples, all else equal.  And higher inflation also means lower multiples.  Similarly, low taxes and low inflation both cause P/E ratios to be higher.

As I write this in January 2018, inflation has been low for many years.  As well, interest rates have been low for many years.  If interest rates stay low enough for long enough, stocks can have an average P/E of 30 or even 50, as Warren Buffett has commented.

The more companies rely on intangible capital rather than tangible capital, the higher the cash-flow-to-net-income ratio.  Overall, the economy is relying increasingly on intangible capital.  Higher cash-flow-to-net-income ratios, and higher returns on capital, mean higher P/E ratios.

 

MEAN REVERSION AND TURNAROUNDS

Growth alone does not create value.  Growth creates value only if the return on invested capital exceeds the cost of capital.  Growth actually destroys value if the return on invested capital is less than the cost of capital.

(Illustration by Teguh Jati Prasetyo)

Over time, a company’s return on capital moves towards its cost of capital.  High returns bring competition and new capital, which drives the return on capital toward the cost of capital.  Similarly, capital exits low-return industries, which lifts the return on capital toward the cost of capital.

Mauboussin reminds us that a good business is not necessarily a good investment, just as a bad business is not necessarily a bad investment.  What matters is the expectations embedded in the current price.  If expectations are overly low for a bad business, it can represent a good investment.  If expectations are too high for a good business, it may be a poor investment.

On the other hand, some cheap stocks deserve to be cheap and aren’t good investments.  And some expensive-looking stocks trading at high multiples may still be good investments if high growth and high return on capital can persist long enough into the future.

 

CONSIDERING COOPERATION AND COMPETITION THROUGH GAME THEORY

(Illustration: Concept of Prisoner’s Dilemma, by CXJ Jensen via Wikimedia Commons)

Mauboussin quotes Robert Axelrod’s The Complexity of Cooperation:

What the Prisoner’s Dilemma captures so well is the tension between the advantages of selfishness in the short run versus the need to elicit cooperation from the other player to be successful over the longer run.  The very simplicity of the Prisoner’s Dilemma is highly valuable in helping us to discover and appreciate the deep consequences of the fundamental processes involved in dealing with this tension.

The Prisoner’s Dilemma shows that the rational response for an individual company  is not necessarily optimal for the industry as a whole.

If the Prisoner’s Dilemma game is going to be repeated many times, then the best strategy is tit-for-tat.  Whatever your competitor’s latest move was, copy that for your next move.  So if your competitor deviates one time and then cooperates, you deviate one time and then cooperate.  Tit-for-tat is both the simplest strategy and also the most effective.

When it comes to market pricing and capacity decisions, competitive markets need not be zero sum.  A tit-for-tat strategy is often optimal, and by definition it includes a policing component if your competitor deviates.

 

THE WISDOM AND WHIM OF THE COLLECTIVE

Mauboussin quotes Robert D. Hanson’s Decision Markets:

[Decision markets] pool the information that is known to diverse individuals into a common resource, and have many advantages over standard institutions for information aggregation, such as news media, peer review, trials, and opinion polls.  Speculative markets are decentralized and relatively egalitarian, and can offer direct, concise, timely, and precise estimates in answer to questions we pose.

Mauboussin then writes about bees and ants, ending with this comment:

What makes the behavior of social insects like bees and ants so amazing is that there is no central authority, no one directing traffic.  Yet the aggregation of simple individuals generates complex, adaptive, and robust results.  Colonies forage efficiently, have life cycles, and change behavior as circumstances warrant.  These decentralized individuals collectively solve very hard problems, and they do it in a way that is very counterintuitive to the human predilection to command-and-control solutions.

(Illustration: Swarm Intelligence, by Farbentek)

Mauboussin again:

Why do decision markets work so well?  First, individuals in these markets think they have some edge, so they self-select to participate.  Second, traders have an incentive to be right—they can take money from less insightful traders.  Third, these markets provide continuous, real-time forecasts—a valuable form of feedback.  The result is that decision markets aggregate information across traders, allowing them to solve hard problems more effectively than any individual can.

 

VOX POPULI

(Painting: Sir Francis Galton, by Charles Wellington Furse, via Wikimedia Commons)

Mauboussin tells of an experiment by Francis Galton:

Victorian polymath Francis Galton was one of the first to thoroughly document this group-aggregation ability.  In a 1907 Nature article, “Vox Populi,” Galton describes a contest to guess the weight of an ox at the West of England Fat Stock and Poultry Exhibition in Plymouth.  He collected 787 participants who each paid a sixpenny fee to participate.  (A small cost to deter practical joking.)  According to Galton, some of the competitors were butchers and farmers, likely expert at guessing the weight.  He surmised that many others, though, were guided by “such information as they might pick up” or “by their own fancies.”

Galton calculated the median estimate—the vox populi—as well as the mean.  He found that the median guess was within 0.8 percent of the correct weight, and that the mean of the guesses was within 0.01 percent.  To give a sense of how the answer emerged, Galton showed the full distribution of answers.  Simply stated, the errors cancel out and the result is distilled information.

Subsequently, we have seen the vox populi results replicated over and over.  Examples include solving a complicated maze, guessing the number of jellybeans in a jar, and finding missing bombs.  In each case, the necessary conditions for information aggregation to work include an aggregation mechanism, an incentive to answer correctly, and group heterogeneity.

 

COMPLEX ADAPTIVE SYSTEMS

(Illustration by Acadac, via Wikimedia Commons)

Complex adaptive systems exhibit a number of essential properties and mechanisms, writes Mauboussin:

  • Aggregation.  Aggregation is the emergence of complex, large-scale behavior from the collective interactions of many less-complex agents.
  • Adaptive decision rules.  Agents within a complex adaptive system take information from the environment, combine it with their own interaction with the environment, and derive decision rules.  In turn, various decision rules compete with one another based on their fitness, with the most effective rules surviving.
  • Nonlinearity.  In a linear model, the whole equals the sum of the parts.  In nonlinear systems, the aggregate behavior is more complicated than would be predicted by totaling the parts.
  • Feedback loops.  A feedback system is one in which the output of one iteration becomes the input of the next iteration.  Feedback loops can amplify or dampen an effect.

Governments, many corporations, and capital markets are all examples of complex adaptive systems.

Humans have a strong drive to invent a cause for every effect.  This has been biologically advantageous for the vast majority of human history.  In the past, if we heard a rustling in the grass, we immediately sought safety.  There was always some cause for the noise.  It virtually never made sense to wait around to see if it was a predator or not.

However, in complex adaptive systems like the stock market, typically there is no simple cause and effect relationship that explains what happens.

For many big moves in the stock market, there is no identifiable cause.  But people have such a strong need identify a cause that they make up causes.  The press delivers to people what they want: explanations for big moves in the stock market.  Usually these explanations are simply made up.  They’re false.

 

THE FUTURE OF CONSILIENCE IN INVESTING

(Painting: Galileo Galilei, by Justus Sustermans, via Wikimedia Commons)

Mauboussin, following Charlie Munger, argues that cross-disciplinary research is likely to produce the deepest insights into the workings of companies and markets.  Here are some examples:

  • Decision making and neuroscience.  Prospect theory—invented by Daniel Kahneman and Amos Tversky—describes how people suffer from cognitive biases when they make decisions under uncertainty.  Prospect theory is extremely well-supported by countless experiments.  But prospect theory still doesn’t explain why people make the decisions they do.  Neuroscience will help with this.
  • Statistical properties of markets—from description to prediction?  Stock price changes are not normally distributed—along a bell-shaped curve—but rather follow a power law.  The statistical distribution has fat tails, which means there are more extreme moves than would occur under a normal distribution.  Once again, a more accurate description is progress.  But the next step involves a greater ability to explain and predict the phenomena in question.
  • Agent-based models.  Individual differences are important in market outcomes.  Feedback mechanisms are also central.
  • Network theory and information flows.  Network research involves epidemiology, psychology, sociology, diffusion theory, and competitive strategy.  Much progress can be made.
  • Growth and size distribution.  There are very few large firms and many small ones.  And all large firms experience significantly slower growth once they reach a certain size.
  • Flight simulator for the mind?  One of the biggest challenges in investing is that long-term investors don’t get nearly enough feedback.  Statistically meaningful feedback for investors typically takes decades.

 

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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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.

You’re deluding yourself

(Image: Zen Buddha Silence, by Marilyn Barbone)

November 26, 2017

You’re deluding yourself.  I’m deluding myself.  Our brains just do this automatically, all the time.  We invent simple stories based on cause and effect.  Often this is harmless.  But sometimes it’s important to recognize that reality is far more unpredictable than we’d like.

We’re not wired to understand probabilities.  As Daniel Kahneman and Amos Tversky have demonstrated, even many professional statisticians are not good “intuitive statisticians.”  They’re usually only good if they slow down and work through the problem at hand step-by-step.  Otherwise, they too tend to create overly simplistic, overly deterministic stories.

(Photo by Wittayayut Seethong)

To develop better mental habits, a good place to start is by recognizing delusions and biases, which are widespread in business, politics, and economics.  To that end, here are four of the best books:

  • Thinking, Fast and Slow (Farrar, Straus and Giroux, 2011), by Daniel Kahneman
  • Poor Charlie’s Almanack (Walsworth, 3rd edition, 2005), by Charles T. Munger
  • The Halo Effect…and Eight Other Business Delusions That Deceive Managers (Free Press, 2007), by Phil Rosenzweig
  • Expert Political Judgment: How Good Is It? How Can We Know? (Princeton University Press, 2006), by Philip Tetlock

Tetlock’s work is particularly important.  He tracked over 27,000 predictions made in real time by 284 experts from 1984 to 2003.  Tetlock found that the expert predictions—on the whole—were no better than chance.  Many of these experts have deep historical knowledge of politics or economics, which can give us important insights and is often a precursor to scientific knowledge.  But it’s not yet science—the ability to make predictions.

Kahneman and Munger both show how our intuition uses mental shortcuts (heuristics) to jump to conclusions.  Often these conclusions are fine.  But not if probabilistic reasoning is needed to reach a good decision.

This blog post focuses on Rosenzweig’s book, which examines delusions in business, with particular emphasis on the Halo Effect.

Outline for this blog post:

  • The Halo Effect
  • Illusions and Delusions
  • How Little We Know
  • The Story of Cisco
  • Up and Down with ABB
  • Halos All Around Us
  • Research to the Rescue?
  • Searching for Stars, Finding Halos
  • The Mother of All Business Questions, Take Two
  • Managing Without Coconut Headsets

 

THE HALO EFFECT

Rosenzweig quotes John Kay of the Financial Times:

The power of the halo effect means that when things are going well praise spills over to every aspect of performance, but also that when the wheel of fortune spins, the reappraisal is equally extensive.  Our search for excessively simple explanations, our desire to find great men and excellent companies, gets in the way of the complex truth.

(Image by Ileezhun)

Rosenzweig explains the essence of the Halo Effect:

If you select companies on the basis of outcomes—whether success or failure—and then gather data that are biased by those outcomes, you’ll never know what drives performance.  You’ll only know how high performers or low performers are described.

Rosenzweig describes his book as “a guide for the reflective manager,” a way to avoid delusions and to think critically.  It’s quite natural for us to construct simple stories about why things happen.  But many events—including business success and failure—don’t happen in a straightforward way.  There’s a large measure of uncertainty (chance) involved.

Rosenzweig adds:

Of course, for those who want a book that promises to reveal the secret of success, or the formula to dominate their market, or the six steps to greatness, there are plenty to choose from.  Every year, dozens of new books claim to reveal the secrets of leading companies… Others tell you how to become an innovation powerhouse, or craft a failsafe strategy, or devise a boundaryless organization, or make the competition irrelevant.

But if anything, the world is getting more unpredictable:

In fact, for all the secrets and formulas, for all the self-proclaimed thought leadership, success in business is as elusive as ever.  It’s probably more elusive than ever, with increasingly global competition and technological change moving at faster and faster rates—which might explain why we’re tempted by promises of breakthroughs and secrets and quick fixes in the first place.  Desperate circumstances push us to look for miracle cures.

Rosenzweig explains that business managers are under great pressure to increase profits.  So they naturally look for clear solutions that they can implement right away.  Business writers and experts are happy to supply what is demanded.  However, reality is usually far more unpredictable than is commonly assumed.

 

ILLUSIONS AND DELUSIONS

Science is the ability to predict things:  if x, then y (with probability z).  (If we’re talking about physics—other than quantum mechanics—then z = 100% in the vast majority of cases.)  But the sciences that deal with human behavior still haven’t discovered enough to make many predictions.  There are specific experiments or circumstances where good predictions can be made—such as where to place specific items in a retailer to maximize sales.  And good research has uncovered numerous statistical correlations.

But on the whole, there’s still much unpredictability in business and in human behavior generally.  There’s still not much scientific knowledge.

Rosenzweig says some of the biggest recent business blockbusters contain several delusions:

For all their claims of scientific rigor, for all their lengthy descriptions of apparently solid and careful research, they operate mainly at the level of storytelling.  They offer tales of inspiration that we find comforting and satisfying, but they’re based on shaky thinking.  They’re deluded.

Rosenzweig explains that most management books seek to understand what leads to high performance.  By contrast, Rosenzweig asks why it is so difficult to understand high performance.  We suffer from many delusions.  Our intuition leads us to construct simple stories to explain things, even when those stories are false.

(Image by Edward H. Adelson, via Wikimedia commons)

Look at squares A and B just above:  Are they the same color?  Or is one square lighter than the other?

A and B are exactly the same color.  However, our visual system automatically uses contrast.  If it didn’t, then as Steven Pinker has pointed out, we would think a lump of coal in bright sunlight was white.  We would think a lump of snow inside a dark house was black.  We don’t make these mistakes because our visual system works in part by contrast.  Kathryn Schulz mentions this in her excellent book, Being Wrong (HarperCollins, 2010).

This use of contrast is a heuristic—a shortcut—used by our visual system.  This happens automatically.  And usually this heuristic helps us, as in Pinker’s examples.

The important point is that our intuition (part of our mental system) is like our visual system Our intuition also uses heuristics.

  • If we are asked a difficult question, our intuition substitutes an easier question and then answers that question.  This happens automatically and without our conscious awareness. 
  • Similarly, our intuition constructs simple stories in terms of cause and effect, even if reality is far more complex and random.  This happens automatically and without our conscious awareness.

(Image by Edward H. Adelson, via Wikimedia Commons)

This second image is the same as the previous one—except this one has two vertical grey bars.  This helps (to some extent) our eyes to see that squares A and B are exactly the same color.

Rosenzweig mentions that some rigorous research of business has been conducted.  But this research often reaches far more modest conclusions than what we seek.  As a result, it’s not popular or well-known.  For instance, there may be a 0.2 correlation between certain approaches of a CEO and business performance.  That’s a huge finding—20% of business performance is based on specific CEO behavior.

But that means 80% of business performance is due to other factors, including chance.  That’s not the type of information people in business want to hear when they’re busy and under pressure.

 

HOW LITTLE WE KNOW

In January 2004, after a disastrous holiday season, Lego—the Danish toymaker—fired its chief operating officer, Poul Ploughman.  Rosenzweig points out that when a company does well, we tend to automatically think its leaders did the right things and should be praised or promoted.  When a company does poorly, we tend to jump to the conclusion that its leaders did the wrong things and should be replaced.

But reality is far more complex.  Good leadership may represent 20-30% of the reason a company is doing well now, but luck may be an even bigger factor.  Similarly, bad leadership may be responsible for 20-30% of a company’s poor performance, whereas bad luck—unforeseeable events—may be a bigger factor.

(Photo by Marco Clarizia)

As humans, we’re driven to construct stories in which success and failure are completely explainable—without reference to luck—based on the actions of people and systems.  This satisfies our psychological need to see the world as a predictable place.

However, reality is unpredictable to an extent.  We understand far less than we think.  Luck usually plays a large role in business success and failure.

When Lego hired Ploughman, it was seen as a coup.  Ploughman helped Lego expand into electronic toys.  When the initial results of this expansion were not positive, Lego’s CEO Kjeld Kirk Kristiansen lost patience and fired Ploughman.

The business press reported that Lego had “strayed from its core.”  However, the company tried to expand because its traditional operations were not as profitable as before.  If the company’s attempted expansion had been more profitable, the business press would have reported that Lego “wisely expanded.”

(Photo of lego bricks by Benjamin D. Esham)

When it comes to business performance, there are many factors—including luck.  A company may move forward on an absolute basis, but fall behind relative to competitors.  Also, consumer tastes are unpredictable.

  • A company may attempt expansion and fail, but the decision may have been wise based on available information.  Regardless, observers are likely to say the company “unwisely strayed from its core.”
  • Or a company may try to expand and succeed, but it may have been a stupid decision based on available information.  Regardless, observers are likely to claim that the company “brilliantly expanded.”

To understand better how businesses succeed, we should try to understand what factors are involved in good decisions, even though good decisions often don’t work and bad decisions sometimes do.  We want to avoid outcome bias, where our evaluation of the quality of a decision is colored by whether the result was favorable or not.

Science is:  if x, then y (with probability z).  This is a slightly modified definition (I added “with probability z”) Rosenzweig borrowed from physicist Richard Feynman.

In some areas of business, scientists have discovered reliable statistical correlations.  For instance, this set of behaviors—a, b, and c—has a 0.10 correlation with revenues.  If you do a, b, and c—holding all else constant—then revenues will increase approximately 10%.

The difficult thing about studying business is that often you cannot run controlled experiments.  Of course, sometimes you can.  For instance, you can experiment with where to place various items in a store (or chain of stores).  You can compare results and gain good statistical information.  Also, there are promotions and advertising campaigns that you can test.  And you can track consumer behavior online.

Often, however, you cannot run controlled experiments.  As Rosenzweig observes, you can’t do 100 acquisitions, and manage half of them one way, the other half another way, and then compare the results.

There’s nothing wrong with stories, which are satisfying explanations we construct about various events.  But stories are not science, and it’s important to keep the distinction straight, especially when we’re trying to understand why things happen.

An even better term than pseudo-science is Feynman’s term, Cargo Cult Science.  Rosenzweig quotes Feynman:

In the South Seas, there is a cult of people.  During the war, they saw airplanes land with lots of materials, and they want the same thing to happen now.  So they’ve arranged to make things like runways, to put fires along the sides of the runways, to make a wooden hut for a man to sit in, with two wooden pieces on his head like headphones and bars of bamboo sticking out like antennas—he’s the controller—and they wait for the airplanes to land.  They’re doing everything right.  The form is perfect.  But it doesn’t work.  No airplanes land.  So I call these things Cargo Cult Science, because they follow all the apparent precepts and forms of scientific investigation, but they’re missing something essential, because the planes don’t land.

(Photo of Richard Feynman in 1984, by Tamiko Thiel)

Rosenzweig concludes:

The business world is full of Cargo Cult Science, books and articles that claim to be rigorous scientific research but operate mainly at the level of storytelling.  In later chapters, we’ll look at some of this research—some that meet the standard of science but aren’t satisfying as stories, and some that offer wonderful stories but are doubtful as science.  As we’ll see, some of the most successful business books of recent years, perched atop the bestseller list for months on end, cloak themselves in the mantle of science, but have little more predictive power than a pair of coconut headsets on a tropical island.

It’s not that stories have nothing to teach us.  For instance, experts may develop deep historical knowledge that offers us useful insights into human behavior.  And such knowledge is often an antecedent to scientific knowledge.

But we have to be careful not to confuse stories with science.  Otherwise, it’s very easy and natural to delude ourselves that we understand something scientifically, when in fact we don’t.  Our intuition creates simply stories of cause and effect just as automatically as our visual system is unable to avoid optical illusions.

(Holy grail or two girls, by Micka)

 

THE STORY OF CISCO

Rosenzweig tells the story of Cisco.  Sandra K. Lerner and Leonard Bosack met in graduate school, fell in love, and got married.  After graduating, they each took jobs managing computer networks at different corners of the Stanford campus.  They wanted to communicate, and they invented a multiprotocol router.  Rosenzweig:

Like many start-ups, Cisco began by operating out of a basement and at first sold its wares to friends and professional acquaintances.  Once revenues approached $1 million, Lerner and Bosack went in search of venture capital.  The man who finally said yes was Donald Valentine at Sequoia Capital, the seventy-seventh moneyman they approached, who invested $2.5 million for a third of the stock and management control.  Valentine began to professionalize Cisco’s management, bringing in as CEO an industry veteran, John Morgridge.  Sales grew rapidly, from $1.5 million in 1987 to $28 million in 1989, and in February 1990, Cisco went public.

Valentine and Morgridge brought on John Chambers as a sales executive in 1991.  Chambers had worked at IBM and Wang Labs, and was ready to work at a smaller company where he might have more of an impact.  Chambers came up with a plan for Cisco to dominate the market for computer infrastructure.  Over the next three years, Cisco acquired two dozen companies.

(Cisco Logo, via Wikimedia Commons)

Chambers became CEO in 1995 and Cisco continued acquiring companies.  Cisco’s revenues reached $4 billion in 1997.  Rosenzweig:

Cisco rode the crest of the internet wave in 1998… Cisco had a 40 percent share of the $20 billion data-networking equipment industry—routers, hubs, and devices that made up the so-called plumbing of the Internet—and a massive 80 percent share of the high-end router market.  But Cisco wasn’t just growing revenues.  It was profitable, too.  At a time when even the most admired Internet start-ups, like Amazon.com, were losing money, Cisco posted operating margins of 60 percent.  This wasn’t some dot-com with a business plan, way out there in the blue, riding on a smile and a shoeshine.  It wasn’t panning for Internet gold, it was selling picks and shovels to miners who were lining up around the corner to buy them…

Cisco reached $100 billion market capitalization in just twelve years.  It had taken Microsoft twenty years (the previous record).

Accounts explaining Cisco’s success nearly always gave credit to John Chambers.  He’d overcome dyslexia to go to law school.  And Chambers said he learned from working at IBM and Wang that if you don’t react to shifts in technology, your work will be lost and the lives of employees disrupted.  Cisco wouldn’t make that mistake, Chambers declared.

Cisco had a disciplined, detailed process for making acquisitions, and an even more disciplined process for integrating acquisitions into Cisco’s operations.  Cisco had made “a science” of acquisitions.  And it cared a lot about the human side—turnover rate for acquired employees was only 2.1% versus an industry average of 20%.

After the Internet stock bubble burst, business reporters completely reversed their opinion of Cisco on every major point:

  • Customer service—from excellent to poor
  • Forecasting ability—from outstanding to terrible
  • Innovation—from nearly perfect to visibly flawed
  • Acquisitions—from scientific process to binge buying
  • Senior leadership—from amazing to arrogant

Business reporters recalled that Chambers had claimed that Cisco “was faster, smarter, and just plain better than competitors.”  Rosenzweig says this is fascinating because only business reporters had said this when Cisco was doing well.  Chambers himself never said it, but now business writers seemed to recall that he had.

Rosenzweig points out that it was possible that Cisco had changed.  But that’s not what business reporters were saying.  They viewed Cisco through an entirely different lens, now that the company was struggling.

The essence of the Halo Effect: If a company is performing well, then it’s easy to view virtually everything it does through a positive lens.  If a company is doing poorly, then it’s natural to view virtually everything it does through a negative lens.  The story of Cisco certainly fits this pattern.

As Rosenzweig remarks, the fundamental problem is twofold:

  • We have little scientific knowledge of what leads to business success or failure.
  • But we do know about revenues, profits, and the stock price.  If these observable measures are positive, we intuitively jump to the conclusion that the company must be doing many things well.  If these observable measures are declining, we conclude that the company must be doing many things poorly.

 

UP AND DOWN WITH ABB

ABB is a Swedish-Swiss industrial company that was created in 1988 by the merger of two leading engineering companies, Sweden’s ASEA and Switzerland’s Brown Boveri.

(ABB Logo, via Wikimedia Commons)

Rosenzweig thought it would be interesting to look at a non-American, non-Internet company.  The Halo Effect is still clearly visible in the accounts of ABB’s rise and fall.

When it came to ABB’s rise, from the late 1980’s to the late 1990’s, we see that business experts drew similar conclusions.  First, the CEO, Percy Barnevik, was widely and highly praised.  Rosenzweig describes Barnevik as a “Scandinavian who combined old world manners and language skills with American pragmatism and an orientation for action.”  Barnevik was described in the press as very driven, but also unpretentious and accessible.  He met frequently with all levels of ABB management.  He was a speed reader and highly analytical.  Away from work, he climbed mountains and went for long jogs (lasting up to 10 hours).  On top of all this, Barnevik was viewed as humble, not arrogant.

By 1993, Barnevik had become a legend.  Another explanation for ABB’s success was its culture.  Despite its conservative Swedish and Swiss roots, ABB had a strong bias for action.  Barnevik said so on several occasions, asserting that the only unacceptable thing was to do nothing.  He claimed that if you do 50 things, and 35 are in the right direction, that is enough.

Third, ABB was designed to be globally efficient, but still able to compete in local markets.  Barnevik wanted people in different locations to be able to launch new products, make design changes, or alter production methods.  ABB had a matrix structure, with fifty-one business areas and forty-one country managers.  This resulted in 5,000 profit centers, with each one empowered to achieve high performance and accountable to do so.

In 1996, ABB was named Europe’s Most Respected Company for the third year in a row by the Financial Times.  Kevin Barham and Claudia Heimer, of Ashridge Management Centre in England, published a 382-page book about ABB.  They identified five reasons for ABB’s success:  customer focus, connectivity, communication, collegiality, and convergence.  They placed ABB in the same category as Microsoft and General Electric.

In 1997, Barnevik stepped down as CEO, replaced by Goran Lindahl.  Then the company transitioned towards businesses based on intellectual capital.  ABB entered new areas, like financial services.  It exited the trains and trams business, as well as the nuclear fuels business.  Rosensweig asks if ABB was “straying from its core.”  Not at all because ABB was still seen as a success.  Lindahl was CEO of the year in 1999 according to the American publication, Industry Week.  Lindahl was the first European to get this award.

In November 2000, Lindahl abruptly stepped down, saying he wanted to be replaced by someone with more expertise in IT.  Jürgen Centerman became the new CEO.

ABB’s performance entered a steep decline.  Centerman was replaced by Jürgen Dormann in September 2002.  Dormann sold the company’s petrochemicals business and its structured finance business.  ABB focused on automation technologies and power technologies.  But the company’s market cap dipped below $4 billion, down from a peak of $40 billion.

When ABB was on the rise in terms of performance, it was described as bold and daring because of its bias for action and experimentation.  Now, with performance being poor, ABB was described as impulsive and foolish.  Moreover, whereas ABB’s decentralized strategy had been praised when ABB was rising, now the same strategy was criticized.  As for Barnevik, while he had previously been described as bold and visionary, now he was called arrogant and imperial.

Most interesting of all, notes Rosenzweig, is that neither the company nor Barnevik was thought to have changed.  It was only how they were characterized that had changed—clear examples of the Halo Effect.

Rosenzweig writes:

…one of the main reasons we love stories is that they don’t simply report disconnected facts but make connections about cause and effect, often ascribing credit or blame to individuals.  Our most compelling stories often place people at the center of events… Once widely revered, Percy Barnevik was now an exemplar of arrogance, of greed, of bad leadership.

 

HALOS ALL AROUND US

During World War I, the American psychologist Edward Thorndike studied how superiors rated their subordinates.  Thorndike noticed that good soldiers were good on nearly every attribute, whereas underperforming soldiers were bad on nearly every attribute.  Rosenzweig comments:

It was as if officers figured that a soldier who was handsome and had good posture should also be able to shoot straight, polish his shoes well, and play the harmonica, too.

Thorndike called this the Halo Effect.  Rosenzweig:

There are a few kinds of Halo Effect.  One refers to what Thorndike observed, a tendency to make inferences about specific traits on the basis of a general impression.  It’s difficult for most people to independently measure separate features; there’s a common tendency to blend them together.  The Halo Effect is a way for the mind to create and maintain a coherent and consistent picture, to reduce cognitive dissonance.

(Image by Aliaksandra Molash)

Rosenzweig gives the example of George W. Bush.  After the September 11 attacks in 2001, Bush’s approval ratings rose sharply, not surprisingly as the public rallied behind him.  But Bush’s ratings on other factors, such as his management of the economy, also rose significantly.  There was no logical reason to think Bush’s handling of the economy was suddenly much better after the attacks.  This is an instance of the Halo Effect.

By October 2005, the situation had reversed.  Support for the Iraq War waned, and people were upset about the government response to Hurricane Katrina.   Bush’s overall ratings were at 37 percent.  His rating was also lower in every individual category.

Rosenzweig then explains another kind of Halo Effect:

…the Halo Effect is not just a way to reduce cognitive dissonance.  It’s also a heuristic, a sort of rule of thumb that people use to make guesses about things that are hard to assess directly.  We tend to grasp information that is relevant, tangible, and appears to be objective, and then make attributions about other features that are more vague or ambiguous.

Rosenzweig later adds:

All of which helps explain what we saw at Cisco and ABB.  As long as Cisco was growing and profitable and setting records for its share price, managers and journalists and professors inferred that it had a wonderful ability to listen to its customers, a cohesive corporate culture, and a brilliant strategy.  And when the bubble burst, observers were quick to make the opposite attribution.  It all made sense.  It told a coherent story.  Same for ABB, where rising sales and profits led to favorable evaluations of its organization structure, its risk-taking culture, and most clearly the man at the top—and then to unfavorable evaluations when performance fell.

Rosenzweig recounts an experiment by professor Barry Staw.  Various groups of people were asked to forecast future sales and earnings based on a set of financial data.  Then some groups were told they’d done a good job, while other groups were told the opposite.  But this was done at random, completely independent of actual performance.

Later, each group was asked about how it had functioned as a group.  Groups that had been told that they did well on their forecasts reported that their group had been cohesive, with good communication, openness to change, and good motivation.  Groups that had been told that they didn’t do well on their forecasts reported that they lacked cohesion, had poor communication, and were unmotivated.

Staw’s experiment is a clear demonstration of the Halo Effect.  Completely irrespective of whether the group actually is effective or not—which, after all, can be very difficult to measure:

  • If people believe that a group is effective, then they attribute one set of characteristics to it.
  • If people believe that a group is ineffective, then they attribute the opposite set of characteristics to it.

This doesn’t mean that cohesiveness, motivation, etc., is unimportant for group communication.  Rather, it means that people typically cannot assess these types of qualities with much (or any) objectivity, especially if they already have a belief about how a given group has performed in some task.

When it’s hard to measure something objectively, people tend to look for something that is objective and use that as a heuristic, inferring that harder-to-measure attributes must be similar to whatever is objective (like financial peformance).

As yet another example, Rosenzweig mentions that IBM’s employees were viewed as smart, creative, and hardworking in 1984 when IBM was doing well.  In 1992, after IBM had faltered, the same people were described as complacent and bureauratic.

As we’ve seen, the Halo Effect is particularly frequent when people try to judge how good a leader is.  Just as we don’t have much scientific knowledge for how a company can succeed, we also don’t have much scientific knowledge about what makes a good leader.  Experts, when they look at a company that is doing well, tend to think that the leader has many good qualities such as courage, clear vision, and integrity.  When the same experts examine a company that is doing poorly, they tend to conclude that the leader lacks courage, vision, and integrity.  This happens even when experts are looking at the same company and that company is doing the same things.

(Image by Kirsty Pargeter)

When Microsoft was doing well, Bill Gates was described as ambitious, brilliant, and visionary.  When Microsoft appeared to falter in 2001, after Judge Thomas Penfield Jackson ordered Microsoft to be broken up, Bill Gates was described as arrogant and stubborn.

Rosenzweig gives two more examples:  Fortune’s World’s Most Admired Companies, and the Great Places to Work Institute’s Best Companies to Work For index.  Both lists appear to be significantly impacted by the Halo Effect.  Companies that have been doing well financially tend to be viewed and described much more favorably on a range of metrics.

Rosenzweig closes the chapter by noting that the Halo Effect is the most basic delusion, but that there are several more delusions he will examine in the coming chapters.

 

RESEARCH TO THE RESCUE?

Rosenzweig:

The Halo Effect shapes how we commonly talk about so many topics in business, from decision processes to people to leadership and more.  It shows up in our everyday conversations and in newspaper and magazine articles.  It affects case studies and large-sample surveys.  It’s not so much the result of conscious distortion as it is a natural human tendency to make judgments about things that are abstract and ambiguous on the basis of other things that are salient and seemingly objective.  The Halo Effect is just too strong, the desire to tell a coherent story too great, the tendency to jump on bandwagons too appealing.

The most fundamental business question is:

What leads to high business performance?

The Halo Effect is far from inevitable, despite being very common.  There are researchers who use careful statistical tests to isolate the effects of independent variables on dependent variables.

The dependent variables relate to company performance.  And we have good data on that, from revenues to profits to return on capital.

As for the independent variables, some of these, such as R&D spending, are not tainted.  Much trickier is what happens inside a company, such as quality of management, customer orientation, company culture, etc.

Rosenzweig explores the question of whether customer focus leads to better company performance.  It probably does.  However, in order to measure the effect of customer focus on performance objectively, we should not look at magazine and newspaper articles—since these are impacted by the Halo Effect.  Nor should we ask company employees about their customer focus.  How a company is performing—well or poorly—will impact the opinions of managers and employees regarding customer focus.

Similar logic applies to the question of how corporate culture impacts business performance.  Surveys of managers and employees will be tainted by the Halo Effect.  Yes, corporate culture impacts business performance.  But to figure out the statistical correlation, we have to be sure to avoid data likely to be skewed by the Halo Effect.

Delusion Two: The Delusion of Correlation and Causality

Rosenzweig gives the example of employee turnover and company performance.  If there is a statistical correlation between the two, then what does that mean?  Does lower employee turnover lead to higher company performance?  That sounds reasonable.  On the other hand, does higher company performance lead to lower employee turnover?  That could very well be the case.

Potential confusion about correlation versus causality is widespread when it comes to the study of business.

One way to get some insight into potential causality is to conduct a longitudinal study, looking at independent variables in one period and hypothetically dependent variables in some later period.  Rosenzweig:

One recent study, by Benjamin Schneider and colleagues at the University of Maryland, used a longitudinal design to examine the question of employee satisfaction and company performance to try to find out which one causes which.  They gathered data over several years so they could watch both changes in satisfaction and changes in company performance.  Their conclusion?  Financial performance, measured by return on assets and earnings per share, has a more powerful effect on employee satisfaction than the reverse.  It seems that being on a winning team is a stronger cause of employee satisfaction; satisfied employees don’t have as much of an effect on company performance.  How were Schneider and his colleagues able to break the logjam and answer the question of which leads to which?  By gathering data over time.

Delusion Three: The Delusion of Single Explanations

Rosenzweig describes two studies that were carefully conducted, one on the effect of market orientation on company performance, and the other on the effect of CSR—corporate social responsibility—on company performance.  The studies were careful in that they didn’t just ask for opinions.  They asked about different activities in which the company did or did not engage.

The conclusion of the first study was that market orientation is responsible for 25 percent of company performance.  The second study concluded that CSR is responsible for 40 percent of company performance.  Rosenzweig asks: Does that mean that market orientation and CSR together explain 65 percent of company performance?  Or do the variables overlap to an extent?  The problem with studying a single cause of company performance is that you don’t know if part of the effect may be due to some other variable you’re not measuring.  If a company is well-managed, then wouldn’t that be seen in market orientation and also in CSR?

(Photo by Jörg Stöber)

We could throw human resource management—HRM—into the mix, too.  Same goes for leadership.  One study found that good leadership is responsible for 15 percent of company performance.  But is that in addition to market orientation, CSR, and HRM?  Or do these things overlap to an extent?  It’s likely that there is significant overlap among these four variables.

One problem is that many researchers would like to tell a clear story about cause and effect.  Admitting that many key variables likely overlap means that the story is much less clear.  People—especially if busy or pressured—prefer simple stories where cause and effect seem obvious.

Furthermore, many important questions are at the intersection of different fields.  Rosenzweig gives the example of decision making, which involves psychology, sociology, and economics.  The trouble is that an expert in marketing will tend to exaggerate the importance of marketing.  An expert in CSR will tend to exaggerate the importance of CSR.  And so forth for other specialties.

 

SEARCHING FOR STARS, FINDING HALOS

Rosenzweig lists the eight practices of America’s best companies according to In Search of Excellence: Lessons from America’s Best-Run Companies, published by Tom Peters and Bob Waterman in 1982:

  • A bias for action—a preference for doing something—anything—rather than sending a question through cycles and cycles of analyses and committee reports.
  • Staying close to the customer—learning his preferences and catering to them.
  • Autonomy and entrepreneurship—breaking the corporation into small companies and encouraging them to think independently and competitively.
  • Productivity through people—creating in all employees the awareness that their best efforts are essential and that they will share in the rewards of the company’s success.
  • Hands-on, value-driven—insisting that executives keep in touch with the firm’s essential business.
  • Stick to the knitting—remaining with the business the company knows best.
  • Simple form, lean staff—few administrative layers, few people at the upper levels.
  • Simultaneous loose-tight properties—fostering a climate where there is dedication to the central values of the company combined with a tolerance for all employees who accept those values.

Rosenzweig points out that this list looks familiar:  Care about your customers.  Have strong values.  Create a culture where people can thrive.  Empower your employees.  Stay focused.

If these look correlated, says Rosenzweig, that’s because they are.  The best companies do all of them.  Of course, again there’s the Halo Effect.  If you isolate the top-performing companies (43 of them in this case), and then ask managers and employees about customer focus, values, culture, leadership, focus, etc., then you won’t know what caused what.  Did clear strategy, good organization, strong corporate culture, and customer focus lead to the high performance?  Or do people view high-performing companies as doing well in these areas?

(Image by Eriksvoboda)

When the book was published in 1982, there was a widespread concern among American businesses that Japanese companies were better overall.  Peters and Waterman made the point that the leading American businesses were doing well in a variety of key areas.  This message was viewed not only as inspirational, but even as patriotic.  It was the right story for the times.

Many thought that In Search of Excellence contained scientific knowledge:  if x, then y (with probability z).  People thought that if they implemented the principles highlighted by Peters and Waterman, then they would be successful in business.

However, just two years later, some of the excellent companies did not seem as excellent as before.  Some were blamed for changing—not sticking to their knitting.  Others were blamed for NOT changing—not being adaptable enough, not taking action.  More generally, some were blamed for overemphasizing certain principles, while underemphasizing other principles.

Rosenzweig examined the profitability of 35 of the 43 excellent companies—the 35 companies for which data were available because these companies were public.  He found that, in the five years after 1982, 30 out of 35 had a decline in profitability.  If these were truly excellent companies, then such a decline for 30 of 35 doesn’t make sense.

(Image by Dejan Lazarevic)

Rosenzweig observes that it’s possible that the previous success of these companies was due to more than the eight principles identified by Peters and Waterman.  And so changes in other variables may explain the subsequent declines in profitability.  It’s also possible—because Peters and Waterman identified 43 highly successful companies and then interviewed managers at those companies—that the Halo Effect came into play.  The eight principles may reflect attributions that people tend to make about currently successful companies.

Delusion Four: The Delusion of Connecting the Winning Dots

You can’t choose a sample based only on the dependent variable you’re trying to test.  The dependent variable in this case is successful companies.  If all you look at is successful companies, then you won’t be able to compare successful companies directly to unsuccessful companies in order to learn about their respective causes—the independent variables.  Rosenzweig refers to this error as the Delusion of Connecting the Winning Dots.  You can connect the dots any way you wish, but following this approach, you can’t learn about the independent variables that lead to success.

Like many areas of social science, it’s not easy.  You can’t run an experiment where you take 100 companies, and manage half of them one way, and half of them another way, and then compare results.

(Image by Macrovector)

Jim Collins and Jerry Porras isolated 18 companies based on excellent performance over a long period of time.  Also, for each of these companies, Collins and Porras identified a similar company that had been less successful.  This at least could avoid the error that Peters and Waterman made.  As Collins and Porras said, if all you looked at were successful companies, you might find that they all reside in buildings.

Collins, Porras, and their team read more than 100 books and looked at more than 3,000 documents.  All told, they had a huge amount of data.  They certainly worked very hard.  But that in itself does not increase the scientific validity of their study.

Collins and Porras claimed to have found “timeless principles,” which they listed:

  • Having a strong core ideology that guides the company’s decisions and behavior
  • Building a strong corporate culture
  • Setting audacious goals that can inspire and stretch people—so-called big hairy audacious goals, or BHAGs
  • Developing people and promoting them from within
  • Creating a spirit of experimentation and risk taking
  • Driving for excellence

Unfortunately, much of the data came from books, the business press, and company documents, all likely to contain Halos.  They also conducted interviews with managers, who were asked to look back on their success and explain the reasons.  These interviews were probably tinged by Halos in many cases.  Some of the principles identified may have led to success.  However, successful companies were also likely to be described in these terms.  The Halo Effect hadn’t been dealt with by Collins and Porras.

Rosenzweig looked at profitability over the subsequent five years.  Eleven companies saw profits decline.  One was unchanged.  Only five of the best companies had profits increase.  It seems the “master blueprint for long-term prosperity” is largely a delusion, writes Rosenzweig.

(Graph by Experimental)

It’s not just some of the companies, but most of the companies that saw profits decline.  Characterizations of the “best” companies were probably impacted significantly by the Halo Effect.  The very fact that these companies had been doing well for some time led many to see them as having positive attributes across the board.

Delusion Five: The Delusion of Rigorous Research

As noted, psychologist Philip Tetlock tracked the predictions of 284 leading experts over two decades.  Tetlock looked at over 27,000 predictions in real time of the form:  more of x, no change in x, or less of x.  He found that these predictions were no better than random chance.

Many experts have deep knowledge—historical or otherwise—that can give us valuable insights into human affairs.  Some of this expertise is probably accurate.  But until we have testable predictions, it’s difficult to say which hypotheses are true and to what degree.

We should never forget the difference between scientific knowledge and other types of knowledge, including stories.  It’s very easy for us humans to be overconfident and deluded, especially if certain stories are the result of “many years of hard work.”

Delusion Six: The Delusion of Lasting Success

Richard Foster and Sarah Kaplan looked at companies in the S&P 500 from 1957 to 1997.  By 1997, only 74 out of the original largest 500 companies were still in the S&P 500.  Of those 74 survivors, how many outperformed the S&P 500 over those 40 years?  Only 12.

Foster and Kaplan conclude:

KcKinsey’s long-term studies of corporate birth, survival, and death in America clearly show that the corporate equivalent of El Dorado, the golden company that continually performs better than the markets, has never existed.  It is a myth.  Managing for survival, even among the best and most revered corporations, does not guarantee strong long-term performance for shareholders.  In fact, just the opposite is true.  In the long run, the markets always win.

It’s not that busines success is completely random.  Of course not.  But there is usually a large degree of luck involved.  More fundamentally, capitalism is about competition through innovation, or creative destruction, as the great Austrian economist Joseph Schumpeter called it.  There is some inherent unpredictability—or luck—in this endless process.

Delusion Seven: The Delusion of Absolute Performance

Kmart improved noticeably from 1994 to 2002, but Wal-Mart and Target were ahead at the beginning of that period, and they improved even faster than Kmart.  Thus, although it would seem Kmart was doing the right things in terms of absolute performance, Kmart was falling even further behind in terms of relative performance.

In 2005, GM was making much better cars than in the 1980s.  But its market share kept slipping, from 35 percent in 1990 to 25 percent in 2005.  GM’s competitors were improving faster.

Rosenzweig sums it up:

The greater the number of rivals, and the easier for competitors to enter the market, and the more rapidly technology changes, the more difficult it is to sustain success.  That’s an uncomfortable truth, because it admits that some elements of business performance are outside of our control.  It’s far more appealing to downplay the relative nature of performance or ignore it completely.  Telling a company it can achieve high performance, regardless of what competitors do, makes for a more attractive story.

Delusion Eight: The Delusion of the Wrong End of the Stick

In Good to Great, Collins argues that a company can decide to become great and follow the blueprint in the book.  Part of the recipe is to be like a Hedgehog—to have a narrow focus and pursue it with great discipline.  The problem, again, is that the role of chance—or factors outside one’s control—is not considered.  (The terms “Hedgehog” and “Fox” come from an essay by Isaiah Berlin.  The Hedgehog knows one big thing, whereas the Fox knows many things.)

(Image by Marek Uliasz)

Statistically, it’s possible that, on the whole, more Hedgehogs than Foxes failed.  You could still argue that the potential upside for becoming a great company is so large that it’s worth taking the risk of being like a Hedgehog.  But Collins doesn’t mention risk, or chance, at all.

Of course, we’d all prefer a story where greatness is purely a matter of choice.  But it’s rarely that simple and luck nearly always plays a pivotal role.

Delusion Nine: The Delusion of Organizational Physics

For many questions in business, we can’t run experiments.  That said, with enough care, important statistical correlations can be discovered.  Other things can be measured even more precisely.

But to think that the study of business is like the science of physics is a delusion, at least for now.

It’s reasonable to suppose that, with enough scientific knowledge in neuroscience, genetics, psychology, economics, artificial intelligence, and related areas, eventually human behavior may become largely predictable.  But there’s a long way to go.

 

THE MOTHER OF ALL BUSINESS QUESTIONS, TAKE TWO

By nature, we prefer stories where business success is entirely a result of choosing to do the right things, while not reaching success must be due to a failure to do the right things.  But stories like this neglect the role of chance.  Rosenzweig writes:

…all the emphasis on steps and formulas may obscure a more simple truth.  It may further the fiction that a specific set of steps will lead, predictably, to success.  And if you never achieve greatness, well, the problem isn’t with our formula—which was, after all, the product of rigorous research, of extensive data exhaustively analyzed—but with you and your failure to follow the formula.  But in fact, the truth may be considerably simpler than these formula suggest.  They may divert our attention from a more powerful insight—that while we can do many things to improve our chances of success, at its core business performance contains a large measure of uncertainty.  Business performance may actually be simpler than it is often made out to be, but may also be less certain and less amenable to engineering with predictable outcomes.

There is a simpler way to think about business performance—suggested by Michael Porter—without neglecting the role of chance.  Strategy is doing certain things different from rivals.  Execution is people working together to create products by implementing the strategy.  This is a reasonable way to think about business performance as long as you also note the role of chance.

It’s usually hard to know how potential customers will behave.  There are, of course, many examples where, contrary to expectations, a product was embraced or rejected.  Moreover, even if you correctly understand customers, competitors may come up with a better product.

There’s also the issue of technological change, which can be a significant source of unpredictability in some industries.

(Illustration by T. L. Furrer)

Clayton Christensen has demonstrated—in The Innovator’s Dilemma—that frequently companies fail because they keep doing the right things, giving customers what they want.  Meanwhile, competitors develop a new technology that, at first, is not profitable—which is part of why the company “doing the right things” ignores it.  But then, unpredictably, some of these new technologies end up being popular and also profitable.

One good question is:  What should a company do when its core comes under pressure?  Should it redouble its focus on the core, like a Hedgehog?  Or should it be adaptable, like a Fox?  There are no good answers at the moment, says Rosenzweig.  There are too many variables.  Chance—or uncertainty—plays a key role.

Rosenzweig continues:

In the meantime, we’re left with the brutal fact that strategic choice is hugely consequential for a company’s performance yet also inherently risky.  We may look at successful companies and applaud them for what seem, in retrospect, to have been brilliant decisions, but we forget that at the time those decisions were made, they were controversial and risky.  McDonald’s bet on franchising looks smart today, but in the 1950s it was a leap in the dark.  Dell’s strategy of selling direct now seems brilliant but was attempted only after multiple failures with conventional channels.  Or, recalling companies we discussed in earlier chapters, remember Cisco’s decision to assemble a full range of product offerings through acquisitions or ABB’s bet on leading rationalization of the European power industry through consolidation and cost cutting.  The managers who took those choices appraised a wide variety of factors and decided to be different from their rivals.  We remember all of these decisions because they turned out well, but success was not inevitable.  As James March of Stanford and Zur Shapira of New York University explained, “Post hoc reconstruction permits history to be told in such a way that ‘chance,’ either in the sense of genuinely probabilistic phenomena or in the sense of unexplained variation, is minimized as an explanation.”  But chance DOES play a role, and the difference between a brilliant visionary and a foolish gambler is usually inferred after the fact, an attribution based on outcomes.  The fact is, strategic choices always involve risk.  The task of strategic leadership is to gather appropriate information and evaluate it thoughtfully, then make choice that, while risky, provide the best chances for success in a competitive industry setting.

(Image by Donfiore)

As for execution, certain practices do correlate with modestly higher performance.  If leaders can identify the few areas where better execution is needed, then some progress can be made.

But inherent unpredictability is hidden by the Halo Effect.  If a company succeeds, it’s easy to say it executed well.  If a company fails, it’s natural to conclude that execution was poor.  Often to a large extent, these conclusions are driven by the Halo Effect, even if there is some truth to them.

In brief, smart strategic choices and good execution—plus good luck—may lead to success, at least temporarily.  But success brings challengers, some of whom will take greater risks that may work.  There’s no formula to guarantee success.  And if success is achieved, there’s no way to guarantee continued success over time.

 

MANAGING WITHOUT COCONUT HEADSETS

Given that there’s no simple formula that brings business success, what should we do?  Rosenzweig answers:

A first step is to set aside the delusions that color so much of our thinking about business performance.  To recognize that stories of inspiration may give us comfort but have little more predictive power than a pair of coconut headsets on a tropical island.  Instead, managers would do better to understand that business success is relative, not absolute, and that competitive advantage demands calculated risks.  To accept that few companies achieve lasting success, and that those that do are perhaps best understood as having strung together several short-term successes rather than having consciously pursued enduring greatness.  To admit that, as Tom Lester of the Financial Times so neatly put it, “the margin between success and failure is often very narrow, and never quite as distinct or as enduring as it appears at a distance.”  By extension, to recognize that good decisions don’t always lead to favorable outcomes, that unfavorable outcomes are not always the result of mistakes, and therefore to resist the natural tendency to make attributions based solely on outcomes.  And finally, to acknowledge that luck often plays a role in company success.  Successful companies aren’t “just lucky”—high performance is not purely random—but good fortune does play a role, and sometimes a pivotal one.

Rosenzweig mentions Robert Rubin as a good example of someone who learned to make decisions in terms of scenarios and their probabilities.

(Image by Elnur)

Rubin worked for eight years in the Clinton administration, first as director of the White House National Economic Council and later as secretary of the Treasury.  Prior to working in the Clinton administration, Rubin toiled for twenty-six years at Goldman Sachs.

Rubin first learned about the fundamental uncertainties of the world when he studied philosophy as an undergraduate.  He learned to view every proposition with skepticism.  Later at Goldman Sachs, Rubin saw first-hand that one had to consider possible outcomes and their associated probabilities.

Rubin spent years in risk arbitrage.  Many times Goldman made money, but roughly one out of every seven times, Goldman lost money.  Sometimes the loss would greatly exceed Goldman’s worst-case scenario.  But occasionally large and painful losses didn’t mean that Goldman’s decision-making process was flawed.  In fact, if Goldman wasn’t taking some losses, then they almost certainly weren’t taking enough risk.

(Photo by Alain Lacroix)

Rosenzweig asks:  If a large and painful loss doesn’t mean a mistake, then what does?

We have to take a close look at the decision process itself, setting aside the eventual outcome.  Had the right information been gathered, or had some important data been overlooked?  Were the assumptions reasonable, or had they been flawed?  Were calculations accurate, or had there been errors?  Had the full set of eventualities been identified and their impact estimated?  Had Goldman Sachs’s overall risk portfolio been properly considered?

Once again, a profitable outcome doesn’t necessarily mean the decision was good.  An unprofitable outcome doesn’t necessarily mean the decision was bad.  If you’re making decisions under uncertainty—probabilistic decisions—the only way to improve is to evaluate the process of decision-making independently of specific outcomes.

Of course, often important decisions for an individual business are quite infrequent.  Rosenzweig highlights important lessons for managers:

  • If independent variables aren’t measured independently, we may find ourselves standing hip-deep in Halos.
  • If the data are full of Halos, it doesn’t matter how much we’ve gathered or how sophisticated our analysis appears to be.
  • Success rarely lasts as long as we’d like—for the most part, long-term success is a delusion based on selection after the fact.
  • Company performance is relative, not absolute.  A company can get better and fall further behind at the same time.
  • It may be true that many successful companies bet on long shots, but betting on long shots does not often lead to success.
  • Anyone who claims to have found laws of business physics either understands little about business, or little about physics, or both.
  • Searching for the secrets of business success reveals little about the world of business but speaks volumes about the searchers—their aspirations and their desires for certainty.

Getting rid of delusions is a crucial step.  Furthermore, writes Rosenzweig, a wise manager knows:

  • Any good strategy involves risk.  If you think your strategy is foolproof, the fool may well be you.
  • Execution, too, is uncertain—what works in one company with one workforce may have different results elsewhere.
  • Chance often plays a greater role than we think, or than successful managers usually like to admit.
  • The link between inputs and outcomes is tenuous.  Bad outcomes don’t always mean that managers made mistakes; and good outcomes don’t always mean they acted brilliantly.
  • But when the die is cast, the best managers act as if chance is irrelevant—persistence and tenacity are everything.

Of course, none of this guarantees success.  But the sensible goal is to improve your chances of success.

 

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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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.

Cheap, Solid Microcaps Far Outperform the S&P 500

(Image: Zen Buddha Silence, by Marilyn Barbone)

November 12, 2017

The wisest long-term investment for most investors is an S&P 500 index fund.  Warren Buffett has maintained this position for some time: 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 has maintained that 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 the course of several decades, 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.

 

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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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.

Lifelong Learning

(Image: Zen Buddha Silence, by Marilyn Barbone)

October 29, 2017

Lifelong learning—especially if pursued in a multidisciplinary fashion—can continuously improve your productivity and ability to think.  Lifelong learning boosts your capacity to serve others.

Robert Hagstrom’s wonderful book, Investing: The Last Liberal Art (Columbia University Press, 2013), is based on the notion of lifelong, multidisciplinary learning.

Ben Franklin was a strong advocate for this broad-based approach to education.  Charlie Munger—Warren Buffett’s business partner—wholeheartedly agrees with Franklin.  Hagstrom quotes Munger:

Worldly wisdom is mostly very, very simple.  There are a relatively small number of disciplines and a relatively small number of truly big ideas.  And it’s a lot of fun to figure out.  Even better, the fun never stops…

What I am urging on you is not that hard to do.  And the rewards are awesome… It’ll help you in business.  It’ll help you in law.  It’ll help you in life.  And it’ll help you in love… It makes you better able to serve others, it makes you better able to serve yourself, and it makes life more fun.

Hagstrom’s book is necessarily abbreviated.  This blog post even more so.  Nonetheless, I’ve tried to capture many of the chief lessons put forth by Hagstrom.

Here’s the outline:

  • A Latticework of Mental Models
  • Physics
  • Biology
  • Sociology
  • Psychology
  • Philosophy
  • Literature
  • Mathematics
  • Decision Making

(Image: Unfolding of the Mind, by Agsandrew)

 

A LATTICEWORK OF MENTAL MODELS

Charlie Munger has long maintained that in order to be able to solve a broad array of problems in life, you must have a latticework of mental models.  This means you have to master the central models from various areas—physics, biology, social studies, psychology, philosophy, literature, and mathematics.

As you assimilate the chief mental models, those models will strengthen and support one another, notes Hagstrom.  So when you make a decision—whether in investing or in any other area—that decision is more likely to be correct if multiple mental models have led you to the same conclusion.

Ultimately, a dedication to lifelong, multidiscipinary learning will make us better people—better leaders, citizens, parents, spouses, and friends.

In the summer of 1749, Ben Franklin put forward a proposal for the education of youth.  The Philadelphia Academy—later called the University of Pennsylvania—would stress both classical (“ornamental”) and practical education.  Hagstrom quotes Franklin:

As to their studies, it would be well if they could be taught everything that is useful and everything that is ornamental.  But art is long and their time is short.  It is therefore proposed that they learn those things that are likely to be most useful and most ornamental, regard being had to the several professions for which they are intended.

Franklin held that gaining the ability to think well required the study of philosophy, logic, mathematics, religion, government, law, chemistry, biology, health, agriculture, physics, and foreign languages.  Moreover, says Hagstrom, Franklin viewed the opportunity to study so many subjects as a wonderful gift rather than a burden.

(Painting by Mason Chamberlin (1762) – Philadelphia Museum of Art, via Wikimedia Commons)

Franklin himself was devoted to lifelong, multidisciplinary learning.  He remained open-minded and intellectually curious throughout his life.

Hagstrom also observes that innovation often depends on multidisciplinary thinking:

Innovative thinking, which is our goal, most often occurs when two or more mental models act in combination.

 

PHYSICS

Hagstrom remarks that the law of supply and demand in economics is based on the notion of equilibrium, a fundamental concept in physics.

(Research scientist writing physics diagrams and formulas, by Shawn Hempel)

Many historians consider Sir Isaac Newton to be the greatest scientific mind of all time, points out Hagstrom.  When he arrived at Trinity College at Cambridge, Newton had no mathematical training.  But the scientific revolution had already begun.  Newton was influenced by the ideas of Johannes Kepler, Galileo Galilei, and René Descartes.  Hagstrom:

The lesson Newton took from Kepler is one that has been repeated many times throughout history:  Our ability to answer even the most fundamental aspects of human existence depends largely on measuring instruments available at the time and the ability of scientists to apply rigorous mathematical reasoning to the data.

Galileo invented the telescope, which then proved that the heliocentric model proposed by Nicolaus Copernicus was correct, rather than the geocentric model—first proposed by Aristotle and later developed by Ptolemy.  Moreover, Galileo developed the mathematical laws that describe and predict falling objects.

Hagstrom then explains the influence of Descartes:

Descartes promoted a mechanical view of the world.  He argued that the only way to understand how something works is to build a mechanical model of it, even if that model is constructed only in our imagination.  According to Descartes, the human body, a falling rock, a growing tree, or a stormy night all suggested that mechanical laws were at work.  This mechanical view provided a powerful research program for seventeenth century scientists.  It suggested that no matter how complex or difficult the observation, it was possible to discover the underlying mechanical laws to explain the phenomenon.

In 1665, due to the Plague, Cambridge was shut down.  Newton was forced to retreat to the family farm.  Hagstrom writes that, in quiet and solitude, Newton’s genius emerged:

His first major discovery was the invention of fluxions or what we now call calculus.  Next he developed the theory of optics.  Previously it was believed that color was a mixture of light and darkness.  But in a series of experiments using a prism in a darkened room, Newton discovered that light was made up of a combination of the colors of the spectrum.  The highlight of that year, however, was Newton’s discovery of the universal law of gravitation.

(Copy of painting by Sir Godfrey Kneller (1689), via Wikimedia Commons)

Newton’s three laws of motion unified Kepler’s planetary laws with Galileo’s laws of falling bodies.  It took time for Newton to state his laws with mathematical precision.  He waited twenty years before finally publishing Principia Mathematica.

Newton’s three laws were central to a shift in worldview on the part of scientists.  The evolving scientific view held that the future could be predicted based on present data if scientists could discover the mathematical, mechanical laws underlying the data.

Prior to the scientific worldview, a mystery was often described as an unknowable characteristic of an “ultimate entity,” whether an “unmoved mover” or a deity.  Under the scientific worldview, a mystery is a chance to discover fundamental scientific laws.  The incredible progress of physics—which now includes quantum mechanics, relativity, and the Big Bang—has depended in part on the belief by scientists that reality is comprehensible.  Albert Einstein:

The most incomprehensible thing about the universe is that it is comprehensible.

Physics was—and is—so successful in explaining and predicting a wide range of phenomena that, not surprisingly, scientists from other fields have often wondered whether precise mathematical laws or ideas can be discovered to predict other types of phenomena.  Hagstrom:

In the nineteenth century, for instance, certain scholars wondered whether it was possible to apply the Newtonian vision to the affairs of men.  Adolphe Quetelet, a Belgian mathematician known for applying probability theory to social phenomena, introduced the idea of “social physics.”  Auguste Comte developed a science for explaining social organizations and for guiding social planning, a science he called sociology.  Economists, too, have turned their attention to the Newtonian paradigm and the laws of physics.

After Newton, scholars from many fields focused their attention on systems that demonstrate equilibrium (whether static or dynamic), believing that it is nature’s ultimate goal.  If any deviations in the forces occurred, it was assumed that the deviations were small and temporary—and the system would always revert back to equilibrium.

Hagstrom explains how the British economist Alfred Marshall adopted the concept of equilibrium in order to explain the law of supply and demand.  Hagstrom quotes Marshall:

When demand and supply are in stable equilibrium, if any accident should move the scale of production from its equilibrium position, there will instantly be brought into play forces tending to push it back to that position; just as a stone hanging from a string is displaced from its equilibrium position, the force of gravity will at once tend to bring it back to its equilibrium position.  The movements of the scale of production about its position of equilibrium will be of a somewhat similar kind.

(Alfred Marshall, via Wikimedia Commons)

Marshall’s Principles of Economics was the standard textbook until Paul Samuelson published Economics in 1948, says Hagstrom.  But the concept of equilibrium remained.  Firms seeking to maximize profits translate the preferences of households into products.  The logical structure of the exchange is a general equilibrium system, according to Samuelson.

Samuelson’s view of the stock market was influenced by the works of Louis Bachelier, Maurice Kendall, and Alfred Cowles, notes Hagstrom.

In 1932, Cowles founded the Cowles Commission for Research and Economics.  Later on, Cowles studied 6,904 predictions of the stock market from 1929 to 1944.  Cowles learned that no one had demonstrated any ability to predict the stock market.

Kendall, a professor of statistics at the London School of Economics, studied the histories of various individual stock prices going back fifty years.  Kendall was unable to find any patterns that would allow accurate predictions of future stock prices.

Samuelson thought that stock prices jump around because of uncertainty about how the businesses in question will perform in the future.  The intrinsic value of a given stock is determined by the future cash flow the business will produce.  But that future cash flow is unknown.

Bachelier’s work showed that the mathematical expectation of a speculator is zero, meaning that the current stock price is in equilibrium based on an equal number of buyers and sellers.

Samuelson, building on Bachelier’s work, invented the rational expectations hypothesis.  From the assumption that market participants are rational, it followed that the current stock price is the best collective guess of the intrinsic value of the business—based on estimated future cash flows.

Eugene Fama later extended Samuelson’s view into what came to be called the Efficient Markets Hypothesis (EMH).  Stock prices fully reflect all available information, therefore it’s not possible—except by luck—for any individual investor to beat the market over the long term.

Many scientists have questioned the EMH.  The stock market sometimes does not seem rational.  People often behave irrationally.

In science, however, it’s not enough to show that the existing theory has obvious flaws.  In order to supplant existing scientific theory, scientists must come up with a better theory—one that better predicts the phenomena in question.  Rationalist economics, including EMH, is still the best approximation for a wide range of phenomena.

Some scientists are working with the idea of a complex adaptive system as a possible replacement for more traditional ideas of the stock market. Hagstrom:

Every complex adaptive system is actually a network of many individual agents all acting in parallel and interacting with one another.  The critical variable that makes a system both complex and adaptive is the idea that agents (neurons, ants, or investors) in the system accumulate experience by interacting with other agents and then change themselves to adapt to a changing environment.  No thoughtful person, looking at the present stock market, can fail to conclude that it shows all the traits of a complex adaptive system.  And this takes us to the crux of the matter.  If a complex adaptive system is, by definition, continuously adapting, it is impossible for any such system, including the stock market, ever to reach a state of perfect equilibrium.

It’s much more widely accepted today that people often do behave irrationally.  But Fama argues that an efficient market does not require perfect rationality or information.

Hagstrom concludes that, while the market is mostly efficient, rationalist economics is not the full answer.  There’s much more to the story, although it will take time to work out the details.

 

BIOLOGY

(Photo by Ben Schonewille)

Robert Darwin, a respected physician, enrolled his son Charles at the University of Edinburgh.  Robert wanted his son to study medicine.  But Charles had no interest.  Instead, he spent his time studying geology and collecting insects and specimens.

Robert realized his son wouldn’t become a doctor, so he sent Charles to Cambridge to study divinity.  Although Charles got a bachelor’s degree in theology, he formed some important connections with scientists, says Hagstrom:

The Reverend John Stevens Henslow, professor of botany, permitted the enthusiastic amateur to sit in on his lectures and to accompany him on his daily walks to study plant life.  Darwin spent so many hours in the professor’s company that he was known around the university as “the man who walks with Henslow.”

Later, Professor Henslow recommended Darwin for the position of naturalist on a naval expedition.  Darwin’s father objected, but Darwin’s uncle, Josiah Wedgewood II, intervened.  When the HMS Beagle set sail on December 27, 1831, from Plymouth, England, Charles Darwin was aboard.

Darwin’s most important observations happened at the Galapagos Islands, near the equator, six hundred miles west of Ecuador.  Hagstrom:

Darwin, the amateur geologist, knew that the Galapagos were classified as oceanic islands, meaning they had arisen from the sea by volcanic action with no life forms aboard.  Nature creates these islands and then waits to see what shows up.  An oceanic island eventually becomes inhabited but only by forms that can reach it by wings (birds) or wind (spores and seeds)…

Darwin was particularly fascinated by the presence of thirteen types of finches.  He first assumed these Galapagos finches, today called Darwin’s finches, were a subspecies of the South American finches he had studied earlier and had most likely been blown to sea in a storm.  But as he studied distribution patterns, Darwin observed that most islands in the archipelago carried only two or three types of finches; only the larger central islands showed greater diversification.  What intrigued him even more was that all the Galapagos finches differed in size and behavior.  Some were heavy-billed seedeaters; others were slender billed and favored insects.  Sailing through the archipelago, Darwin discovered that the finches on Hood Island were different from those on Tower Island and that both were different from those on Indefatigable Island.  He began to wonder what would happen if a few finches on Hood Island were blown by high winds to another island.  Darwin concluded that if the newcomers were pre-adapted to the new habitat, they would survive and multiply alongside the resident finches; if not, their number would ultimately diminish.  It was one thread of what would ultimately become his famous thesis.

(Galapagos Islands, Photo by Hugoht)

Hagstrom continues:

Reviewing his notes from the voyage, Darwin was deeply perplexed.  Why did the birds and tortoises on some islands of the Galapagos resemble the species found in South America while those on other islands did not?  This observation was even more disturbing when Darwin learned that the finches he brought back from the Galapagos belonged to different species and were not simply different varieties of the same species, as he had previously believed.  Darwin also discovered that the mockingbirds he had collected were three distinct species and the tortoises represented two species.  He began referring to these troubling questions as “the species problem,” and outlined his observations in a notebook he later entitled “Notebook on the Transmutation of the Species.”

Darwin now began an intense investigation into the species variation.  He devoured all the written work on the subject and exchanged voluminous correspondence with botanists, naturalists, and zookeepers—anyone who had information or opinions about species mutation.  What he learned convinced him that he was on the right track with his working hypothesis that species do in fact change, whether from place to place or from time period to time period.  The idea was not only radical at the time, it was blasphemous.  Darwin struggled to keep his work secret.

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

It took several years—until 1838—for Darwin to put together his hypothesis.  Darwin wrote in his notebook:

Being well-prepared to appreciate the struggle for existence which everywhere goes on from long-continued observation of the habits of animals and plants, it at once struck me that under these circumstances, favorable variations would tend to be preserved and unfavorable ones to be destroyed.  The result of this would be the formation of new species.  Here, then, I had at last got a theory—a process by which to work.

The struggle for survival was occurring not only between species, but also between individuals of the same species, Hagstrom points out.  Favorable variations are preserved.  After many generations, small gradual changes begin to add up to larger changes.  Evolution.

Darwin delayed publishing his ideas, perhaps because he knew they would be highly controversial, notes Hagstrom.  Finally, in 1859, Darwin published On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life.  The book sold out on its first day.  By 1872, The Origin of Species was in its sixth edition.

Hagstrom writes that in the first edition of Alfred Marshall’s famous textbook, Principles of Economics, the economist put the following on the title page:

Natura non facit saltum

Darwin himself used the same phrase—which means “nature does not make leaps”—in his book, The Origin of Species.  Although Marshall never explained his thinking explicitly, it seems Marshall meant to align his work with Darwinian thinking.

Less than two decades later, Austrian-born economist Joseph Schumpeter put forth his central idea of creative destruction.  Hagstrom quotes British economist Christopher Freeman, who—after studying Schumpeter’s life—remarked:

The central point of his whole life work is that capitalism can only be understood as an evolutionary process of continuous innovation and creative destruction.

Hagstrom explains:

Innovation, said Schumpeter, is the profitable application of new ideas, including products, production processes, supply sources, new markets, or new ways in which a company could be organized.  Whereas standard economic theory believed progress was a series of small incremental steps, Schumpeter’s theory stressed innovative leaps, which in turn caused massive disruption and discontinuity—an idea captured in Schumpeter’s famous phrase “the perennial gale of creative destruction.”

But all these innovative possibilities meant nothing without the entrepreneur who becomes the visionary leader of innovation.  It takes someone exceptional, said Schumpeter, to overcome the natural obstacles and resistance to innovation.  Without the entrepreneur’s desire and willingness to press forward, many great ideas could never be launched.

(Image from the Department of Economics, University of Freiburg, via Wikimedia Commons)

Moreover, Schumpeter held that entrepreneurs can thrive only in certain environments.  Property rights, a stable currency, and free trade are important.  And credit is even more important.

In the fall of 1987, a group of physicists, biologists, and economists held a conference at the Santa Fe Institute.  The economist Brian Arthur gave a presentation on “New Economics.”  A central idea was to apply the concept of complex adaptive systems to the science of economics.  Hagstrom records that the Santa Fe group isolated four features of the economy:

Dispersed interaction:  What happens in the economy is determined by the interactions of a great number of individual agents all acting in parallel.  The action of any one individual agent depends on the anticipated actions of a limited number of agents as well as on the system they cocreate.

No global controller:  Although there are laws and institutions, there is no one global entity that controls the economy.  Rather, the system is controlled by the competition and coordination between agents of the system.

Continual adaptation:  The behavior, actions, and strategies of agents, as well as their products and services, are revised continually on the basis of accumulated experience.  In other words, the system adapts.  It creates new products, new markets, new institutions, and new behavior.  It is an ongoing system.

Out-of-equilibrium dynamics:  Unlike the equilibrium models that dominate the thinking in classical economics, the Santa Fe group believed the economy, because of constant change, operates far from equilibrium.

Hagstrom argues that different investment or trading strategies throughout history have competed against one another.  Those that have most accurately predicted the future for various businesses and their associated stock prices have survived, while less profitable strategies have disappeared.

But in any given time period, once a specific strategy becomes profitable, then more money flows into it, which eventually makes it less profitable.  New strategies are then invented and compete against one another.  As a result, a new strategy becomes dominant and then the process repeats.

Thus, economies and markets evolve over time.  There is no stable equilibrium in a market except in the short term.  To go from the language of biology to the language of business, Hagstrom refers to three important books:

  • Creative Destruction: Why Companies That Are Built to Last Underperform the Market—and How to Successfully Transform Them, by Richard Foster and Sarah Kaplan of McKinsey & Company
  • The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, by Clayton Christensen
  • The Innovator’s Solution: Creating and Sustaining Successful Growth, by Clayton Christensen and Michael Raynor

Hagstrom sums up the lessons from biology as compared to the previous ideas from physics:

Indeed, the movement from the mechanical view of the world to the biological view of the world has been called the “second scientific revolution.”  After three hundred years, the Newtonian world, the mechanized world operating in perfect equilibrium, is now the old science.  The old science is about a universe of individual parts, rigid laws, and simple forces.  The systems are linear:  Change is proportional to the inputs.  Small changes end in small results, and large changes make for large results.  In the old science, the systems are predictable.

The new science is connected and entangled.  In the new science, the system is nonlinear and unpredictable, with sudden and abrupt changes.  Small changes can have large effects while large events may result in small changes.  In nonlinear systems, the individual parts interact and exhibit feedback effects that may alter behavior.  Complex adaptive systems must be studied as a whole, not in individual parts, because the behavior of the system is greater than the sum of the parts.

The old science was concerned with understanding the laws of being.  The new science is concerned with the laws of becoming.

(Photo by Isabellebonaire)

Hagstrom then quotes the last passage from Darwin’s The Origin of Species:

It is interesting to contemplate an entangled bank, clothed with many plants of many kinds, with birds singing on the bushes, with various insects flitting about, and with worms crawling through the damp earth, and to reflect that these elaborately constructed forms, so different from each other, and dependent on each other in so complex a manner, have all been produced by laws acting around us.  These laws, taken in the largest sense, being Growth with Reproduction; Inheritance which is almost implied by reproduction; Variability from the indirect and direct action of the external conditions of life, and from use and disuse; a Ratio of Increase so high as to lead to a Struggle for Life, and as a consequence to Natural Selection, entailing divergence of Character and Extinction of less improved forms.  Thus, from the war of nature, from famine and death, the most exalted object which we are capable of conceiving, namely, the production of higher animals, directly follows.  There is grandeur in this view of life, with its several powers, having been originally breathed into a few forms or into one; and that, whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved.

 

SOCIOLOGY

Because significant increases in computer power are now making vast amounts of data about human behavior available, the social sciences may at some point get enough data to figure out more precisely and more generally the laws of human behavior.  But we’re not there yet.

(Auguste Comte, via Wikimedia Commons)

The nineteenth century—despite the French philosopher Auguste Comte’s efforts to establish one unified social science—ended with several distinct specialties, says Hagstrom, including economics, political science, and anthropology.

Scottish economist Adam Smith published his Wealth of Nations in 1776.  Smith argued for what is now called laissez-faire capitalism, or a system free from government interference, including industry regulation and protective tariffs.  Smith also held that a division of labor, with individuals specializing in various tasks, led to increased productivity.  This meant more goods at lower prices for consumers, but it also meant more wealth for the owners of capital.  And it implied that the owners of capital would try to limit the wages of labor.  Furthermore, working conditions would likely be bad without government regulation.

Predictably, political scientists appeared on the scene to study how the government should protect the rights of workers in a democracy.  Also, the property rights of owners of capital had to be protected.

Social psychologists studied how culture affects psychology, and how the collective mind affects culture.  Social biologists, meanwhile, sought to apply biology to the study of society, notes Hagstrom.  Recently scientists, including Edward O. Wilson, have introduced sociobiology, which involves the attempt to apply the scientific principles of biology to social development.

Hagstrom writes:

Although the idea of a unified theory of social science faded in the late nineteenth century, here at the beginning of the twenty-first century, there has been a growing interest in what we might think of as a new unified approach.  Scientists have now begun to study the behavior of whole systems—not only the behavior of individuals and groups but the interactions between them and the ways in which this interaction may in turn influence subsequent behavior.  Because of this reciprocal influence, our social system is constantly engaged in a socialization process the consequence of which not only alters our individual behavior but often leads to unexpected group behavior.

To explain the formation of a social group, the theory of self-organization has been developed.  Ilya Prigogine, the Russian chemist, was awarded the Nobel Prize in 1977 for his thermodynamic concept of self-organization.

Paul Krugman, winner of the 2008 Nobel Prize for Economics, studied self-organization as applied to the economy.  Hagstrom:

Setting aside for the moment the occasional recessions and recoveries caused by exogenous events such as oil shocks or military conflicts, Krugman believes that economic cycles are in large part caused by self-reinforcing effects.  During a prosperous period, a self-reinforcing process leads to greater construction and manufacturing until the return on investment begins to decline, at which point an economic slump begins.  The slump in itself becomes a self-reinforcing effect, leading to lower production; lower production, in turn, will eventually cause return on investment to increase, which starts the process all over again.

Hagstrom notes that equity and debt markets are good examples of self-organizing, self-reinforcing systems.

If self-organization is the first characteristic of complex adaptive systems, then emergence is the second characteristic.  Hagstrom says that emergence refers to the way individual units—whether cells, neurons, or consumers—combine to create something greater than the sum of the parts.

(Collective Dynamics of Complex Systems, by Dr. Hiroki Sayama, via Wikimedia Commons)

One fascinating aspect of human collectives is that, in many circumstances—like finding the shortest way through a maze—the collective solution is better when there are both smart and not-so-smart individuals in the collective.  This more diverse collective outperforms a group that is composed only of smart individuals.

This implies that the stock market may more accurately aggregate information when the participants include many different types of people, such as smart and not-so-smart, long-term and short-term, and so forth, observes Hagstrom.

There are many areas where a group of people is actually smarter than the smartest individual in the group.  Hagstrom mentions that Francis Galton, the English Victorian-era polymath, wrote about a contest in which 787 people guessed at the weight of a large ox.  Most participants in the contest were not experts by any means, but ordinary people.  The ox actually weighed 1,198 pounds.  The average guess of the 787 guessers was 1,197 pounds, which was more accurate than the guesses made by the smartest and the most expert guessers.

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

In order for the collective to be that smart, the members must be diverse and the members’ guesses must be independent from one another.  So the stock market is efficient when these two conditions are satisfied.  But if there is a breakdown in diversity, or if individuals start copying one another too much—what Michael Mauboussin calls an information cascade—then you could have a boom, fad, fashion, or crash.

There are some situations where an individual can be impacted by the group.  Solomon Asch did a famous experiment in which the subject is supposed to match lines that have the same length.  It’s an easy question that every subject—if left alone—gets right.  But then Asch has seven out of eight participants deliberately choose the wrong answer, unbeknownst to the subject of the experiment, who is the eighth participant in the same room.  When this experiment was repeated many times, roughly one-third of the subjects gave the same answer as the group, even though this answer is obviously wrong.  Such can be the power of a group opinion.

Hagstrom asks about how crashes can happen.  Danish theoretical physicist Per Bak developed the notion of self-organized criticality.

According to Bak, large complex systems composed of millions of interacting parts can break down not only because of a single catastrophic event but also because of a chain reaction of smaller events.  To illustrate the concept of self-criticality, Bak often used the metaphor of a sand pile… Each grain of sand is interlocked in countless combinations.  When the pile has reached its highest level, we can say the sand is in a state of criticality.  It is just on the verge of becoming unstable.

(Computer Simulation of Bak-Tang-Weisenfeld sandpile, with 28 million grains, by Claudio Rocchini, via Wikimedia Commons)

Adding one more grain starts an avalanche.  Bak and two colleagues applied this concept to the stock market.  They assumed that there are two types of agents, rational agents and noise traders.  Most of the time, the market is well-balanced.

But as stock prices climb, rational agents sell and leave the market, while more noise traders following the trend join.  When noise traders—trend followers—far outnumber rational agents, a bubble can form in the stock market.

 

PSYCHOLOGY

The psychologists Daniel Kahneman and Amos Tversky did research together for over two decades.  Kahneman was awarded the Nobel Prize in Economics in 2002.  Tversky would also have been named had he not passed away.

(Daniel Kahneman, via Wikimedia Commons)

Much of their groundbreaking research is contained in Judgment Under Uncertainty: Heuristics and Biases (1982).

Here you will find all the customary behavioral finance terms we have come to know and understand:  anchoring, framing, mental accounting, overconfidence, and overreaction bias.  But perhaps the most significant insight into individual behavior was loss aversion.

Kahneman and Tversky discovered that how choices are framed—combined with loss aversion—can materially impact how people make decisions.  For instance, in one of their well-known experiments, they asked people to choose between the following two options:

  • (a) Save 200 lives for sure.
  • (b) Have a one-third chance of saving 600 lives and a two-thirds chance of saving no one.

In this scenario, people overwhelmingly chose (a)—to save 200 lives for sure.  Kahneman and Tversky next asked the same people to choose between the following two options:

  • (a) Have 400 people die for sure.
  • (b) Have a two-thirds chance of 600 people dying and a one-third chance of no one dying.

In this scenario, people preferred (b)—a two-thirds chance of 600 people dying, and a one-third chance of no one dying.

But the two versions of the problem are identical.  The number of people saved in the first version equals the number of people who won’t die in the second version.

What Kahneman and Tversky had demonstrated is that people are risk averse when considering potential gains, but risk seeking when facing the possibility of a certain loss.  This is the essence of prospect theory, which is captured in the following graph:

(Value function in Prospect Theory, drawing by Marc Rieger, via Wikimedia Commons)

Loss aversion refers to the fact that people weigh a potential loss about 2.5 times more than an equivalent gain.  That’s why the value function in the graph is steeper for losses.

Richard Thaler and Shlomo Benartzi researched loss aversion by hypothesizing that the less frequently an investor checks the price of a stock he or she owns, the less likely the investor will be to sell the stock because of temporary downward volatility.  Thaler and Benartzi invented the term myopic loss aversion.

Hagstrom writes:

In my opinion, the single greatest psychological obstacle that prevents investors from doing well in the stock market is myopic loss aversion.  In my twenty-eight years in the investment business, I have observed firsthand the difficulty investors, portfolio managers, consultants, and committee members of large institutional funds have with internalizing losses (loss aversion), made all the more painful by tabulating losses on a frequent basis (myopic loss aversion).  Overcoming this emotional burden penalizes all but a very few select individuals.

Perhaps it is not surprising that the one individual who has mastered myopic loss aversion is also the world’s greatest investor—Warren Buffett…

Buffett understands that as long as the earnings of the businesses you own move higher over time, there’s no reason to worry about shorter term stock price volatility.  Because Berkshire Hathaway, Buffett’s investment vehicle, holds both public stocks and wholly owned private businesses, Buffett’s long-term outlook has been reinforced.  Hagstrom quotes Buffett:

I don’t need a stock price to tell me what I already know about value.

Hagstrom mentions Berkshire’s investment in The Coca-Cola Company (KO), in 1988.  Berkshire invested $1 billion, which was at that time the single largest investment Berkshire had ever made.  Over the ensuing decade, KO stock went up ten times, while the S&P 500 Index only went up three times.  But four out of those ten years, KO stock underperformed the market.  Trailing the market 40 percent of the time didn’t bother Buffett a bit.

As Hagstrom observes, Benjamin Graham—the father of value investing, and Buffett’s teacher and mentor—made a distinction between the investor focused on long-term business value and the speculator who tries to predict stock prices in the shorter term.  The true investor should never be concerned with shorter term stock price volatility.

(Ben Graham, Photo by Equim43, via Wikimedia Commons)

Hagstrom quotes Graham’s The Intelligent Investor:

The investor who permits himself to be stampeded or unduly worried by unjustified market declines in his holdings is perversely transforming his basic advantage into a basic disadvantage.  That man would be better off if his stocks had no market quotation at all, for he would then be spared the mental anguish caused him by another person’s mistakes of judgment.

Terence Odean, a behavioral economist, has done extensive research on the investment decisions of individuals and households.  Odean discovered that:

  • Many investors trade often—Odean found a 78 percent portfolio turnover ratio in his first study, which tracked 97,483 trades from ten thousand randomly selected accounts.
  • Over the subsequent 4 months, one year, and two years, the stocks that investors bought consistently trailed the market, while the stocks that investors sold beat the market.

Hagstrom mentions that people use mental models as a basis for understanding reality and making decisions.  But we tend to assume that each mental model we have is equally probable, rather than working to assign different probabilities to different models.

Moreover, people typically can make models for what something is—or what is true—instead of what something is not—or what is false.  Also, our mental models are usually quite incomplete.  And we tend to forget details of our models, especially after time passes.  Finally, writes Hagstrom, people tend to construct mental models based on superstition or unwarranted belief.

Hagstrom asks the question: Why do people tend to be so gullible in general?  For instance, while there’s no evidence that market forecasts have any value, many otherwise intelligent people pay attention to them and even make decisions based on them.

The answer, states Hagstrom, is that we are wired to seek and to find patterns.  We have two basic mental systems, System 1 (intuition) and System 2 (reason).  System 1 operates automatically.  It takes mental shortcuts which often work fine, but not always.  System 1 is designed to find patterns.  And System 1 seeks confirming evidence for its hypotheses (patterns).

But even System 2—which humans can use to do math, logic, and statistics—uses a positive test strategy, meaning that it seeks confirming evidence for its hypotheses (patterns), rather than disconfirming evidence.

 

PHILOSOPHY

Hagstrom introduces the chapter:

A true philosopher is filled with a passion to understand, a process that never ends.

(Socrates, J. Aars Platon (1882), via Wikimedia Commons)

Metaphysics is one area of philosophy.  Aesthetics, ethics, and politics are other areas.  But Hagstrom focuses his discussion of philosophy on epistemology, the study of knowledge.

Having spent a few years studying the history and philosophy of science, I would say that epistemology includes the following questions:

  • What different kinds of knowledge can we have?
  • What constitutes scientific knowledge?
  • Is any part of our knowledge certain, or can all knowledge be improved indefinitely?
  • How does scientific progress happen?

In a sense, epistemology is thinking about thinking.  Epistemology is also studying the history of science in great detail, because humans have made enormous progress in generating scientific knowledge.

Studying epistemology can help us to become better, more rigorous, and more coherent thinkers, which can make us better investors.

Hagstrom makes it clear in the Preface that his book is necessarily abbreviated, otherwise it would have been a thousand pages long.  That said, had he been aware of Willard Van Orman Quine’s epistemology, Hagstrom likely would have mentioned it.

Here is a passage from Quine’s From A Logical Point of View:

The totality of our so-called knowledge or beliefs, from the most casual matters of geography and history to the profoundest laws of atomic physics or even of pure mathematics and logic, is a man-made fabric which impinges on experience only along the edges.  Or, to change the figure, total science is like a field of force whose boundary conditions are experience.  A conflict with experience at the periphery occasions readjustments in the interior of the field.  Truth values have to be redistributed over some of our statements.  Re-evaluation of some statements entails re-evaluation of others, because of their logical interconnections—the logical laws being in turn simply certain further statements of the system, certain further elements of the field.  Having re-evaluated one statement we must re-evaluate some others, which may be statements logically connected with the first or may be the statements of logical connections themselves.  But the total field is so underdetermined by its boundary conditions, experience, that there is much latitude of choice as to what statements to re-evaluate in the light of any single contrary experience.  No particular experiences are linked with any particular statements in the interior of the field, except indirectly through considerations of equilibrium affecting the field as a whole.

If this view is right, it is misleading to speak of the empirical content of an individual statement—especially if it is a statement at all remote from the experiential periphery of the field.  Furthermore it becomes folly to seek a boundary between synthetic statements, which hold contingently on experience, and analytic statements, which hold come what may.  Any statement can be held true come what may, if we make drastic enough adjustments elsewhere in the system.  Even a statement very close to the periphery can be held true in the face of recalcitrant experience by pleading hallucination or by amending certain statements of the kind called logical laws.  Conversely, by the same token, no statement is immune to revision.  Revision even of the logical law of the excluded middle has been proposed as a means of simplifying quantum mechanics…

(Image by Dmytro Tolokonov)

Quine continues:

For vividness I have been speaking in terms of varying distances from a sensory periphery.  Let me now try to clarify this notion without metaphor.  Certain statements, though about physical objects and not sense experience, seem peculiarly germane to sense experience—and in a selective way: some statements to some experiences, others to others.  Such statements, especially germane to particular experiences, I picture as near the periphery.  But in this relation of “germaneness” I envisage nothing more than a loose association reflecting the relative likelihood, in practice, of our choosing one statement rather than another for revision in the event of recalcitrant experience.  For example, we can imagine recalcitrant experiences to which we would surely be inclined to accomodate our system by re-evaluating just the statement that there are brick houses on Elm Street, together with related statements on the same topic.  We can imagine other recalcitrant experiences to which we would be inclined to accomodate our system by re-evaluating just the statement that there are no centaurs, along with kindred statements.  A recalcitrant experience can, I have urged, be accomodated by any of various alternative re-evaluations in various alternative quarters of the total system; but, in the cases which we are now imagining, our natural tendency to disturb the total system as little as possible would lead us to focus our revisions upon these specific statements concerning brick houses or centaurs.  These statements are felt, therefore, to have a sharper empirical reference than highly theoretical statements of physics or logic or ontology.  The latter statements may be thought of as relatively centrally located within the total network, meaning merely that little preferential connection with any particular sense data obtrudes itself.

As an empiricist, I continue to think of the conceptual scheme of science as a tool, ultimately, for predicting future experience in the light of past experience.  Physical objects are conceptually imported into the situation as convenient intermediaries—not by definition in terms of experience, but simply as irreducible posits comparable, epistemologically, to the gods of Homer.  For my part I do, qua lay physicist, believe in physical objects and not in Homer’s gods; and I consider it a scientific error to believe otherwise.  But in point of epistemological footing the physical objects and the gods differ only in degree and not in kind.  Both sorts of entities enter our conception only as cultural posits.  The myth of physical objects is epistemologically superior to most in that it has proved more efficacious than other myths as a device for working a manageable structure into the flux of experience.

Physical objects, small and large, are not the only posits.  Forces are another example; and indeed we are told nowadays that the boundary between energy and matter is obsolete.  Moreover, the abstract entities which are the substance of mathematics—ultimately classes and classes of classes and so on up—are another posit in the same spirit.  Epistemologically these are posits on the same footing with physical objects and gods, neither better nor worse except for differences in the degree to which they expedite our dealings with sense experiences.

Historically, philosophers distinguished between “analytic” statements, which were thought to be true by definition, and “synthetic” statements, which were thought to be true on the basis of certain empirical data or experiences.  One of Quine’s chief points is that this distinction doesn’t hold.

Mathematics, logic, scientific theories, scientific laws, working hypotheses, ordinary language, and much else including simple observations, are all a part of science.  The goal of science—which extends common sense—is to predict various future experiences—including experiments—on the basis of past experiences.

When predictions—including experiments—don’t turn out as expected, then any part of the totality of science is revisable.  Often it makes sense to revise specific hypotheses, or specific statements that are close to experience.  But sometimes highly theoretical statements or ideas—including the laws of mathematics, the laws of logic, and the most well-established scientific laws—are revised in order to make the overall system of science work better, i.e., predict phenomena (future experiences) better, with more generality or with more exactitude.

The chief way scientists have made—and continue to make—progress is by testing predictions that are implied by existing scientific theory or law, or that are implied by new hypotheses under consideration.

(Top quark and anti top quark pair decaying into jets, Collider Detector at Fermilab, via Wikimedia Commons)

Because of recent advances in computing power and because of the explosion of shared knowledge, ideas, and experiments on the internet, scientific progress is probably happening much faster than ever before.  It’s a truly exciting time for all curious people and scientists.  And once artificial intelligence passes the singularity threshold, scientific progress is likely to skyrocket, even beyond what we can imagine.

 

LITERATURE

Critical reading is a crucial part of becoming a better thinker.

(Photo by VijayGES2, via Wikimedia Commons)

One excellent book about how to read analytically is How to Read a Book, by Mortimer J. Adler.  The goal of analytical reading is to improve your understanding—as opposed to only gaining information.  To this end, Adler suggests active readers keep the following four questions in mind:

  • What is the book about as a whole?
  • What is being said in detail?
  • Is the book true, in whole or part?
  • What of it?

Before deciding to read a book in detail, it can be helpful to read the preface, table of contents, index, and bibliography.  Also, read a few paragraphs at random.  These steps will help you to get a sense of what the book is about as a whole.  Next, you can skim the book to learn more about what is being said in detail, and whether it’s worth reading the entire book carefully.

Then, if you decide to read the entire book carefully, you should approach it like you would approach assigned reading for a university class.  Figure out the main points and arguments.  Take notes if that helps you learn.  The goal is to understand the author’s chief arguments, and whether—or to what extent—those arguments are true.

The final step is comparative reading, says Hagstrom.  Adler considers this the hardest step.  Here the goal is to learn about a specific subject.  You want to determine which books on the subject are worth reading, and then compare and contrast these books.

Hagstrom points out that the three greatest detectives in fiction are Auguste Dupin, Sherlock Holmes, and Father Brown.  We can learn much from studying the stories involving these sleuths.

Auguste Dupin was created by Edgar Allan Poe.  Hagstrom remarks that we can learn the following from Dupin’s methods:

  • Develop a skeptic’s mindset; don’t automatically accept conventional wisdom.
  • Conduct a thorough investigation.

Sherlock Holmes was created by Sir Arthur Conan Doyle.

(Illustration by Sidney Paget (1891), via Wikimedia Commons)

From Holmes, we can learn the following, says Hagstrom:

  • Begin an investigation with an objective and unemotional viewpoint.
  • Pay attention to the tiniest details.
  • Remain open-minded to new, even contrary, information.
  • Apply a process of logical reasoning to all you learn.

Father Brown was created by G. K. Chesterton.  From Father Brown, we can learn:

  • Become a student of psychology.
  • Have faith in your intuition.
  • Seek alternative explanations and redescriptions.

Hagstrom ends the chapter by quoting Charlie Munger:

I believe in… mastering the best that other people have figured out [rather than] sitting down and trying to dream it up yourself… You won’t find it that hard if you go at it Darwinlike, step by step with curious persistence.  You’ll be amazed at how good you can get… It’s a huge mistake not to absorb elementary worldly wisdom… Your life will be enriched—not only financially but in a host of other ways—if you do.

 

MATHEMATICS

Hagstrom quotes Warren Buffett:

…the formula for valuing ALL assets that are purchased for financial gain has been unchanged since it was first laid out by a very smart man in about 600 B.C.E.  The oracle was Aesop and his enduring, though somewhat incomplete, insight was “a bird in the hand is worth two in the bush.”  To flesh out this principle, you must answer only three questions.  How certain are you that there are indeed birds in the bush?  When will they emerge and how many will there be?  What is the risk-free interest rate?  If you can answer these three questions, you will know the maximum value of the bush—and the maximum number of birds you now possess that should be offered for it.  And, of course, don’t literally think birds.  Think dollars.

Hagstrom explains that it’s the same formula whether you’re evaluating stocks, bonds, manufacturing plants, farms, oil royalties, or lottery tickets.  As long as you have the numbers needed for the calculation, the attractiveness of all investment opportunities can be evaluated and compared.

So to value any business, you have to estimate the future cash flows of the business, and then discount those cash flows back to the present.  This is the DCF—discounted cash flows—method for determining the value of a business.

Although Aesop gave the general idea, John Burr Williams, in The Theory of Investment Value (1938), was the first to explain the DCF approach explicitly.  Williams had studied mathematics and chemistry as an undergraduate at Harvard University.  After working as a securities analyst, Williams returned to Harvard to get a PhD in economics.  The Theory of Investment Value was Williams’ dissertation.

Hagstrom writes that in 1654, the Chevalier de Méré, a French nobleman who liked to gamble, asked the mathematician Blaise Pascal the following question: “How do you divide the stakes of an unfinished game of chance when one of the players is ahead?”

(Photo by Rossapicci, via Wikimedia Commons)

Pascal was a child prodigy and a brilliant mathematician.  To help answer de Méré’s question, Pascal turned to Pierre de Fermat, a lawyer who was also a brilliant mathematician.  Hagstrom reports that Pascal and Fermat exchanged a series of letters which are the foundation of what is now called probability theory.

There are two broad categories of probabilities:

  • frequency probability
  • subjective probability

A frequency probability typically refers to a system that can generate a great deal of statistical data over time.  Examples include coin flips, roulette wheels, cards, and dice, notes Hagstrom.  For instance, if you flip a coin 1,000 times, you expect to get heads about 50 percent of the time.  If you roll one 6-sided dice 1,000 times, you expect to get each number about 16.67 percent of the time.

If you don’t have a sufficient frequency of events, plus a long time period to analyze results, then you must rely on a subjective probability.  A subjective probability, says Hagstrom, is often a reasonable assessment made by a knowledgeable person.  It’s a best guess based a logical analysis of the given data.

When using a subjective probability, obviously you want to make sure you have all the available data that could be relevant.  And clearly you have to use logic correctly.

But the key to using a subjective probability is to update your beliefs as you gain new data.  The proper way to update your beliefs is by using Bayes’ Rule.

(Thomas Bayes, via Wikimedia Commons)

Bayes’ Rule

Eliezer Yudkowsky of the Machine Intelligence Research Institute provides an excellent intuitive explanation of Bayes’ Rule:  http://www.yudkowsky.net/rational/bayes

Yudkowsky begins by discussing a situation that doctors often encounter:

1% of women at age forty who participate in routine screening have breast cancer.  80% of women with breast cancer will get positive mammographies.  9.6% of women without breast cancer will also get positive mammographies.  A woman in this age group had a positive mammography in a routine screening.  What is the probability that she actually has breast cancer?

Most doctors estimate the probability between 70% and 80%, which is wildly incorrect.

In order to arrive at the correct answer, Yudkowsky asks us to think of the question as follows.  We know that 1% of women at age forty who participate in routine screening have breast cancer.  So consider 10,000 women who participate in routine screening:

  • Group 1: 100 women with breast cancer.
  • Group 2: 9,900 women without breast cancer.

After the mammography, the women can be divided into four groups:

  • Group A:  80 women with breast cancer, and a positive mammography.
  • Group B:  20 women with breast cancer, and a negative mammography.
  • Group C:  950 women without breast cancer, and a positive mammography.
  • Group D:  8,950 women without breast cancer, and a negative mammography.

So the question again:  If a woman out of this group of 10,000 women has a positive mammography, what is the probability that she actually has breast cancer?

The total number of women who had positive mammographies is 80 + 950 = 1,030.  Of that total, 80 women had positive mammographies AND have breast cancer.  In looking at the total number of positive mammographies (1,030), we know that 80 of them actually have breast cancer.

So if a woman out of the 10,000 has a positive mammography, the chances that she actually has breast cancer = 80/1030  or 0.07767 or 7.8%.

That’s the intuitive explanation.  Now let’s look at Bayes’ Rule:

P(A|B) = [P(B|A) P(A)] / P(B)

Let’s apply Bayes’ Rule to the same question above:

1% of women at age forty who participate in routine screening have breast cancer.  80% of women with breast cancer will get positive mammographies.  9.6% of women without breast cancer will also get positive mammographies.  A woman in this age group had a positive mammography in a routine screening.  What is the probability that she actually has breast cancer?

P(A|B) = the probability that the woman has breast cancer (A), given a positive mammography (B)

Here is what we know:

P(B|A) = 80% – the probability of a positive mammography (B), given that the woman has breast cancer (A)

P(A) = 1% – the probability that a woman out of the 10,000 screened actually has breast cancer

P(B) = (80+950) / 10,000 = 10.3% – the probability that a woman out of the 10,000 screened has a positive mammography

Bayes’ Rule again:

P(A|B) = [P(B|A) P(A)] / P(B)

P(A|B) = [0.80*0.01] / 0.103 = 0.008 / 0.103 = 0.07767 or 7.8%

Derivation of Bayes’ Rule:

Bayesians consider conditional probabilities as more basic than joint probabilities.  You can define P(A|B) without reference to the joint probability P(A,B).  To see this, first start with the conditional probability formula:

P(A|B) P(B) = P(A,B)

but by symmetry you get:

P(B|A) P(A) = P(A,B)

It follows that:

P(A|B) = [P(B|A) P(A)] / P(B)

which is Bayes’ Rule.

In conclusion, Hagstrom makes the important observation that there is much we still don’t know about nature and about ourselves.  (The question mark below is by Khaydock, via Wikimedia Commons.)

Nothing is absolutely certain.

One clear lesson from history—whether the history of investing, the history of science, or some other area—is that very often people who are “absolutely certain” about something turn out to be wrong.

Economist and Nobel laureate Kenneth Arrow:
  • Our knowledge of the way things work, in society or in nature, comes trailing clouds of vagueness.  Vast ills have followed a belief in certainty.

Investor and author Peter Bernstein:

The recognition of risk management as a practical art rests on a simple cliché with the most profound consequences:  when our world was created, nobody remembered to include certainty.  We are never certain;  we are always ignorant to some degree.  Much of the information we have is either incorrect or incomplete.

 

DECISION MAKING

Take a few minutes and try answering these three problems:

  • A bat and a ball cost $1.10.  The bat costs one dollar more than the ball.  How much does the ball cost?
  • If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?
  • In a lake, there is a patch of lily pads.  Every day the patch doubles in size.  If it takes 48 days for the patch to cover the entire lake, how long will it take for the patch to cover half the lake?

Roughly 75 percent of the Princeton and Harvard students got at least one problem wrong.  These questions form the Cognitive Reflection Test, invented by Shane Frederick, an assistant professor of management science at MIT.

Recall that System 1 (intuition) is quick, associative, and operates automatically all the time.  System 2 (reason) is slow and effortful—it requires conscious activation and sustained focus—and it can learn to solve problems involving math, statistics, or logic.

To understand the mental mistake that many people—including smart people—make, let’s consider the first of the three questions:

  • A bat and a ball cost $1.10.  The bat costs one dollar more than the ball.  How much does the ball cost?

After we read the question, our System 1 (intuition) immediately suggests to us that the bat costs $1.00 and the ball costs 10 cents.  But if we slow down just a moment and engage System 2, we realize that if the bat costs $1.00 and the ball costs 10 cents, then the bat costs only 90 cents more than the ball.  This violates the condition stated in the problem that the bat costs one dollar more than the ball.  If we think a bit more, we see that the bat must cost $1.05 and the ball must cost 5 cents.

System 1 takes mental shortcuts, which often work fine.  But when we encounter a problem that requires math, statistics, or logic, we have to train ourselves to slow down and to think through the problem.  If we don’t slow down in these situations, we’ll often jump to the wrong conclusion.

(Cognitive Bias Codex, by John Manoogian III, via Wikimedia Commons.  For a closer look, try this link: https://upload.wikimedia.org/wikipedia/commons/1/18/Cognitive_Bias_Codex_-_180%2B_biases%2C_designed_by_John_Manoogian_III_%28jm3%29.jpg)

It’s possible to train your intuition under certain conditions, according to Daniel Kahneman.  Hagstrom:

Kahneman believes there are indeed cases where intuitive skill reveals the answer, but that such cases are dependent on two conditions.  First, “the environment must be sufficiently regular to be predictable” second, there must be an “opportunity to learn these regularities through prolonged practice.”  For familiar examples, think about the games of chess, bridge, and poker.  They all occur in regular environments, and prolonged practice at them helps people develop intuitive skill.  Kahneman also accepts the idea that army officers, firefighters, physicians, and nurses can develop skilled intuition largely because they all have had extensive experience in situations that, while obviously dramatic, have been repeated many times over.

Kahneman concludes that intuitive skill exists mostly in people who operate in simple, predictable environments and that people in more complex environments are much less likely to develop this skill.  Kahneman, who has spent much of his career studying clinicians, stock pickers, and economists, notes that evidence of intuitive skill is largely absent in this group.  Put differently, intuition appears to work well in linear systems where cause and effect is easy to identify.  But in nonlinear systems, including stock markets and economies, System 1 thinking, the intuitive side of our brain, is much less effectual.

Experts in fields such as investing, economics, and politics have, in general, not demonstrated the ability to make accurate forecasts or predictions with any reliable consistency.

Philip Tetlock, professor of psychology at the University of Pennsylvania, tracked 284 experts over fifteen years (1988-2003) as they made 27,450 forecasts.  The results are no better than “dart-throwing chimpanzees,” as Tetlock describes in Expert Political Judgment: How Good Is It? How Can We Know? (Princeton University Press, 2005).

Hagstrom explains:

It appears experts are penalized, like the rest of us, by thinking deficiencies.  Specifically, experts suffer from overconfidence, hindsight bias, belief system defenses, and lack of Bayesian process.

Hagstrom then refers to an essay by Sir Isaiah Berlin, “The Hedgehog and the Fox: An Essay on Tolstoy’s View of History.”  Hedgehogs view the world using one large idea, while Foxes are skeptical of grand theories and instead consider a variety of information and experiences before making decisions.

(Photo of Hedgehog, by Nevit Dilmen, via Wikimedia Commons)

Tetlock found that Foxes, on the whole, were much more accurate than Hedgehogs.  Hagstrom:

Why are hedgehogs penalized?  First, because they have a tendency to fall in love with pet theories, which gives them too much confidence in forecasting events.  More troubling, hedgehogs were too slow to change their viewpoint in response to discomfirming evidence.  In his study, Tetlock said Foxes moved 59 percent of the prescribed amount toward alternate hypotheses, while Hedgehogs moved only 19 percent.  In other words, Foxes were much better at updating their Bayesian inferences than Hedgehogs.

Unlike Hedgehogs, Foxes appreciate the limits of their knowledge.  They have better calibration and discrimination scores than Hedgehogs.  (Calibration, which can be thought of as intellectual humility, measures how much your subjective probabilities correspond to objective probabilities.  Discrimination, sometimes called justified decisiveness, measures whether you assign higher probabilities to things that occur than to things that do not.)

(Photo of Fox, by Alan D. Wilson, via Wikimedia Commons)

Hagstrom comments that Foxes have three distinct cognitive advantages, according to Tetlock:

  • They begin with “reasonable starter” probability estimates.  They have better “inertial-guidance” systems that keep their initial guesses closer to short-term base rates.
  • They are willing to acknowledge their mistakes and update their views in response to new information.  They have a healthy Bayesian process.
  • They can see the pull of contradictory forces, and, most importantly, they can appreciate relevant analogies.

Hagstrom concludes that the Fox “is the perfect mascot for The College of Liberal Arts Investing.”

Many people with high IQ have difficulty making rational decisions.  Keith Stanovich, professor of applied psychology at the University of Toronto, invented the term dysrationalia to refer to the inability to think and behave rationally despite high intelligence, remarks Hagstrom.  There are two principal causes of dysrationalia:

  • a processing problem
  • a content problem

Stanovich explains that people are lazy thinkers in general, preferring to think in ways that require less effort, even if those methods are less accurate.  As we’ve seen, System 1 operates automatically, with little or no effort.  Its conclusions are often correct.  But when the situation calls for careful reasoning, the shortcuts of System 1 don’t work.

Lack of adequate content is a mindware gap, says Hagstrom.  Mindware refers to rules, strategies, procedures, and knowledge that people possess to help solve problems.  Harvard cognitive scientist David Perkins coined the term mindware.  Hagstrom quotes Perkins:

What is missing is the metacurriculum—the ‘higher order’ curriculum that deals with good patterns of thinking in general and across subject matters.

Perkins’ proposed solution is a mindware booster shot, which means teaching new concepts and ideas in “a deep and far-reaching way,” connected with several disciplines.

Of course, Hagstrom’s book, Investing: The Last Liberal Art, is a great example of a mindware booster shot.

 

Hagstrom concludes by stressing the vital importance of lifelong, continuous learning.  Buffett and Munger have always highlighted this as a key to their success.

(Statue of Ben Franklin in front of College Hall, Philadelphia, Pennsylvania, Photo by Matthew Marcucci, via Wikimedia Commons)

Hagstrom:

Although the greatest number of ants in a colony will follow the most intense pheromone trail to a food source, there are always some ants that are randomly seeking the next food source.  When Native Americans were sent out to hunt, most of those in the party would return to the proven hunting grounds.  However, a few hunters, directed by a medicine man rolling spirit bones, were sent in different directions to find new herds.  The same was true of Norwegian fishermen.  Each day most of the ships in the fleet returned to the same spot where the previous day’s catch had yielded the greatest bounty, but a few vessels were also sent in random directions to locate the next school of fish.  As investors, we too must strike a balance between exploiting what is most obvious while allocating some mental energy to exploring new possibilities.

Hagstrom adds:

The process is similar to genetic crossover that occurs in biological evolution.  Indeed, biologists agree that genetic crossover is chiefly responsible for evolution.  Similarly, the constant recombination of our existing mental building blocks will, over time, be responsible for the greatest amount of investment progress.  However, there are occasions when a new and rare discovery opens up new opportunities for investors.  In much the same way that mutation can accelerate the evolutionary process, so too can new ideas speed us along in our understanding of how markets work.  If you are able to discover a new building block, you have the potential to add another level to your model of understanding.

 

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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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.

Common Stocks and Common Sense

(Image:  Zen Buddha Silence by Marilyn Barbone)

October 15, 2017

It’s crucial in investing to have the proper balance of confidence and humility.  Overconfidence is very deep-seated in human nature.  Nearly all of us tend to believe that we’re above average across a variety of dimensions, such as looks, smarts, academic ability, business aptitude, driving skill, and even luck (!).

Overconfidence is often harmless and it even helps in some areas.  But when it comes to investing, if we’re overconfident about what we know and can do, eventually our results will suffer.

(Image by Wilma64)

The simple truth is that the vast majority of us should invest in broad market low-cost index funds.  Buffett has maintained this argument for a long time: http://boolefund.com/warren-buffett-jack-bogle/

The great thing about investing in index funds is that you can outperform most investors, net of costs, over the course of several decades.  This is purely a function of costs.  A Vanguard S&P 500 index fund costs 2-3% less per year than the average actively managed fund.  This means that, after a few decades, you’ll be ahead of roughly 90% (or more) of all active investors.

You can do better than a broad market index fund if you invest in a solid quantitative value fund.  Such a fund can do at least 1-2% better per year, on average and net of costs, than a broad market index fund.

But you can do even better—at least 5% better per year than the S&P 500 index—by investing in a quantitative value fund focused on microcap stocks.

  • At the Boole Microcap Fund, our mission is to help you do at least 5% better per year, on average, than an S&P 500 index fund.  We achieve this by implementing a quantitative deep value approach focused on cheap micro caps with improving fundamentals.  See: http://boolefund.com/best-performers-microcap-stocks/

 

I recently re-read Common Stocks and Common Sense (Wiley, 2016), by Edgar Wachenheim III.  It’s a wonderful book.  Wachenheim is one of the best value investors.  He and his team at Greenhaven Associates have produced 19% annual returns for over 25 years.

Wachenheim emphasizes that, due to certain behavioral attributes, he has outperformed many other investors who are as smart or smarter.  As Warren Buffett has said:

Success in investing doesn’t correlate with IQ once you’re above the level of 125.  Once you have ordinary intelligence, what you need is the temperament to control the urges that get other people into trouble in investing.

That’s not to say IQ isn’t important.  Most of the finest investors are extremely smart.  Wachenheim was a Baker Scholar at Harvard Business School, meaning that he was in the top 5% of his class.

The point is that—due to behavioral factors such as patience, discipline, and rationality—top investors outperform many other investors who are as smart or smarter.  Buffett again:

We don’t have to be smarter than the rest; we have to be more disciplined than the rest.

Buffett himself has always been extraordinarily patient and disciplined.  There have been several times in Buffett’s career when he went for years on end without making a single investment.

Wachenheim highlights three behavioral factors that have helped him outperform others of equal or greater talent.

The bulk of Wachenheim’s book—chapters 3 through 13—is case studies of specific investments.  Wachenheim includes a good amount of fascinating business history, some of which is mentioned here.

Outline for this blog post:

  • Approach to Investing
  • Being a Contrarian
  • Probable Scenarios
  • Controlling Emotions
  • IBM
  • Interstate Bakeries
  • U.S. Home Corporation
  • Centex
  • Union Pacific
  • American International Group
  • Lowe’s
  • Whirlpool
  • Boeing
  • Southwest Airlines
  • Goldman Sachs

(Photo by Lsaloni)

 

APPROACH TO INVESTING

From 1960 through 2009 in the United States, common stocks have returned about 9 to 10 percent annually (on average).

The U.S. economy grew at roughly a 6 percent annual rate—3 percent from real growth (unit growth) and 3 percent from inflation (price increases).  Corporate revenues—and earnings—have increased at approximately the same 6 percent annual rate.  Share repurchases and acquisitions have added 1 percent a year, while dividends have averaged 2.5 percent a year.  That’s how, on the whole, U.S. stocks have returned 9 to 10 percent annually, notes Wachenheim.

Even if the economy grows more slowly in the future, Wachenheim argues that U.S. investors should still expect 9 to 10 percent per year.  In the case of slower growth, corporations will not need to reinvest as much of their cash flows.  That extra cash can be used for dividends, acquisitions, and share repurchases.

Following Warren Buffett and Charlie Munger, Wachenheim defines risk as the potential for permanent loss.  Risk is not volatility.

Stocks do fluctuate up and down.  But every time the market has declined, it has ultimately recovered and gone on to new highs.  The financial crisis in 2008-2009 is an excellent example of large—but temporary—downward volatility:

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 that he would have received during the six-year period.  Earlier, I mentioned that over a 50-year period, the stock market provided an average annual return of 9 to 10 percent.  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.

(Photo by Terry Mason)

Wachenheim notes that volatility is the friend of the long-term investor.  The more volatility there is, the more opportunity to buy at low prices and sell at high prices.

Because the stock market increases on average 9 to 10 percent per year and always recovers from declines, hedging is a waste of money over the long term:

While many investors believe that they should continually reduce their risks to a possible decline in the stock market, I disagree.  Every time the stock market has declined, it eventually has more than fully recovered.  Hedging the stock market by shorting stocks, or by buying puts on the S&P 500 Index, or any other method usually is expensive, and, in the long run, is a waste of money.

Wachenheim describes his investment strategy as buying deeply undervalued stocks of strong and growing companies that are likely to appreciate significantly due to positive developments not yet discounted by stock prices.

Positive developments can include:

  • a cyclical upturn in an industry
  • an exciting new product or service
  • the sale of a company to another company
  • the replacement of a poor management with a good one
  • a major cost reduction program
  • a substantial share repurchase program

If the positive developments do not occur, Wachenheim still expects the investment to earn a reasonable return, perhaps close to the average market return of 9 to 10 percent annually.  Also, Wachenheim and his associates view undervaluation, growth, and strength as providing a margin of safety—protection against permanent loss.

Wachenheim emphasizes that at Greenhaven, they are value investors not growth investors.  A growth stock investor focuses on the growth rate of a company.  If a company is growing at 15 percent a year and can maintain that rate for many years, then most of the returns for a growth stock investor will come from future growth.  Thus, a growth stock investor can pay a high P/E ratio today if growth persists long enough.

Wachenheim disagrees with growth investing as a strategy:

…I have a problem with growth-stock investing.  Companies tend not to grow at high rates forever.  Businesses change with time.  Markets mature.  Competition can increase.  Good managements can retire and be replaced with poor ones.  Indeed, the market is littered with once highly profitable growth stocks that have become less profitable cyclic stocks as a result of losing their competitive edge.  Kodak is one example.  Xerox is another.  IBM is a third.  And there are hundreds of others.  When growth stocks permanently falter, the price of their shares can fall sharply as their P/E ratios contract and, sometimes, as their earnings fall—and investors in the shares can suffer serious permanent loss.

Many investors claim that they will be able to sell before a growth stock seriously declines.  But very often it’s difficult to determine whether a company is suffering from a temporary or permanent decline.

Wachenheim observes that he’s known many highly intelligent investors—who have similar experiences to him and sensible strategies—but who, nonetheless, haven’t been able to generate results much in excess of the S&P 500 Index.  Wachenheim says that a key point of his book is that there are three behavioral attributes that a successful investor needs:

In particular, I believe that a successful investor must be adept at making contrarian decisions that are counter to the conventional wisdom, must be confident enough to reach conclusions based on probabilistic future developments as opposed to extrapolations of recent trends, and must be able to control his emotions during periods of stress and difficulties.  These three behavioral attributes are so important that they merit further analysis.

 

BEING A CONTRARIAN

(Photo by Marijus Auruskevicius)

Most investors are not contrarians because they nearly always follow the crowd:

Because at any one time the price of a stock is determined by the opinion of the majority of investors, a stock that appears undervalued to us appears appropriately valued to most other investors.  Therefore, by taking the position that the stock is undervalued, we are taking a contrarian position—a position that is unpopular and often is very lonely.  Our experience is that while many investors claim they are contrarians, in practice most find it difficult to buck the conventional wisdom and invest counter to the prevailing opinions and sentiments of other investors, Wall Street analysts, and the media.  Most individuals and most investors simply end up being followers, not leaders.

In fact, I believe that the inability of most individuals to invest counter to prevailing sentiments is habitual and, most likely, a genetic trait.  I cannot prove this scientifically, but I have witnessed many intelligent and experienced investors who shunned undervalued stocks that were under clouds, favored fully valued stocks that were in vogue, and repeated this pattern year after year even though it must have become apparent to them that the pattern led to mediocre results at best.

Wachenheim mentions a fellow investor he knows—Danny.  He notes that Danny has a high IQ, attended an Ivy League university, and has 40 years of experience in the investment business.  Wachenheim often describes to Danny a particular stock that is depressed for reasons that are likely temporary.  Danny will express his agreement, but he never ends up buying before the problem is fixed.

In follow-up conversations, Danny frequently states that he’s waiting for the uncertainty to be resolved.  Value investor Seth Klarman explains why it’s usually better to invest before the uncertainty is resolved:

Most investors strive fruitlessly for certainty and precision, avoiding situations in which information is difficult to obtain.  Yet high uncertainty is frequently accompanied by low prices.  By the time the uncertainty is resolved, prices are likely to have risen.  Investors frequently benefit from making investment decisions with less than perfect knowledge and are well rewarded for bearing the risk of uncertainty.  The time other investors spend delving into the last unanswered detail may cost them the chance to buy in at prices so low that they offer a margin of safety despite the incomplete information.

 

PROBABLE SCENARIOS

(Image by Alain Lacroix)

Many (if not most) investors tend to extrapolate recent trends into the future.  This usually leads to underperforming the market.  See:

The successful investor, by contrast, is a contrarian who can reasonably estimate future scenarios and their probabilities of occurrence:

Investment decisions seldom are clear.  The information an investor receives about the fundamentals of a company usually is incomplete and often is conflicting.  Every company has present or potential problems as well as present or future strengths.  One cannot be sure about the future demand for a company’s products or services, about the success of any new products or services introduced by competitors, about future inflationary cost increases, or about dozens of other relevant variables.  So investment outcomes are uncertain.  However, when making decisions, an investor often can assess the probabilities of certain outcomes occurring and then make his decisions based on the probabilities.  Investing is probabilistic.

Because investing is probabilitistic, mistakes are unavoidable.  A good value investor typically will have at least 33% of his or her ideas not work, whether due to an error, bad luck, or an unforeseeable event.  You have to maintain equanimity despite inevitable mistakes:

If I carefully analyze a security and if my analysis is based on sufficiently large quantities of accurate information, I always will be making a correct decision.  Granted, the outcome of the decision might not be as I had wanted, but I know that decisions always are probabilistic and that subsequent unpredictable changes or events can alter outcomes.  Thus, I do my best to make decisions that make sense given everything I know, and I do not worry about the outcomes.  An analogy might be my putting game in golf.  Before putting, I carefully try to assess the contours and speed of the green.  I take a few practice strokes.  I aim the putter to the desired line.  I then putt and hope for the best.  Sometimes the ball goes in the hole…

 

CONTROLLING EMOTION

(Photo by Jacek Dudzinski)

Wachenheim:

I have observed that when the stock market or an individual stock is weak, there is a tendency for many investors to have an emotional response to the poor performance and to lose perspective and patience.  The loss of perspective and patience often is reinforced by negative reports from Wall Street and from the media, who tend to overemphasize the significance of the cause of the weakness.  We have an expression that aiplanes take off and land every day by the tens of thousands, but the only ones you read about in the newspapers are the ones that crash.  Bad news sells.  To the extent that negative news triggers further selling pressures on stocks and further emotional responses, the negativism tends to feed on itself.  Surrounded by negative news, investors tend to make irrational and expensive decisions that are based more on emotions than on fundamentals. This leads to the frequent sale of stocks when the news is bad and vice versa.  Of course, the investor usually sells stocks after they already have materially decreased in price.  Thus, trading the market based on emotional reactions to short-term news usually is expensive—and sometimes very expensive.

Wachenheim agrees with Seth Klarman that, to a large extent, many investors simply cannot help making emotional investment decisions.  It’s part of human nature.  People overreact to recent news.

I have continually seen intelligent and experienced investors repeatedly lose control of their emotions and repeatedly make ill-advised decisions during periods of stress.

That said, it’s possible (for some, at least) to learn to control your emotions.  Whenever there is news, you can learn to step back and look at your investment thesis.  Usually the investment thesis remains intact.

 

IBM

(IBM Watson by Clockready, Wikimedia Commons)

When Greenhaven purchases a stock, it focuses on what the company will be worth in two or three years.  The market is more inefficient over that time frame due to the shorter term focus of many investors.

In 1993, Wachenheim estimated that IBM would earn $1.65 in 1995.  Any estimate of earnings two or three years out is just a best guess based on incomplete information:

…having projections to work with was better than not having any projections at all, and my experience is that a surprisingly large percentage of our earnings and valuation projections eventually are achieved, although often we are far off on the timing.

The positive development Wachenheim expected was that IBM would announce a concrete plan to significantly reduce its costs.  On July 28, 1993, the CEO Lou Gerstner announced such a plan.  When IBM’s shares moved up from $11½ to $16, Wachenheim sold his firm’s shares since he thought the market price was now incorporating the expected positive development.

Selling IBM at $16 was a big mistake based on subsequent developments.  The company generated large amounts of cash, part of which it used to buy back shares.  By 1996, IBM was on track to earn $2.50 per share.  So Wachenheim decided to repurchase shares in IBM at $24½.  Although he was wrong to sell at $16, he was right to see his error and rebuy at $24½.  When IBM ended up doing better than expected, the shares moved to $48 in late 1997, at which point Wachenheim sold.

Over the years, I have learned that we can do well in the stock market if we do enough things right and if we avoid large permanent losses, but that it is impossible to do nearly everything right.  To err is human—and I make plenty of errors.  My judgment to sell IBM’s shares in 1993 at $16 was an expensive mistake.  I try not to fret over mistakes.  If I did fret, the investment process would be less enjoyable and more stressful.  In my opinion, investors do best when they are relaxed and are having fun.

Finding good ideas takes time.  Greenhaven rejects the vast majority of its potential ideas.  Good ideas are rare.

 

INTERSTATE BAKERIES

(Photo of a bakery by Mohylek, Wikimedia Commons)

Wachenheim discovered that Howard Berkowitz bought 12 percent of the outstanding shares of Interstate Bakeries, became chairman of the board, and named a new CEO.  Wachenheim believed that Howard Berkowitz was an experienced and astute investor.  In 1967, Berkowitz was a founding partner of Steinhardt, Fine, Berkowitz & Co., one of the earliest and most successful hedge funds.  Wachenheim started analyzing Interstate in 1985 when the stock was at about $15:

Because of my keen desire to survive by minimizing risks of permanent loss, the balance sheet then becomes a good place to start efforts to understand a company.  When studying a balance sheet, I look for signs of financial and accounting strengths.  Debt-to-equity ratios, liquidity, depreciation rates, accounting practices, pension and health care liabilities, and ‘hidden’ assets and liabilities all are among common considerations, with their relative importance depending on the situation.  If I find fault with a company’s balance sheet, especially with the level of debt relative to the assets or cash flows, I will abort our analysis, unless there is a compelling reason to do otherwise.  

Wachenheim looks at management after he is done analyzing the balance sheet.  He admits that he is humble about his ability to assess management.  Also, good or bad results are sometimes due in part to chance.

Next Wachenheim examines the business fundamentals:

We try to understand the key forces at work, including (but not limited to) quality of products and services, reputation, competition and protection from future competition, technological and other possible changes, cost structure, growth opportunities, pricing power, dependence on the economy, degree of governmental regulation, capital intensity, and return on capital.  Because we believe that information reduces uncertainty, we try to gather as much information as possible.  We read and think—and we sometimes speak to customers, competitors, and suppliers.  While we do interview the managements of the companies we analyze, we are wary that their opinions and projections will be biased.

Wachenheim reveals that the actual process of analyzing a company is far messier than you might think based on the above descriptions:

We constantly are faced with incomplete information, conflicting information, negatives that have to be weighed against positives, and important variables (such as technological change or economic growth) that are difficult to assess and predict.  While some of our analysis is quantitative (such as a company’s debt-to-equity ratio or a product’s share of market), much of it is judgmental.  And we need to decide when to cease our analysis and make decisions.  In addition, we constantly need to be open to new information that may cause us to alter previous opinions or decisions.

Wachenheim indicates a couple of lessons learned.  First, it can often pay off when you follow a capable and highly incentivized business person into a situation.  Wachenheim made his bet on Interstate based on his confidence in Howard Berkowitz.  Interstate’s shares were not particularly cheap.

Years later, Interstate went bankrupt because they took on too much debt.  This is a very important lesson.  For any business, there will be problems.  Working through difficulties often takes much longer than expected.  Thus, having low or no debt is essential.

 

U.S. HOME CORPORATION

(Photo by Dwight Burdette, Wikimedia Commons)

Wachenheim describes his use of screens:

I frequently use Bloomberg’s data banks to run screens.  I screen for companies that are selling for low price-to-earnings (PE) ratios, low prices to revenues, low price-to-book values, or low prices relative to other relevant metrics.  Usually the screens produce a number of stocks that merit additional analyses, but almost always the additional analyses conclude that there are valid reasons for the apparent undervaluations. 

Wachenheim came across U.S. Home in mid-1994 based on a discount to book value screen.  The shares appeared cheap at 0.63 times book and 6.8 times earnings:

Very low multiples of book and earnings are adrenaline flows for value investors.  I eagerly decided to investigate further.

Later, although U.S. Home was cheap and produced good earnings, the stock price remained depressed.  But there was a bright side because U.S. Home led to another homebuilder idea…

 

CENTEX CORPORATION

(Photo by Steven Pavlov, Wikimedia Commons)

After doing research and constructing a financial model of Centex Corporation, Wachenheim had a startling realization:  the shares would be worth about $63 a few years in the future, and the current price was $12.  Finally, a good investment idea:

…my research efforts usually are tedious and frustrating.  I have hundreds of thoughts and I study hundreds of companies, but good investment ideas are few and far between.  Maybe only 1 percent or so of the companies we study ends up being part of our portfolios—making it much harder for a stock to enter our portfolio than for a student to enter Harvard.  However, when I do find an exciting idea, excitement fills the air—a blaze of light that more than compensates for the hours and hours of tedium and frustration.

Greenhaven typically aims for 30 percent annual returns on each investment:

Because we make mistakes, to achieve 15 to 20 percent average returns, we usually do not purchase a security unless we believe that it has the potential to provide a 30 percent or so annual return.  Thus, we have very high expectations for each investment.

In late 2005, Wachenheim grew concerned that home prices had gotten very high and might decline.  Many experts, including Ben Bernanke, argued that because home prices had never declined in U.S. history, they were unlikely to decline.  Wachenheim disagreed:

It is dangerous to project past trends into the future.  It is akin to steering a car by looking through the rearview mirror…

 

UNION PACIFIC

(Photo by Slambo, Wikimedia Commons)

After World War II, the construction of the interstate highway system gave trucks a competitive advantage over railroads for many types of cargo.  Furthermore, fewer passengers took trains, partly due to the interstate highway system and partly due to the commercialization of the jet airplane.  Excessive regulation of the railroadsin an effort to help farmersalso caused problems.  In the 1960s and 1970s, many railroads went bankrupt.  Finally, the government realized something had to be done and it passed the Staggers Act in 1980, deregulating the railroads:

The Staggers Act was a breath of fresh air.  Railroads immediately started adjusting their rates to make economic sense.  Unprofitable routes were dropped.  With increased profits and with confidence in their future, railroads started spending more to modernize.  New locomotives, freight cars, tracks, automated control systems, and computers reduced costs and increased reliability.  The efficiencies allowed the railroads to reduce their rates and become more competitive with trucks and barges….

In the 1980s and 1990s, the railroad industry also enjoyed increased efficiencies through consolidating mergers.  In the west, the Burlington Northern merged with the Santa Fe, and the Union Pacific merged with the Southern Pacific.  

Union Pacific reduced costs during the 2001-2002 recession, but later this led to congestion on many of its routes and to the need to hire and train new employees once the economy had picked up again.  Union Pacific experienced an earnings shortfall, leading the shares to decline to $14.86.

Wachenheim thought that Union Pacific’s problems were temporary, and that the company would earn about $1.55 in 2006.  With a conservative multiple of 14 times earnings, the shares would be worth over $22 in 2006.  Also, the company was paying a $0.30 annual dividend.  So the total return over a two-year period from buying the shares at $14½ would be 55 percent.

Wachenheim also thought Union Pacific stock had good downside protection because the book value was $12 a share.

Furthermore, even if Union Pacific stock just matched the expected return from the S&P 500 Index of 9½ percent a year, that would still be much better than cash.

The fact that the S&P 500 Index increases about 9½ percent a year is an important reason why shorting stocks is generally a bad business.  To do better than the market, the short seller has to find stocks that underperform the market by 19 percent a year.  Also, short sellers have limited potential gains and unlimited potential losses.  On the whole, shorting stocks is a terrible business and often even the smartest short sellers struggle.

Greenhaven sold its shares in Union Pacific at $31 in mid-2007, since other investors had recognized the stock’s value.  Including dividends, Greenhaven earned close to a 24 percent annualized return.

Wachenheim asks why most stock analysts are not good investors.  For one, most analysts specialize in one industry or in a few industries.  Moreover, analysts tend to extrapolate known information, rather than define future scenarios and their probabilities of occurrence:

…in my opinion, most individuals, including securities analysts, feel more comfortable projecting current fundamentals into the future than projecting changes that will occur in the future.  Current fundamentals are based on known information.  Future fundamentals are based on unknowns.  Predicting the future from unknowns requires the efforts of thinking, assigning probabilities, and sticking one’s neck out—all efforts that human beings too often prefer to avoid.

Also, I believe it is difficult for securities analysts to embrace companies and industries that currently are suffering from poor results and impaired reputations.  Often, securities analysts want to see tangible proof of better results before recommending a stock.  My philosophy is that life is not about waiting for the storm to pass.  It is about dancing in the rain.  One usually can read a weather map and reasonably project when a storm will pass.  If one waits for the moment when the sun breaks out, there is a high probability others already will have reacted to the improved prospects and already will have driven up the price of the stock—and thus the opportunity to earn large profits will have been missed.

Wachenheim then quotes from a New York Times op-ed piece written on October 17, 2008, by Warren Buffett:

A simple rule dictates my buying:  Be fearful when others are greedy, and be greedy when others are fearful.  And most certainly, fear is now widespread, gripping even seasoned investors.  To be sure, investors are right to be wary of highly leveraged entities or businesses in weak competitive positions.  But fears regarding the long-term prosperity of the nation’s many sound companies make no sense.  These businesses will indeed suffer earnings hiccups, as they always have.  But most major companies will be setting new profit records 5, 10, and 20 years from now.  Let me be clear on one point:  I can’t predict the short-term movements of the stock market.  I haven’t the faintest idea as to whether stocks will be higher or lower a month—or a year—from now.  What is likely, however, is that the market will move higher, perhaps substantially so, well before either sentiment or the economy turns up.  So if you wait for the robins, spring will be over.

 

AMERICAN INTERNATIONAL GROUP

(AIG Corporate, Photo by AIG, Wikimedia Commons)

Wachenheim is forthright in discussing Greenhaven’s investment in AIG, which turned out to be a huge mistake.  In late 2005, Wachenheim estimated that the intrinsic value of AIG would be about $105 per share in 2008, nearly twice the current price of $55.  Wachenheim also liked the first-class reputation of the company, so he bought shares.

In late April 2007, AIG’s shares had fallen materially below Greenhaven’s cost basis:

When shares of one of our holdings are weak, we usually revisit the company’s longer-term fundamentals.  If the longer-term fundamentals have not changed, we normally will continue to hold the shares, if not purchase more.  In the case of AIG, it appeared to us that the longer-term fundamentals remained intact.

When Lehman filed for Chapter 11 bankruptcy protection on September 15, 2008, all hell broke loose:

The decline in asset values caused financial institutions to mark down the carrying value of their assets, which, in turn, caused sharp reductions in their credit ratings.  Sharp reductions in credit ratings required financial institutions to raise capital and, in the case of AIG, to post collateral on its derivative contracts.  But the near freezing of the financial markets prevented the requisite raising of capital and cash and thus caused a further deterioration in creditworthiness, which further increased the need for new capital and cash, and so on… On Tuesday night, September 16, the U.S. government agreed to provide the requisite cash in return for a lion’s share of the ownership of AIG.  As soon as I read the agreement, it was clear to me that we had a large permanent loss in our holdings of AIG.

Wachenheim defends the U.S. government bailouts.  Much of the problem was liquidity, not solvency.  Also, the bailouts helped restore confidence in the financial system.

Wachenheim asked himself if he would make the same decision today to invest in AIG:

My answer was ‘yes’—and my conclusion was that, in the investment business, relatively unpredictable outlier developments sometimes can quickly derail otherwise attractive investments.  It comes with the territory.  So while we work hard to reduce the risks of large permanent loss, we cannot completely eliminate large risks.  However, we can draw a line on how much risk we are willing to accept—a line that provides sufficient apparent protection and yet prevents us from being so risk averse that we turn down too many attractive opportunities.  One should not invest with the precept that the next 100-year storm is around the corner.

Wachenheim also points out that when Greenhaven learns of a flaw in its investment thesis, usually the firm is able to exit the position with only a modest loss.  If you’re right 2/3 of the time and if you limit losses as much as possible, the results should be good over time.

 

LOWE’S

(Photo by Miosotis Jade, Wikimedia Commons)

In 2011, Wachenheim carefully analyzed the housing market and reached an interesting conclusion:

I was excited that we had a concept about a probable strong upturn in the housing market that was not shared by most others.  I believed that the existing negativism about housing was due to the proclivity of human beings to uncritically project recent trends into the future and to overly dwell on existing problems.  When analyzing companies and industries, I tend to be an optimist by nature and a pragmatist through effort.  In terms of the proverbial glass of water, it is never half empty, but always half full—and, as a pragmatist, it is twice as large as it needs to be.

Next Wachenheim built a model to estimate normalized earnings for Lowe’s three years in the future (in 2014).  He came up with normal earnings of $3 per share.  He thought the appropriate price-to-earnings ratio was 16.  So the stock would be worth $48 in 2014 versus its current price (in 2011) of $24.  It looked like a bargain.

After gathering more information, Wachenheim revised his earnings model:

…I revise models frequently because my initial models rarely are close to being accurate.  Usually, they are no better than directional.  But they usually do lead me in the right direction, and, importantly, the process of constructing a model forces me to consider and weigh the central fundamentals of a company that will determine the company’s future value.

Wachenheim now thought that Lowe’s could earn close to $4.10 in 2015, which would make the shares worth even more than $48.  In August 2013, the shares hit $45.

In late September 2013, after playing tennis, another money manager asked Wachenheim if he was worried that the stock market might decline sharply if the budget impasse in Congress led to a government shutdown:

I answered that I had no idea what the stock market would do in the near term.  I virtually never do.  I strongly believe in Warren Buffett’s dictum that he never has an opinion on the stock market because, if he did, it would not be any good, and it might interfere with opinions that are good.  I have monitored the short-term market predictions of many intelligent and knowledgeable investors and have found that they were correct about half the time.  Thus, one would do just as well by flipping a coin.

I feel the same way about predicting the short-term direction of the economy, interest rates, commodities, or currencies.  There are too many variables that need to be identified and weighed.

As for Lowe’s, the stock hit $67.50 at the end of 2014, up 160 percent from what Greenhaven paid.

 

WHIRLPOOL CORPORATION

(Photo by Steven Pavlov, Wikimedia Commons)

Wachenheim does not believe in the Efficient Market Hypothesis:

It seems to me that the boom-bust of growth stocks in 1968-1974 and the subsequent boom-bust of Internet technology stocks in 1998-2002 serve to disprove the efficient market hypothesis, which states that it is impossible for an investor to beat the stock market because stocks always are efficiently priced based on all the relevant and known information on the fundamentals of the stocks.  I believe that the efficient market hypothesis fails because it ignores human nature, particularly the nature of most individuals to be followers, not leaders.  As followers, humans are prone to embrace that which already has been faring well and to shun that which recently has been faring poorly.  Of course, the act of buying into what already is doing well and shunning what is doing poorly serves to perpetuate a trend.  Other trend followers then uncritically join the trend, causing the trend to feed on itself and causing excesses.

Many investors focus on the shorter term, which generally harms their long-term performance:

…so many investors are too focused on short-term fundamentals and investment returns at the expense of longer-term fundamentals and returns.  Hunter-gatherers needed to be greatly concerned about their immediate survival—about a pride of lions that might be lurking behind the next rock… They did not have the luxury of thinking about longer-term planning… Then and today, humans often flinch when they come upon a sudden apparent danger—and, by definition, a flinch is instinctive as opposed to cognitive.  Thus, over years, the selection process resulted in a subconscious proclivity for humans to be more concerned about the short term than the longer term.

By far the best thing for long-term investors is to do is absolutely nothing.  The investors who end up performing the best over the course of several decades are nearly always those investors who did virtually nothing.  They almost never checked prices.  They never reacted to bad news.

Regarding Whirlpool:

In the spring of 2011, Greenhaven studied Whirlpool’s fundamentals.  We immediately were impressed by management’s ability and willingness to slash costs.  In spite of a materially subnormal demand for appliances in 2010, the company was able to earn operating margins of 5.9 percent.  Often, when a company is suffering from particularly adverse industry conditions, it is unable to earn any profit at all.  But Whirlpool remained moderately profitable.  If the company could earn 5.9 percent margins under adverse circumstances, what could the company earn once the U.S. housing market and the appliance market returned to normal?

Not surprisingly, Wall Street analysts were focused on the short term:

…A report by J. P. Morgan dated April 27, 2011, stated that Whirlpool’s current share price properly reflected the company’s increased costs for raw materials, the company’s inability to increase its prices, and the current soft demand for appliances…

The J. P. Morgan report might have been correct about the near-term outlook for Whirlpool and its shares.  But Greenhaven invests with a two- to four-year time horizon and cares little about the near-term outlook for its holdings.

The bulk of Greenhaven’s returns has been generated by relatively few of its holdings:

If one in five of our holdings triples in value over a three-year period, then the other four holdings only have to achieve 12 percent average annual returns in order for our entire portfolio to achieve its stretch goal of 20 percent.  For this reason, Greenhaven works extra hard trying to identify potential multibaggers.  Whirlpool had the potential to be a multibagger because it was selling at a particularly low multiple of its potential earnings power.  Of course, most of our potential multibaggers do not turn out to be multibaggers.  But one cannot hit a multibagger unless one tries, and sometimes our holdings that initially appear to be less exciting eventually benefit from positive unforeseen events (handsome black swans) and unexpectedly turn out to be a complete winner.  For this reason, we like to remain fully invested as long as our holdings remain reasonably priced and free from large risks of permanent loss.

 

BOEING

(Photo by José A. Montes, Wikimedia Commons)

Wachenheim likes to read about the history of each company that he studies.

On July 4, 1914, a flight took place in Seattle, Washington, that had a major effect on the history of aviation.  On that day, a barnstormer named Terah Maroney was hired to perform a flying demonstration as part of Seattle’s Independence Day celebrations.  After displaying aerobatics in his Curtis floatplane, Maroney landed and offered to give free rides to spectators.  One spectator, William Edward Boeing, a wealthy owner of a lumber company, quickly accepted Maroney’s offer.  Boeing was so exhilarated by the flight that he completely caught the aviation bug—a bug that was to be with him for the rest of his life.

Boeing launched Pacific Aero Products (renamed the Boeing Airplane Company in 1917).  In late 1916, Boeing designed an improved floatplane, the Model C.  The Model C was ready by April 1917, the same month the United States entered the war.  Boeing thought the Navy might need training aircraft.  The Navy bought two.  They performed well, so the Navy ordered 50 more.

Boeing’s business naturally slowed down after the war.  Boeing sold a couple of small floatplanes (B-1’s), then 13 more after Charles Lindberg’s 1927 transatlantic flight.  Still, sales of commercial planes were virtually nonexistent until 1933, when the company started marketing its model 247.

The twin-engine 247 was revolutionary and generally is recognized as the world’s first modern airplane.  It had a capacity to carry 10 passengers and a crew of 3.  It had a cruising speed of 189 mph and could fly about 745 miles before needing to be refueled.

Boeing sold seventy-five 247’s before making the much larger 307 Stratoliner, which would have sold well were it not for the start of World War II.

Boeing helped the Allies defeat Germany.  The Boeing B-17 Flying Fortress bomber and the B-29 Superfortress bomber became legendary.  More than 12,500 B-17s and more than 3,500 B-29s were built (some by Boeing itself and some by other companies that had spare capacity).

Boeing prospered during the war, but business slowed down again after the war.  In mid-1949, the de Havilland Aircraft Company started testing its Comet jetliner, the first use of a jet engine.  The Comet started carrying passengers in 1952.  In response, Boeing started developing its 707 jet.  Commercial flights for the 707 began in 1958.

The 707 was a hit and soon became the leading commercial plane in the world.

Over the next 30 years, Boeing grew into a large and highly successful company.  It introduced many models of popular commercial planes that covered a wide range of capacities, and it became a leader in the production of high-technology military aircraft and systems.  Moreover, in 1996 and 1997, the company materially increased its size and capabilities by acquiring North American Aviation and McDonnell Douglas.

In late 2012, after several years of delays on its new, more fuel-efficient plane—the 787—Wall Street and the media were highly critical of Boeing.  Wachenheim thought that the company could earn at least $7 per share in 2015.  The stock in late 2012 was at $75, or 11 times the $7.  Wachenheim believed that this was way too low for such a strong company.

Wachenheim estimated that two-thirds of Boeing’s business in 2015 would come from commercial aviation.  He figured that this was an excellent business worth 20 times earnings (he used 19 times to be conservative).  He reckoned that defense, one-third of Boeing’s business, was worth 15 times earnings.  Therefore, Wachenheim used 17.7 as the multiple for the whole company, which meant that Boeing would be worth $145 by 2015.

Greenhaven established a position in Boeing at about $75 a share in late 2012 and early 2013.  By the end of 2013, Boeing was at $136.  Because Wall Street now had confidence that the 787 would be a commercial success and that Boeing’s earnings would rise, Wachenheim and his associates concluded that most of the company’s intermediate-term potential was now reflected in the stock price.  So Greenhaven started selling its position.

 

SOUTHWEST AIRLINES

(Photo by Eddie Maloney, Wikimedia Commons)

The airline industry has had terrible fundamentals for a long time.  But Wachenheim was able to be open-minded when, in August 2012, one of his fellow analysts suggested Southwest Airlines as a possible investment.  Over the years, Southwest had developed a low-cost strategy that gave the company a clear competitive advantage.

Greenhaven determined that the stock of Southwest was undervalued, so they took a position.

The price of Southwest’s shares started appreciating sharply soon after we started establishing our position.  Sometimes it takes years before one of our holdings starts to appreciate sharply—and sometimes we are lucky with our timing.

After the shares tripled, Greenhaven sold half its holdings since the expected return from that point forward was not great.  Also, other investors now recognized the positive fundamentals Greenhaven had expected.  Greenhaven sold the rest of its position as the shares continued to increase.

 

GOLDMAN SACHS

(Photo of Marcus Goldman, Wikimedia Commons)

Wachenheim echoes Warren Buffett when it comes to recognizing how much progress the United States has made:

My experience is that analysts and historians often dwell too much on a company’s recent problems and underplay its strengths, progress, and promise.  An analogy might be the progress of the United States during the twentieth century.  At the end of the century, U.S. citizens generally were far wealthier, healthier, safer, and better educated than at the start of the century.  In fact, the century was one of extraordinary progress.  Yet most history books tend to focus on the two tragic world wars, the highly unpopular Vietnam War, the Great Depression, the civil unrest during the Civil Rights movement, and the often poor leadership in Washington.  The century was littered with severe problems and mistakes.  If you only had read the newspapers and the history books, you likely would have concluded that the United States had suffered a century of relative and absolute decline.  But the United States actually exited the century strong and prosperous.  So did Goldman exit 2013 strong and prosperous.

In 2013, Wachenheim learned that Goldman had an opportunity to gain market share in investment banking because some competitors were scaling back in light of new regulations and higher capital requirements.  Moreover, Goldman had recently completed a $1.9 billion cost reduction program.  Compensation as a percentage of sales had declined significantly in the past few years.

Wachenheim discovered that Goldman is a technology company to a large extent, with a quarter of employees working in the technology division.  Furthermore, the company had strong competitive positions in its businesses, and had sold or shut down sub-par business lines.  Wachenheim checked his investment thesis with competitors and former employees.  They confirmed that Goldman is a powerhouse.

Wachenheim points out that it’s crucial for investors to avoid confirmation bias:

I believe that it is important for investors to avoid seeking out information that reinforces their original analyses.  Instead, investors must be prepared and willing to change their analyses and minds when presented with new developments that adversely alter the fundamentals of an industry or company.  Good investors should have open minds and be flexible.

Wachenheim also writes that it’s very important not to invent a new thesis when the original thesis has been invalidated:

We have a straightforward approach.  When we are wrong or when fundamentals turn against us, we readily admit we are wrong and we reverse our course.  We do not seek new theories that will justify our original decision.  We do not let errors fester and consume our attention.  We sell and move on.

Wachenheim loves his job:

I am almost always happy when working as an investment manager.  What a perfect job, spending my days studying the world, economies, industries, and companies;  thinking creatively;  interviewing CEOs of companies… How lucky I am.  How very, very lucky.

 

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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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.

Buffett’s Best: Microcap Cigar Butts

(Image:  Zen Buddha Silence by Marilyn Barbone)

October 8, 2017

Warren Buffett, the world’s greatest investor, earned the highest returns of his career from microcap cigar butts.  Buffett wrote in the 2014 Berkshire Letter:

My cigar-butt strategy worked very well while I was managing small sums.  Indeed, the many dozens of free puffs I obtained in the 1950’s made the decade by far the best of my life for both relative and absolute performance.

Even then, however, I made a few exceptions to cigar butts, the most important being GEICO.  Thanks to a 1951 conversation I had with Lorimer Davidson, a wonderful man who later became CEO of the company, I learned that GEICO was a terrific business and promptly put 65% of my $9,800 net worth into its shares.  Most of my gains in those early years, though, came from investments in mediocre companies that traded at bargain prices.  Ben Graham had taught me that technique, and it worked.

But a major weakness in this approach gradually became apparent:  Cigar-butt investing was scalable only to a point.  With large sums, it would never work well…

Before Buffett led Berkshire Hathaway, he managed an investment partnership from 1957 to 1970 called Buffett Partnership Ltd. (BPL).  While running BPL, Buffett wrote letters to limited partners filled with insights (and humor) about investing and business.  Jeremy C. Miller has written a great book— Warren Buffett’s Ground Rules (Harper, 2016)—summarizing the lessons from Buffett’s partnership letters.

This blog post considers a few topics related to microcap cigar butts:

  • Net Nets
  • Dempster: The Asset Conversion Play
  • Liquidation Value or Earnings Power?
  • Mean Reversion for Cigar Butts
  • Focused vs. Statistical
  • The Rewards of Psychological Discomfort
  • Conclusion

 

NET NETS

Here Miller quotes the November 1966 letter, in which Buffett writes about valuing the partnership’s controlling ownership position in a cigar-butt stock:

…Wide changes in the market valuations accorded stocks at some point obviously find reflection in the valuation of businesses, although this factor is of much less importance when asset factors (particularly when current assets are significant) overshadow earnings power considerations in the valuation process…

Ben Graham’s primary cigar-butt method was net nets.  Take net current asset value minus ALL liabilities, and then only buy the stock at 2/3 (or less) of that level.  If you buy a basket (at least 20-30) of such stocks, then given enough time (at least a few years), you’re virtually certain to get good investment results, predominantly far in excess of the broad market.

A typical net-net stock might have $30 million in cash, with no debt, but have a market capitalization of $20 million.  Assume there are 10 million shares outstanding.  That means the company has $3/share in net cash, with no debt.  But you can buy part ownership of this business by paying only $2/share.  That’s ridiculously cheap.  If the price remained near those levels, you could in theory buy $1 million in cash for $667,000—and repeat the exercise many times.

Of course, a company that cheap almost certainly has problems and may be losing money.  But every business on the planet, at any given time, is in either one of two states:  it is having problems, or it will be having problems.  When problems come—whether company-specific, industry-driven, or macro-related—that often causes a stock to get very cheap.

The key question is whether the problems are temporary or permanent.  Statistically speaking, many of the problems are temporary when viewed over the subsequent 3 to 5 years.  The typical net-net stock is so extremely cheap relative to net tangible assets that usually something changes for the better—whether it’s a change by management, or a change from the outside (or both).  Most net nets are not liquidated, and even those that are still bring a profit in many cases.

The net-net approach is one of the highest-returning investment strategies ever devised.  That’s not a surprise because net nets, by definition, are absurdly cheap on the whole, often trading below net cash—cash in the bank minus ALL liabilities.

Buffett called Graham’s net-net method the cigar butt approach:

…I call it the cigar butt approach to investing.  You walk down the street and you look around for a cigar butt someplace.  Finally you see one and it is soggy and kind of repulsive, but there is one puff left in it.  So you pick it up and the puff is free – it is a cigar butt stock.  You get one free puff on it and then you throw it away and try another one.  It is not elegant.  But it works.  Those are low return businesses.

Link: http://intelligentinvestorclub.com/downloads/Warren-Buffett-Florida-Speech.pdf

(Photo by Sky Sirasitwattana)

When running BPL, Buffett would go through thousands of pages of Moody’s Manuals (and other such sources) to locate just one or a handful of microcap stocks trading at less than liquidation value.  Other leading value investors have also used this technique.  This includes Charlie Munger (early in his career), Walter Schloss, John Neff, Peter Cundill, and Marty Whitman, to name a few.

The cigar butt approach is also called deep value investing.  This normally means finding a stock that is available below liquidation value, or at least below net tangible book value.

When applying the cigar butt method, you can either do it as a statistical group approach, or you can do it in a focused manner.  Walter Schloss achieved one of the best long-term track records of all time—near 21% annually (gross) for 47 years—using a statistical group approach to cigar butts.  Schloss typically had a hundred stocks in his portfolio, most of which were trading below tangible book value.

At the other extreme, Warren Buffett—when running BPL—used a focused approach to cigar butts.  Dempster is a good example, which Miller explores in detail in his book.

 

DEMPSTER: THE ASSET CONVERSION PLAY

Dempster was a tiny micro cap, a family-owned company in Beatrice, Nebraska, that manufactured windmills and farm equipment.  Buffett slowly bought shares in the company over the course of five years.

(Photo by Digikhmer)

Dempster had a market cap of $1.6 million, about $13.3 million in today’s dollars, says Miller.

  • Note:  A market cap of $13.3 million is in the $10 to $25 million range—among the tiniest micro caps—which is avoided by nearly all investors, including professional microcap investors.

Buffett’s average price paid for Dempster was $28/share.  Buffett’s estimate of liquidation value early on was near $35/share, which is intentionally conservative.  Miller quotes one of Buffett’s letters:

The estimated value should not be what we hope it would be worth, or what it might be worth to an eager buyer, etc., but what I would estimate our interest would bring if sold under current conditions in a reasonably short period of time.

To estimate liquidation value, Buffett followed Graham’s method, as Miller explains:

  • cash, being liquid, doesn’t need a haircut
  • accounts receivable are valued at 85 cents on the dollar
  • inventory, carried on the books at cost, is marked down to 65 cents on the dollar
  • prepaid expenses and “other” are valued at 25 cents on the dollar
  • long-term assets, generally less liquid, are valued using estimated auction values

Buffett’s conservative estimate of liquidation value for Dempster was $35/share, or $2.2 million for the whole company.  Recall that Buffett paid an average price of $28/share—quite a cheap price.

Even though the assets were clearly there, Dempster had problems.  Stocks generally don’t get that cheap unless there are major problems.  In Dempster’s case, inventories were far too high and rising fast.  Buffett tried to get existing management to make needed improvements.  But eventually Buffett had to throw them out.  Then the company’s bank was threatening to seize the collateral on the loan.  Fortunately, Charlie Munger—who later became Buffett’s business partner—recommended a turnaround specialist, Harry Bottle.  Miller:

Harry did such an outstanding job whipping the company into shape that Buffett, in the next year’s letter, named him “man of the year.”  Not only did he reduce inventories from $4 million to $1 million, alleviating the concerns of the bank (whose loan was quickly repaid), he also cut administrative and selling expenses in half and closed five unprofitable branches.  With the help of Buffett and Munger, Dempster also raised prices on their used equipment up to 500% with little impact to sales volume or resistance from customers, all of which worked in combination to restore a healthy economic return in the business.

Miller explains that Buffett rationally focused on maximizing the return on capital:

Buffett was wired differently, and he achieves better results in part because he invests using an absolute scale.  With Dempster he wasn’t at all bogged down with all the emotional baggage of being a veteran of the windmill business.  He was in it to produce the highest rate of return on the capital he had tied up in the assets of the business.  This absolute scale allowed him to see that the fix for Dempster would come by not reinvesting back into windmills.  He immediately stopped the company from putting more capital in and started taking the capital out.

With profits and proceeds raised from converting inventory and other assets to cash, Buffett started buying stocks he liked.  In essence, he was converting capital that was previously utilized in a bad (low-return) business, windmills, to capital that could be utilized in a good (high-return) business, securities.

Bottle, Buffett, and Munger maximized the value of Dempster’s assets.  Buffett took the further step of not reinvesting cash in a low-return business, but instead investing in high-return stocks.  In the end, on its investment of $28/share, BPL realized a net gain of $45 per share.  This is a gain of a bit more than 160% on what was a very large position for BPL—one-fifth of the portfolio.  Had the company been shut down by the bank, or simply burned through its assets, the return after paying $28/share could have been nothing or even negative.

Miller nicely summarizes the lessons of Buffett’s asset conversion play:

Buffett teaches investors to think of stocks as a conduit through which they can own their share of the assets that make up a business.  The value of that business will be determined by one of two methods: (1) what the assets are worth if sold, or (2) the level of profits in relation to the value of assets required in producing them.  This is true for each and every business and they are interrelated…

Operationally, a business can be improved in only three ways: (1) increase the level of sales; (2) reduce costs as a percent of sales; (3) reduce assets as a percentage of sales.  The other factors, (4) increase leverage or (5) lower the tax rate, are the financial drivers of business value.  These are the only ways a business can make itself more valuable.

Buffett “pulled all the levers” at Dempster…

 

LIQUIDATION VALUE OR EARNINGS POWER?

For most of the cigar butts that Buffett bought for BPL, he used Graham’s net-net method of buying at a discount to liquidation value, conservatively estimated.  However, you can find deep value stocks—cigar butts—on the basis of other low “price-to-a-fundamental” ratio’s, such as low P/E or low EV/EBITDA.  Even Buffett, when he was managing BPL, used a low P/E in some cases to identify cigar butts.  (See an example below: Western Insurance Securities.)

Tobias Carlisle and Wes Gray tested various measures of cheapness from 1964 to 2011.  Quantitative Value (Wiley, 2012)—an excellent book—summarizes their results.  James P. O’Shaugnessy has conducted one of the broadest arrays of statistical backtests.  See his results in What Works on Wall Street (McGraw-Hill, 4th edition, 2012), a terrific book.

(Illustration by Maxim Popov)

  • Carlisle and Gray found that low EV/EBIT was the best-performing measure of cheapness from 1964 to 2011. It even outperformed composite measures.
  • O’Shaugnessy learned that low EV/EBITDA was the best-performing individual measure of cheapness from 1964 to 2009.
  • But O’Shaugnessy also discovered that a composite measure—combining low P/B, P/E, P/S, P/CF, and EV/EBITDA—outperformed low EV/EBITDA.

Assuming relatively similar levels of performance, a composite measure is arguably better because it tends to be more consistent over time.  There are periods when a given individual metric might not work well.  The composite measure will tend to smooth over such periods.  Besides, O’Shaugnessy found that a composite measure led to the best performance from 1964 to 2009.

Carlisle and Gray, as well as O’Shaugnessy, didn’t include Graham’s net-net method in their reported results.  Carlisle wrote another book, Deep Value (Wiley, 2014)—which is fascinating—in which he summarizes several tests of net nets:

  • Henry Oppenheimer found that net nets returned 29.4% per year versus 11.5% per year for the market from 1970 to 1983.
  • Carlisle—with Jeffrey Oxman and Sunil Mohanty—tested net nets from 1983 to 2008. They discovered that the annual returns for net nets averaged 35.3% versus 12.9% for the market and 18.4% for a Small Firm Index.
  • A study of the Japanese market from 1975 to 1988 uncovered that net nets outperformed the market by about 13% per year.
  • An examination of the London Stock Exchange from 1981 to 2005 established that net nets outperformed the market by 19.7% per year.
  • Finally, James Montier analyzed all developed markets globally from 1985 to 2007. He learned that net nets averaged 35% per year versus 17% for the developed markets on the whole.

Given these outstanding returns, why didn’t Carlisle and Gray, as well as O’Shaugnessy, consider net nets?  Primarily because many net nets are especially tiny microcap stocks.  For example, in his study, Montier found that the median market capitalization for net nets was $21 million.  Even the majority of professionally managed microcap funds do not consider stocks this tiny.

  • Recall that Dempster had a market cap of $1.6 million, or about $13.3 million in today’s dollars.
  • Unlike the majority of microcap funds, the Boole Microcap Fund does consider microcap stocks in the $10 to $25 million market cap range.

In 1999, Buffett commented that he could get 50% per year by investing in microcap cigar butts.  He was later asked about this comment in 2005, and he replied:

Yes, I would still say the same thing today.  In fact, we are still earning those types of returns on some of our smaller investments.  The best decade was the 1950s;  I was earning 50% plus returns with small amounts of capital.  I would do the same thing today with smaller amounts.  It would perhaps even be easier to make that much money in today’s environment because information is easier to access.  You have to turn over a lot of rocks to find those little anomalies.  You have to find the companies that are off the map—way off the map.  You may find local companies that have nothing wrong with them at all.  A company that I found, Western Insurance Securities, was trading for $3/share when it was earning $20/share!!  I tried to buy up as much of it as possible.  No one will tell you about these businesses.  You have to find them.

Although the majority of microcap cigar butts Buffett invested in were cheap relative to liquidation value—cheap on the basis of net tangible assets—Buffett clearly found some cigar butts on the basis of a low P/E.  Western Insurance Securities is a good example.

 

MEAN REVERSION FOR CIGAR BUTTS

Warren Buffett commented on high quality companies versus statistically cheap companies in his October 1967 letter to partners:

The evaluation of securities and businesses for investment purposes has always involved a mixture of qualitative and quantitative factors.  At the one extreme, the analyst exclusively oriented to qualitative factors would say, “Buy the right company (with the right prospects, inherent industry conditions, management, etc.) and the price will take care of itself.”  On the other hand, the quantitative spokesman would say, “Buy at the right price and the company (and stock) will take care of itself.”  As is so often the pleasant result in the securities world, money can be made with either approach.  And, of course, any analyst combines the two to some extent—his classification in either school would depend on the relative weight he assigns to the various factors and not to his consideration of one group of factors to the exclusion of the other group.

Interestingly enough, although I consider myself to be primarily in the quantitative school… the really sensational ideas I have had over the years have been heavily weighted toward the qualitative side where I have had a “high-probability insight”.  This is what causes the cash register to really sing.  However, it is an infrequent occurrence, as insights usually are, and, of course, no insight is required on the quantitative side—the figures should hit you over the head with a baseball bat.  So the really big money tends to be made by investors who are right on qualitative decisions but, at least in my opinion, the more sure money tends to be made on the obvious quantitative decisions.

Buffett and Munger acquired See’s Candies for Berkshire Hathaway in 1972.  See’s Candies is the quintessential high quality company because of its sustainably high ROIC (return on invested capital) of over 100%.

Truly high quality companies—like See’s—are very rare and difficult to find.  Cigar butts—including net nets—are much easier to find by comparison.

Furthermore, it’s important to understand that Buffett got around 50% annual returns from cigar butts because he took a focused approach, like BPL’s 20% position in Dempster.

The vast majority of investors, if using a cigar butt approach like net nets, should implement a group—or statistical—approach, and regularly buy and hold a basket of cigar butts (at least 20-30).  This typically won’t produce 50% annual returns.  But net nets, as a group, clearly have produced very high returns, often 30%+ annually.  To do this today, you’d have to look globally.

As an alternative to net nets, you could implement a group approach using one of O’Shaugnessy’s composite measures—such as low P/B, P/E, P/S, P/CF, EV/EBITDA.  Applying this to micro caps can produce 15-20% annual returns.  Generally not as good as net nets, but much easier to apply consistently.

You may think that you can find some high quality companies.  But that’s not enough.  You have to find a high quality company that can maintain its competitive position and high ROIC.  And it has to be available at a reasonable price.

Most high quality companies are trading at very high prices, to the extent that you can’t do better than the market by investing in them.  In fact, often the prices are so high that you’ll probably do worse than the market.

Consider this comment by Charlie Munger:

The model I like to sort of simplify the notion of what goes o­n in a market for common stocks is the pari-mutuel system at the racetrack.  If you stop to think about it, a pari-mutuel system is a market.  Everybody goes there and bets and the odds change based o­n what’s bet.  That’s what happens in the stock market.

Any damn fool can see that a horse carrying a light weight with a wonderful win rate and a good post position etc., etc. is way more likely to win than a horse with a terrible record and extra weight and so o­n and so on.  But if you look at the odds, the bad horse pays 100 to 1, whereas the good horse pays 3 to 2.  Then it’s not clear which is statistically the best bet using the mathematics of Fermat and Pascal.  The prices have changed in such a way that it’s very hard to beat the system.

(Illustration by Nadoelopisat)

A horse with a great record (etc.) is much more likely to win than a horse with a terrible record.  But—whether betting on horses or betting on stocks—you don’t get paid for identifying winners.  You get paid for identifying mispricings.

The statistical evidence is overwhelming that if you systematically buy stocks at low multiples—P/B, P/E, P/S, P/CF, EV/EBITDA, etc.—you’ll almost certainly do better than the market over the long haul.

A deep value—or cigar butt—approach has always worked, given enough time.  Betting on “the losers” has always worked eventually, whereas betting on “the winners” hardly ever works.

Classic academic studies showing “the losers” doing far better than “the winners” over subsequent 3- to 5-year periods:

That’s not to say deep value investing is easy.  When you put together a basket of statistically cheap companies, you’re buying stocks that are widely hated or neglected.  You have to endure loneliness and looking foolish.  Some people can do it, but it’s important to know yourself before using a deep value strategy.

In general, we extrapolate the poor performance of cheap stocks and the good performance of expensive stocks too far into the future.  This is the mistake of ignoring mean reversion.

When you find a group of companies that have been doing poorly for at least several years, those conditions typically do not persist.  Instead, there tends to be mean reversion, or a return to “more normal” levels of revenues, earnings, or cash flows.

Similarly for a group of companies that have been doing exceedingly well.  Those conditions also do not continue in general.  There tends to be mean reversion, but in this case the mean—the average or “normal” conditions—is below recent activity levels.

Here’s Ben Graham explaining mean reversion:

It is natural to assume that industries which have fared worse than the average are “unfavorably situated” and therefore to be avoided.  The converse would be assumed, of course, for those with superior records.  But this conclusion may often prove quite erroneous.  Abnormally good or abnormally bad conditions do not last forever.  This is true of general business but of particular industries as well.  Corrective forces are usually set in motion which tend to restore profits where they have disappeared or to reduce them where they are excessive in relation to capital.

With his taste for literature, Graham put the following quote from Horace’s Ars Poetica at the beginning of Security Analysis—the bible for value investors:

Many shall be restored that now are fallen and many shall fall than now are in honor.

Tobias Carlisle, while discussing mean reversion in Deep Value, smartly (and humorously) included this image of Albrecht Durer’s Wheel of Fortune:

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

 

FOCUSED vs. STATISTICAL

We’ve already seen that there are two basic ways to do cigar-butt investing: focused vs. statistical (group).

Ben Graham usually preferred the statistical—or group—approach.  Near the beginning of the Great Depression, Graham’s managed accounts lost more than 80 percent.  Furthermore, the economy and the stock market took a long time to recover.  As a result, Graham had a strong tendency towards conservatism in investing.  This is likely part of why he preferred the statistical approach to net nets.  By buying a basket of net nets (at least 20-30), the investor is virtually certain to get the statistical results of the group over time, which are broadly excellent.

Graham also was a polymath of sorts.  He had wide-ranging intellectual interests.  Because he knew net nets as a group would do quite well over the long term, he wasn’t inclined to spend much time analyzing individual net nets.  Instead, he spent time on his other interests.

Warren Buffett was Graham’s best student.  Buffett was the only student ever to be awarded an A+ in Graham’s class at Columbia University.  Unlike Graham, Buffett has always had an extraordinary focus on business and investing.  After spending many years learning everything about virtually every public company, Buffett took a focused approach to net nets.  He found the ones that were the cheapest and that seemed the surest.

Buffett has asserted that returns can be improved—and risk lowered—if you focus your investments only on those companies that are within your circle of competence—those companies that you can truly understand.  Buffett also maintains, however, that the vast majority of investors should simply invest in index funds: http://boolefund.com/warren-buffett-jack-bogle/

Regarding individual net nets, Graham admitted a danger:

Corporate gold dollars are now available in quantity at 50 cents and less—but they do have strings attached.  Although they belong to the stockholder, he doesn’t control them.  He may have to sit back and watch them dwindle and disappear as operating losses take their toll.  For that reason the public refuses to accept even the cash holdings of corporations at their face value.

Graham explained that net nets are cheap because they “almost always have an unsatisfactory trend in earnings.”  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.

(Image by Preecha Israphiwat)

Value investor Seth Klarman warns:

As long as working capital is not overstated and operations are not rapidly consuming cash, a company could liquidate its assets, extinguish all liabilities, and still distribute proceeds in excess of the market price to investors.  Ongoing business losses can, however, quickly erode net-net working capital.  Investors must therefore always consider the state of a company’s current operations before buying.

Even Buffett—nearly two decades after closing BPL—wrote the following in his 1989 letter to Berkshire shareholders:

If you buy a stock at a sufficiently low price, there will usually be some hiccup in the fortunes of the business that gives you a chance to unload at a decent profit, even though the long-term performance of the business may be terrible.  I call this the “cigar butt” approach to investing.  A cigar butt found on the street that has only one puff left in it may not offer much of a smoke, but the “bargain purchase” will make that puff all profit.

Unless you are a liquidator, that kind of approach to buying businesses is foolish.  First, the original “bargain” price probably will not turn out to be such a steal after all.  In a difficult business, no sooner is one problem solved than another surfaces—never is there just one cockroach in the kitchen.  Second, any initial advantage you secure will be quickly eroded by the low return that the business earns.  For example, if you buy a business for $8 million that can be sold or liquidated for $10 million and promptly take either course, you can realize a high return.  But the investment will disappoint if the business is sold for $10 million in ten years and in the interim has annually earned and distributed only a few percent on cost…

Based on these objections, you might think that Buffett’s focused approach is better than the statistical (group) method.  That way, the investor can figure out which net nets are more likely to recover rather than burn through their assets and leave the investor with a low or negative return.

However, Graham’s response was that the statistical or group approach to net nets is highly profitable over time.  There is a wide range of potential outcomes for net nets, and many of those scenarios are good for the investor.  Therefore, while there are always some individual net nets that don’t work out, a group or basket of net nets is nearly certain to work well eventually.

Indeed, Graham’s application of a statistical net-net approach produced 20% annual returns over many decades.  Most backtests of net nets have tended to show annual returns of close to 30%.  In practice, while around 5 percent of net nets may suffer a terminal decline in stock price, a statistical group of net nets has done far better than the market and has experienced fewer down years.  Moreover, as Carlisle notes in Deep Value, very few net nets are actually liquidated or merged.  In the vast majority of cases, there is a change by management, a change from the outside, or both, in order to restore earnings to a level more in line with net asset value.  Mean reversion.

 

THE REWARDS OF PSYCHOLOGICAL DISCOMFORT

We noted earlier that it’s far more difficult to find a company like See’s Candies, at a reasonable price, than it is to find statistically cheap stocks.  Moreover, if you buy a basket of statistically cheap stocks, you don’t have to possess an ability to analyze individual businesses in great depth.

That said, in order to use a deep value strategy, you do have to be able to handle the psychological discomfort of being lonely and looking foolish.

(Illustration by Sangoiri)

John Mihaljevic, author of The Manual of Ideas (Wiley, 2013), writes:

Comfort can be expensive in investing.  Put differently, acceptance of discomfort can be rewarding, as equities that cause their owners discomfort frequently trade at exceptionally low valuations….

…Misery loves company, so it makes sense that rewards may await those willing to be miserable in solitude…

Mihaljevic explains:

If we owned nothing but a portfolio of Ben Graham-style bargain equities, we may become quite uncomfortable at times, especially if the market value of the portfolio declined precipitously.  We might look at the portfolio and conclude that every investment could be worth zero.  After all, we could have a mediocre business run by mediocre management, with assets that could be squandered.  Investing in deep value equities therefore requires faith in the law of large numbers—that historical experience of market-beating returns in deep value stocks and the fact that we own a diversified portfolio will combine to yield a satisfactory result over time.  This conceptually sound view becomes seriously challenged in times of distress…

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?

Deep value investors often find some of the best investments in cyclical areas.  A company at a cyclical low may have multi-bagger potential—the prospect of returning 300-500% (or more) to the investor.

A good current example is Ensco plc (NYSE: ESV), an offshore oil driller.  Having just completed its acquisition of Atwood Oceanics (NYSE: ATW), Ensco is now a leading offshore driller with a high-specification, globally diverse fleet.  The company also has one of the lowest cost structures, and relatively low debt levels (with the majority of debt due in 2024 or later).  Ensco—like Atwood—has a long history of operational excellence and safety.  Ensco has been rated #1 for seven consecutive years in the leading independent customer satisfaction survey.

  • At $5.60 recently, Ensco is trading near 20% of tangible book value.  (It purchased Atwood at about the same discount to tangible book.)  If oil prices revert to a mean of $60-70 per barrel (or more), Ensco will probably be worth at least tangible book value.
  • That implies a 400% return (or more)—over the next 3 to 5 years—for an investor who owns shares today.

However, it’s possible oil will never return to $60-70.  It’s possible the seemingly cyclical decline for offshore oil drillers is actually more permanent in nature.  Mihaljevic observes:

The question of whether a company has entered permanent decline is anything but easy to answer, as virtually all companies appear to be in permanent decline when they hit a rock-bottom market quotation.  Even if a business has been cyclical in the past, analysts generally adopt a “this time is different” attitude.  As a pessimistic stock price inevitably influences the appraisal objectivity of most investors, it becomes exceedingly difficult to form a view strongly opposed to the prevailing consensus.

Consider the following industries that have been pronounced permanently impaired in the past, only to rebound strongly in subsequent years:  Following the financial crisis of 2008-2009, many analysts argued that the banking industry would be permanently negatively affected, as higher capital requirements and regulatory oversight would compress returns on equity.  The credit rating agencies were seen as impaired because the regulators would surely alter the business model of the industry for the worse following the failings of the rating agencies during the subprime mortgage bubble.  The homebuilding industry would fail to rebound as strongly as in the past, as overcapacity became chronic and home prices remained tethered to building costs.  The refining industry would suffer permanently lower margins, as those businesses were capital-intensive and driven by volatile commodity prices.

Are offshore oil drillers in a cyclical or a secular decline?  It’s likely that oil will return to $60-70, at least in the next 5-10 years.  But no one knows for sure.

Ongoing improvements in technology allow oil producers to get more oil—more cheaply—out of existing fields.  Also, growth in transport demand for oil will slow significantly at some point, due to ongoing improvements in fuel efficiency.  See: https://www.spe.org/en/jpt/jpt-article-detail/?art=3286

Transport demand is responsible for over 50% of daily oil consumption, and it’s inelastic—typically people have to get where they’re going, so they’re not very sensitive to fuel price increases.

But even if oil never returns to $60+, oil will be needed for many decades.  At least some offshore drilling will still be needed, and Ensco will be a survivor.

Full Disclosure:

  • The Boole Fund had an investment in Atwood Oceanics. With the acquisition of Atwood by Ensco now completed, the Boole Fund currently owns shares in Ensco plc.
  • The Boole Fund holds positions for 3 to 5 years. The fund doesn’t sell an investment that is still cheap, even if the stock in question is no longer a micro cap.

 

CONCLUSION

Buffett has made it clear, including in his 2014 letter to shareholders, that the best returns of his career came from investing in microcap cigar butts.  Most of these were mediocre businesses (or worse).  But they were ridiculously cheap.  And, in some cases like Dempster, Buffett was able to bring about needed improvements when required.

When Buffett wrote about buying wonderful businesses in his 1989 letter, that’s chiefly because investable assets at Berkshire Hathaway had grown far too large for microcap cigar butts.

Even in recent years, Buffett invested part of his personal portfolio in a group of cigar butts he found in South Korea.  So he’s never changed his view that an investor can get the highest returns from microcap cigar butts, either by using a statistical group approach or by using a more focused method.

 

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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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 Art of Value Investing

(Image:  Zen Buddha Silence by Marilyn Barbone.)

September 17, 2017

The Art of Value Investing (Wiley, 2013) is an excellent book by John Heins and Whitney Tilson.  Heins and Tilson have been running the monthly newsletter, Value Investor Insight, for a decade now.  Over that time, they have interviewed many of the best value investors in the world.  The Art of Value Investing is a collection of quotations carefully culled from those interviews.

I’ve selected and discussed the best quotes from the following areas:

  • Margin of Safety
  • Humility, Flexibility, and Patience
  • Courage
  • Cigar-Butt’s
  • Opportunities in Micro Caps
  • Predictable Human Irrationality
  • Long-Term Time Horizon
  • Screening and Quantitative Models

 

MARGIN OF SAFETY

(Ben Graham, by Equim43)

Ben Graham, the father of value investing, stressed having a margin of safety by buying well below the probable intrinsic value of a stock.  This is essential because the future is uncertain.  Also, mistakes are inevitable.  (Good value investors tend to be right 60 percent of the time and wrong 40 percent of the time.)  Jean-Marie Eveillard:

Whenever Ben Graham was asked what he thought would happen to the economy or to company X’s or Y’s profits, he always used to deadpan, ‘The future is uncertain.’  That’s precisely why there’s a need for a margin of safety in investing, which is more relevant today than ever.

Value investing legend Seth Klarman:

People should be highly skeptical of anyone’s, including their own, ability to predict the future, and instead pursue strategies that can survive whatever may occur.  

The central idea in value investing is to figure out what a business is worth (approximately), and then pay a lot less to acquire part ownership of that business via stock.  Howard Marks:

If I had to identify a single key to consistently successful investing, I’d say it’s ‘cheapness.’  Buying at low prices relative to intrinsic value (rigorously and conservatively derived) holds the key to earning dependably high returns, limiting risk and minimizing losses.  It’s not the only thing that matters—obviously—but it’s something for which there is no substitute.

 

HUMILITY, FLEXIBILITY, AND PATIENCE

(Image by Wilma64)

Successful value investing, to a large extent, is about having the right mindset.  Matthew McLennan identifies humility, flexibility, and patience as key traits:

Starting with the first recorded and reliable history that we can find—a history of the Peloponnesian war by a Greek author named Thucydides—and following through a broad array of key historical global crises, you see recurring aspects of human nature that have gotten people into trouble:  hubris, dogma, and haste.  The keys to our investing approach are the symmetrical opposite of that:  humility, flexibility, and patience.

On the humility side, one of the things that Jean-Marie Eveillard firmly ingrained in the culture here is that the future is uncertain.  That results in investing with not only a price margin of safety, but in companies with conservative balance sheets and prudent and proven management teams….

In terms of flexibility, we’ve been willing to be out of the biggest sectors of the market…

The third thing in terms of temperament we think we value more than most other investors is patience.  We have a five-year average holding period….We like to plant seeds and then watch the trees grow, and our portfolio is often kind of a portrait of inactivity.

It’s hard to overstate the importance of humility in investing.  Many of the biggest investing mistakes have occurred when intelligent investors who have succeeded in the past have developed high conviction in an idea that happens to be wrong.  Kyle Bass explains this point clearly:

You obviously need to develop strong opinions and to have the conviction to stick with them when you believe you’re right, even when everybody else may think you’re an idiot.  But where I’ve seen ego get in the way is by not always being open to questions and to input that could change your mind.  If you can’t ever admit you’re wrong, you’re more likely to hang on to your losers and sell your winners, which is not a recipe for success.

It often happens in investing that ideas that seem obvious or even irrefutable turn out to be wrong.  The very best investors—such as Warren Buffett, Charlie Munger, Seth Klarman, Howard Marks, Jeremy Grantham, George Soros, and Ray Dalio—have developed enough humility to admit when they’re wrong, even when all the evidence seems to indicate that they’re right.

Here are two great examples of how seemingly irrefutable ideas can turn out to be wrong:

  • shorting the U.S. stock market;
  • shorting the Japanese yen.

(Illustration by Eti Swinford)

Professor Russell Napier is the author of Anatomy of the Bear (Harriman House, 4th edition, 2016).  Napier was a top-rated analyst for many years and has been studying and writing about global macro strategy for institutional investors since 1995.

Napier has maintained (at least since 2012) that the U.S. stock market is significantly overvalued based on the Q-ratio and also the CAPE (cyclically adjusted P/E).  Moreover, Napier points out that every major U.S. secular bear market bottom in the last 100 years or so has seen the CAPE approach single digits.  The catalyst for the major drop has always been either inflation or deflation, states Napier.

Napier continues to argue (mid-2017) that U.S. stocks are overvalued and that deflation will cause the U.S. stock market to drop significantly, similar to previous secular bear markets.

Many highly intelligent value investors—at least since 2012 or 2013—have maintained high cash balances and/or short positions because they essentially agree with Napier’s argument.

However, Napier is probably wrong.  Here’s why:  U.S. interest rates are quite low, while profit margins are high compared to history.  And these conditions are likely to continue.

Low interest rates cause stocks to be much higher than otherwise.  At the extreme, as Buffett has noted, if rates stayed low enough for long enough, the stock market could have a P/E of 50 or more.

Also, U.S. profit margins are considerably higher than they have been in the last 100 years.  This situation will probably persist because software and related technology keep becoming more important in the U.S. and global economy.  The five largest U.S. companies are Google, Apple, Microsoft, Facebook, and Amazon, all technology companies.

One of the most astute value investors who tracks fair value of the S&P 500 Index is Jeremy Grantham of GMO.  Grantham used to think, back in 2012-2013, that the U.S. secular bear market was not over.  Then he partially revised his view and predicted that the S&P 500 Index was likely to exceed 2250-2300.  This level would have made the S&P 500’s value two standard deviations above the historical mean, indicating that it was back in bubble territory according to GMO’s definition.

Recently, in the GMO Quarterly Letter (Q2 2017), Grantham has revised his view again.  See: https://www.gmo.com/docs/default-source/public-commentary/gmo-quarterly-letter.pdf

Grantham now says that without a crash in profit margins, or without a dramatic sustained rise in inflation, there’s no reason to expect a market crash.  Furthermore, Grantham believes it’s unlikely for either of those things to happen, especially in the near term.  The fact that Grantham has been able to take in new information and noticeably revise his strongest convictions illustrates why he is a top value investor.

(Image by joshandandreaphotography)

As John Maynard Keynes is (probably incorrectly) reported to have said:

When the information changes, I alter my conclusions.  What do you do, sir?

There are some very smart value investors—such as Frank Martin and John Hussman—who still basically agree with Russell Napier’s views.  They may eventually be right.

But no one has ever been able to predict the stock market.  Ben Graham—with a 200 IQ—was as smart or smarter than any value investor who’s ever lived.  And here’s what Graham said near the end of his career:

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.

In 1963, Graham gave a lecture, “Securities in an Insecure World.”  Link: https://www8.gsb.columbia.edu/rtfiles/Heilbrunn/Schloss%20Archives%20for%20Value%20Investing/Articles%20by%20Benjamin%20Graham/DOC005.PDF

In the lecture, Graham admits that the Graham P/E—based on ten-year average earnings of the Dow components—was much too conservative.  Graham:

The action of the stock market since then would appear to demonstrate that these methods of valuations are ultra-conservative and much too low, although they did work out extremely well through the stock market fluctuations from 1871 to about 1954, which is an exceptionally long period of time for a test.  Unfortunately in this kind of work, where you are trying to determine relationships based upon past behavior, the almost invariable experience is that by the time you have had a long enough period to give you sufficient confidence in your form of measurement just then new conditions supersede and the measurement is no longer dependable for the future.

Jeremy Grantham, in the GMO Q2 2017 Letter mentioned earlier, actually quotes these two sentences (among others).  But I first discovered Graham’s 1963 lecture several years ago.

Graham goes on to note that, in the 1962 edition of Security Analysis, Graham and Dodd addressed this issue.  Because of the U.S. government’s more aggressive policy with respect to preventing a depression, Graham and Dodd concluded that the U.S. stock market should have a fair value 50 percent higher.

Similar logic can be applied to the S&P 500 Index today—which exceeds 2500.  If interest rates remain relatively low for many years—in part based on a more aggressive Fed policy (designed to avoid deflation and create inflation)—and if profit margins are at a permanently higher level, then fair value for the S&P 500 has arguably increased significantly.  Whereas the CAPE (cyclically adjusted P/E)—the modern form of the original Graham P/E—put fair value of the S&P 500 Index at around 1100-1200 back in 2011-2013, that’s way too low if interest rates remain low and if profit margins are permanently higher.

In brief, previous methods—very well-established based on nearly a century—put fair value for the S&P 500 Index around 1100-1200.  But actual fair value could easily be closer to 1800 or more.  And fair value grows each year as the economy grows.  The U.S. economy is still growing steadily.  So 2500 for the S&P 500 may be quite far from “bubble” territory.  In fact, the market may be fairly valued—if not now, then in 5-10 years.

Furthermore, always bear in mind that no one can predict the stock market.  This has not only been observed by Graham.  But it’s also been pointed out by Peter Lynch, Seth Klarman, Henry Singleton, and Warren Buffett.  Peter Lynch is one of the best investors.  Klarman is even better.  Buffett is arguably the best.  And Singleton was even smarter than Buffett.

In a word, history strongly demonstrates that no one has ever been able to predict the stock market with any sort of reliability.

(Illustration by Maxim Popov)

Peter Lynch:

Nobody can predict interest rates, the future direction of the economy, or the stock market.  Dismiss all such forecasts and concentrate on what’s actually happening to the companies in which you’ve invested.

Seth Klarman:

In reality, no one knows what the market will do; trying to predict it is a waste of time, and investing based upon that prediction is a speculative undertaking.

Now, every year there are “pundits” who make predictions about the stock market.  Therefore, as a matter of pure chance, there will always be people in any given year who are “right.”  But there’s zero evidence that any of those who were “right” at some point in the past have been correct with any sort of reliability.

Howard Marks has asked: of those who correctly predicted the bear market in 2008, how many of them predicted the recovery in 2009 and since then?  The answer: very few.  Marks points out that most of those who got 2008 right were already disposed to bearish views in general.  So when a bear market finally came, they were “right,” but the vast majority missed the recovery starting in 2009.

There are always naysayers making bearish predictions.  But anyone who owned an S&P 500 index fund from 2007 to present (Sept. 2017) would have done dramatically better than most of those who listened to naysayers.  Buffett:

Ever-present naysayers may prosper by marketing their gloomy forecasts.  But heaven help them if they act on the nonsense they peddle.

Buffett himself made a 10-year wager against a group of talented hedge fund (and fund of hedge fund) managers.  With only a few months left until the conclusion of the bet, Buffett’s investment in a Vanguard S&P 500 index fund has roughly quadrupled the performance of the hedge funds: http://boolefund.com/warren-buffett-jack-bogle/

Some very able investors have stayed largely in cash since 2011-2012.  The S&P 500 Index has more than doubled since then.  Moreover, many have tried to short the U.S. stock market since 2011-2012.  Some are down 50 percent, while the S&P 500 Index has more than doubled.  The net result of that combination is to be at only 20-25% of the S&P 500’s current value.

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, never relied on financial forecasts—despite operating in a secular bear market from 1968 to 1982:

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.

Warren Buffett puts it best:

  • Charlie and I never have an opinion on the market because it wouldn’t be any good and it might interfere with the opinions we have that are good.
  • We will continue to ignore political and economic forecasts, which are an expensive distraction for many investors and businessmen.
  • Market forecasters will fill your ear but never fill your wallet.
  • Forecasts may tell you a great deal about the forecaster; they tell you nothing about the future.
  • Stop trying to predict the direction of the stock market, the economy, interest rates, or elections.
  • [On economic forecasts:] Why spend time talking about something you don’t know anything about?  People do it all the time, but why do it?
  • I don’t invest a dime based on macro forecasts.

Another good example of a “can’t lose” investment idea that has turned out not to be right:  shorting the Japanese yen.  Many macro experts have been quite certain that the Japanese yen versus the U.S. dollar would eventually exceed 200.  They thought this would have happened years ago.  Some called it the “trade of the decade.”  But the yen versus U.S. dollar is still around 110.  A simple S&P 500 index fund appears to be doing far better than the “trade of the decade.”

(Illustration by Shalom3)

Some have tried to short Japanese government bonds (JGB’s), rather than shorting the yen currency.  But that hasn’t worked for decades.  In fact, shorting JGB’s has become known as the widowmaker trade.

Seth Klarman on humility:

In investing, certainty can be a serious problem, because it causes one not to reassess flawed conclusions.  Nobody can know all the facts.  Instead, one must rely on shreds of evidence, kernels of truth, and what one suspects to be true but cannot prove.

Klarman on the vital importance of doubt:

It is much harder psychologically to be unsure than to be sure;  certainty builds confidence, and confidence reinforces certainty.  Yet being overly certain in an uncertain, protean, and ultimately unknowable world is hazardous for investors.  To be sure, uncertainty breeds doubt, which can be paralyzing.  But uncertainty also motivates diligence, as one pursues the unattainable goal of eliminating all doubt.  Unlike premature or false certainty, which induces flawed analysis and failed judgments, a healthy uncertainty drives the quest for justifiable conviction.

My own painful experiences:  shorting the U.S. stock market and shorting the Japanese yen.  In each case, I believed that the evidence was overwhelming.  By far the biggest mistake I’ve ever made was shorting the U.S. stock market in 2011-2013.  At the time, I agreed with Russell Napier’s arguments.  I was completely wrong.

After that, I shorted the Japanese yen because I was convinced the argument was virtually irrefutable.  Wrong.  Perhaps the yen will collapse some day, but if it’s 10-20 years in the future—or even later—then an index fund or a quantitative value fund would be a far better and safer investment.

Spencer Davidson:

Over a long career you learn a certain humility and are quicker to attribute success to luck rather than your own brilliance.  I think that makes you a better investor, because you’re less apt to make the big mistake and you’re probably quicker to capitalize on good fortune when it shines upon you.

Jeffrey Bronchick:

It’s important not to get carried away with yourself when times are good, and to be able to admit your mistakes and move on when they’re not so good.  If you are intellectually honest—and not afraid to be visibly and sometimes painfully judged by your peers—investing is not work, it’s fun.

Patiently waiting for pessimism or temporary bad news to create low stock prices (some place), and then buying stocks well below probable intrinsic value, does not require genius in general.  But it does require the humility to focus only on areas where you can do well.  As Warren Buffett has remarked:

What counts for most people in investing is not how much they know, but rather how realistically they define what they don’t know.

 

COURAGE

(Courage concept by Travelling-light)

Humility is essential for success in investing.  But you also need the courage to think and act independently.  You have to be able to develop an investment thesis based on the facts and good reasoning without worrying if many others disagree.  Most of the best value investments are contrarian, meaning that your view differs from the consensus.  Ben Graham:

In the world of securities, courage becomes the supreme virtue after adequate knowledge and a tested judgment are at hand.

Graham again:

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

Or as Carlo Cannell says:

Going against the grain is clearly not for everyone—and it doesn’t tend to help you in your social life—but to make the really large money in investing, you have to have the guts to make the bets that everyone else is afraid to make.

Joel Greenblatt identifies two chief reasons why contrarian value investing is hard:

Value investing strategies have worked for years and everyone’s known about them.  They continue to work because it’s hard for people to do, for two main reasons.  First, the companies that show up on the screens can be scary and not doing so well, so people find them difficult to buy.  Second, there can be one-, two- or three-year periods when a strategy like this doesn’t work.  Most people aren’t capable of sticking it out through that.

Contrarian value investing requires buying what is out-of-favor, neglected, or hated.  It also requires the ability to endure multi-year periods of trailing the market, which most investors just can’t do.  Furthermore, while you’re buying what everyone hates and while you’re trailing the market, you also have to put up with people calling you an idiot.  In a word, you must have the ability to suffer.  Eveillard:

If you are a value investor, you’re a long-term investor.  If you are a long-term investor, you’re not trying to keep up with a benchmark on a short-term basis.  To do that, you accept in advance that every now and then you will lag behind, which is another way of saying you will suffer.  That’s very hard to accept in advance because, the truth is, human nature shrinks from pain.  That’s why not so many people invest this way.  But if you believe as strongly as I do that value investing not only makes sense, but that it works, there’s really no credible alternative.

 

CIGAR-BUTT’S

(Photo by Leung Cho Pan)

Warren Buffett has remarked that buying baskets of statistically cheap cigar-butt’s—50-cent dollars—is a more dependable way to generate good returns than buying high-quality businesses.  Rich Pzena perhaps expressed it best:

When I talk about the companies I invest in, you’ll be able to rattle off hundreds of bad things about them—but that’s why they’re cheap!  The most common comment I get is ‘Don’t you read the paper?’  Because if you read the paper, there’s no way you’d buy these stocks.

They’re priced where they are for good reason, but I invest when I believe the conditions that are causing them to be priced that way are probably not permanent.  By nature, you can’t be short-term oriented with this investment philosophy.  If you’re going to worry about short-term volatility, you’re just not going to be able to buy the cheapest stocks.  With the cheapest stocks, the outlooks are uncertain.

Many investors incorrectly assume that high growth in the past will continue into the future, or that a high-quality company is automatically a good investment.  Behavioral finance expert and value investor James Montier:

There’s a great chapter [in Dan Ariely’s Predictably Irrational] about the ways in which we tend to misjudge price and use it as an indicator of something or other.  That links back to my whole thesis that the most common error we as investors make is overpaying for the hope of growth.  Dan did an experiment involving wine, in which he told people, ‘Here’s a $10 bottle of wine and here’s a $90 bottle of wine.  Please rate them and tell me which tastes better.’  Not surprisingly, nearly everyone thought the $90 wine tasted much better than the $10 wine.  The only snag was that the $90 wine and the $10 wine were actually the same $10 wine.

 

OPPORTUNITIES IN MICRO CAPS

(Illustration by Mopic)

Micro-cap stocks are the most inefficiently priced.  That’s because, for most professional investors, assets under management are too large.  These investors cannot even consider micro caps.  The Boole Microcap Fund is designed to take advantage of this inefficiency: http://boolefund.com/best-performers-microcap-stocks/

James Vanasek on the opportunity in micro caps:

We’ll invest in companies with up to $1 billion or so in market cap, but have been most successful in ideas that start out in the $50 million to $300 million range.  Fewer people are looking at them and the industries the companies are in can be quite stable.  Given that, if you find a company doing well, it’s more likely it can sustain that advantage over time.

Because very few professional investors can even contemplate investing in micro caps, there’s far less competition.  Carlo Cannell:

My basic premise is that the efficient markets hypothesis breaks down when there is inconsistent, imperfect dissemination of information.  Therefore it makes sense to direct our attention to the 14,000 or so publicly traded companies in the U.S. for which there is little or no investment sponsorship by Wall Street, meaning three or fewer sell-side analysts who publish research…

You’d be amazed how little competition we have in this neglected universe.  It is just not in the best interest of the vast majority of the investing ecosphere to spend 10 minutes on the companies we spend our lives looking at.

Robert Robotti adds:

We focus on smaller-cap companies that are largely ignored by Wall Street and face some sort of distress, of their own making or due to an industry cycle.  These companies are more likely to be inefficiently priced and if you have conviction and a long-term view they can produce not 20 to 30 percent returns, but multiples of that.

 

PREDICTABLE HUMAN IRRATIONALITY

Value investors recognize that the stock market is not always efficient, largely because humans are often less than fully rational.  As Seth Klarman explains:

Markets are inefficient because of human nature—innate, deep-rooted, permanent.  People don’t consciously choose to invest with emotion—they simply can’t help it.

Quantitative value investor James O’Shaugnessy:

Because of all the foibles of human nature that are well documented by behavioral research—people are always going to overshoot and undershoot when pricing securities.  A review of financial markets all the way back to the South Sea Company nearly 300 years ago proves this out.

Bryan Jacoboski:

The very reason price and value diverge in predictable and exploitable ways is because people are emotional beings.  That’s why the distinguishing attribute among successful investors is temperament rather than brainpower, experience, or classroom training.  They have the ability to be rational when others are not.

Overconfidence is extremely deep-rooted in human psychology.  When asked, the vast majority of us rate ourselves as above average across a wide variety of dimensions such as looks, smarts, driving skill, academic ability, future well-being, and even luck (!).

In a field such as investing, it’s vital to become aware of our natural overconfidence.  Charlie Munger likes this quote from Demosthenes:

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

But becoming aware of our overconfidence is usually not enough.  We also have to develop systems—such as checklists – that can automatically reduce both the frequency and the severity of mistakes.

(Image by Aleksey Vanin)

Charlie Munger reminds value investors not only to develop and use a checklist, but also to follow the advice of mathematician Carl Jacobi:

Invert, always invert.

In other words, instead of thinking about how to succeed, Munger advises value investors to figure out all the ways you can fail.  This is a powerful concept in a field like investing, where overconfidence frequently causes failure.  Munger:

It is occasionally possible for a tortoise, content to assimilate proven insights of his best predecessors, to outrun hares which seek originality or don’t wish to be left out of some crowd folly which ignores the best work of the past.  This happens as the tortoise stumbles on some particularly effective way to apply the best previous work, or simply avoids the standard calamities.  We try more to profit by always remembering the obvious than from grasping the esoteric.  It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent.

When it comes to checklists, it’s helpful to have a list of cognitive biases.  Here’s my list: http://boolefund.com/cognitive-biases/

Munger’s list is more comprehensive: http://boolefund.com/the-psychology-of-misjudgment/

Recency bias is one of the most important biases to be aware of as an investor.  Jed Nussdorf:

It is very hard to avoid recency bias, when what just happened inordinately informs your expectation of what will happen next.  One of the best things I’ve read on that is The Icarus Syndrome, by Peter Beinart.  It’s not about investing, but describes American hubris in foreign policy, in many cases resulting from doing what seemed to work in the previous 10 years even if the setting was materially different or conditions had changed.  One big problem is that all the people who succeed in the recent past become the ones in charge going forward, and they think they have it all figured out based on what they did before.  It’s all quite natural, but can result in some really bad decisions if you don’t constantly challenge your core beliefs.

Availability bias is closely related to recency bias and vividness bias.  You’re at least 15-20 times more likely to be hit by lightning in the United States than to be bitten by shark.  But often people don’t realize this because shark attacks tend to be much more vivid in people’s minds.  Similarly, your odds of dying in a car accident are 1 in 5,000, while your odds of dying in a plane crash are 1 in 11 million.  Nonetheless, many people view flying as more dangerous.

John Dorfman on investors overreacting to recent news:

Investors overreact to the latest news, which has always been the case, but I think it’s especially true today with the Internet.  Information spreads so quickly that decisions get made without particularly deep knowledge about the companies involved.  People also overemphasize dramatic events, often without checking the facts.

 

LONG-TERM TIME HORIZON

(Illustration by Marek)

Because so many investors worry and think about the shorter term, value investors continue to gain a large advantage by focusing on the longer term (especially three to five years).  In a year or less, a given stock can do almost anything.  But over a five-year period, a stock tracks intrinsic business value to a large extent.  Jeffrey Ubben:

It’s still true that the biggest players in the public markets—particularly mutual funds and hedge funds—are not good at taking short-term pain for long-term gain.  The money’s very quick to move if performance falls off over short periods of time.  We don’t worry about headline risk—once we believe in an asset, we’re buying more on any dips because we’re focused on the end game three or four years out.

Mario Cibelli:

One of the last great arbitrages left is to be long-term-oriented when there is a large class of shareholders who have no tolerance for short-term setbacks.  So it’s interesting when stocks get beaten-up because a company misses earnings or the market reacts to a short-term business development.  It’s crazy to me when someone says something is cheap but doesn’t buy it because they think it won’t go anywhere for the next 6 to 12 months.  We have a pretty high tolerance for taking that pain if we see glory longer term.

Whitney Tilson wrote about a great story that value investor Bill Miller told.  Miller recalled that, early in his career, he was visiting an institutional money manager, to whom he was pitching R.J. Reynolds, then trading at four times earnings.  Miller:

“When I finished, the chief investment officer said: ‘That’s a really compelling case but we can’t own that.  You didn’t tell me why it’s going to outperform the market in the next nine months.’  I said I didn’t know if it was going to do that or not but that there was a very high probability it would do well over the next three to five years.

“He said: ‘How long have you been in this business?  There’s a lot of performance pressure, and performing three to five years down the road doesn’t cut it.  You won’t be in business then.  Clients expect you to perform right now.’

“So I said: ‘Let me ask you, how’s your performance?’

“He said: ‘It’s terrible, that’s why we’re under a lot of performance pressure.’

“I said: ‘If you bought stocks like this three years ago, your performance would be good right now and you’d be buying RJR to help your performance over the next three years.’”

Link: http://www.tilsonfunds.com/Patience%20can%20find%20a%20virtue%20in%20market%20inefficiency-FT-6-9-06.pdf

Many investors are so focused on shorter periods of time (a year or less).  They forget that the value of any business is ALL of its (discounted) future free cash flow, which often means 10-20 years or more.  David Herro:

I would assert the biggest reason quality companies sell at discounts to intrinsic value is time horizon.  Without short-term visibility, most investors don’t have the conviction or courage to hold a stock that’s facing some sort of challenge, either internally or externally generated.  It seems kind of ridiculous, but what most people in the market miss is that intrinsic value is the sum of ALL future cash flows discounted back to the present.  It’s not just the next six months’ earnings or the next year’s earnings.  To truly invest for the long term, you have to be able to withstand underperformance in the short term, and the fact of the matter is that most people can’t.

As Mason Hawkins observes, a company may be lagging now precisely because it’s making longer-term investments that will probably increase business value in the future:

Classic opportunities for us get back to time horizon.  A company reports a bad quarter, which disappoints Wall Street with its 90-day focus, but that might be for explainable temporary reasons or even because the company is making very positive long-term investments in the business.  Many times that investment increases the likely value of the company five years from now, but disappoints people who want the stock up tomorrow.

Whitney George:

We evaluate businesses over a full business cycle and probably our biggest advantage is an ability to buy things when most people can’t because the short-term outlook is lousy or very hard to judge.  It’s a good deal easier to know what’s likely to happen than to know precisely when it’s going to happen.

In general, humans are impatient and often discount multi-year investment gains far too much.  John Maynard Keynes: 

Human nature desires quick results, there is a particular zest in making money quickly, and remoter gains are discounted by the average man at a very high rate.

 

SCREENING AND QUANTITATIVE MODELS

(Word cloud by Arloofs)

Automating of the investment process, including screening, is often more straightforward now than it has been, thanks to enormous advances in computing in the past two decades.

Will Browne:

We often start with screens on all aspects of valuation.  There are characteristics that have been proven over long periods to be associated with above-average rates of return:  low P/Es, discounts to book value, low debt/equity ratios, stocks with recent significant price declines, companies with patterns of insider buying and—something we’re paying a lot more attention to—stocks with high dividend yields.

Stephen Goddard:

Our basic screening process weights three factors equally:  return on tangible capital, the multiple of EBIT to enterprise value, and free cash flow yield.  We rank the universe we’ve defined on each factor individually from most attractive to least, and then combine the rankings and focus on the top 10%.

Carlo Cannell:

[We] basically spend our time trying to uncover the assorted investment misfits in the market’s underbrush that are largely neglected by the investment community.  One of the key metrics we assign to our companies is an analyst ratio, which is simply the number of analysts who follow the company.  The lower the better—as of the end of last year, about 65 percent of the companies in our portfolio had virtually no analyst coverage.

For some time now, it has been clear that simple quant models outperform experts in a wide variety of areas: http://boolefund.com/simple-quant-models-beat-experts-in-a-wide-variety-of-areas/

Quantitative value investor James O’Shaugnessy:

Models beat human forecasters because they reliably and consistently apply the same criteria time after time.  Models never vary.  They are never moody, never fight with their spouse, are never hung over from a night on the town, and never get bored.  They don’t favor vivid, interesting stories over reams of statistical data.  They never take anything personally.  They don’t have egos.  They’re not out to prove anything.  If they were people, they’d be the death of any party.

People on the other hand, are far more interesting.  It’s far more natural to react emotionally or to personalize a problem than it is to dispassionately review broad statistical occurrences—and so much more fun!  It’s much more natural for us to look at the limited set of our personal experiences and then generalize from this small sample to create a rule-of-thumb heuristic.  We are a bundle of inconsistencies, and although this tends to make us interesting, it plays havoc with our ability to successfully invest.

Buffett maintains (correctly) that the vast majority of investors, large or small, should invest in low-cost broad market index funds: http://boolefund.com/quantitative-microcap-value/

If you invest in a quantitative value fund focused on cheap micro caps with improving fundamentals, then you can reasonably expect to do about 7% (+/- 3%) better than the S&P 500 Index over time: http://boolefund.com/best-performers-microcap-stocks/

Will Browne:

When you have a model you believe in, that you’ve used for a long time and which is more empirical than intuitive, sticking with it takes the emotion away when markets are good or bad.  That’s been a central element of our success.  It’s the emotional dimension that drives people to make lousy, irrational decisions.

 

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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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 10, 2017

Warren Buffett:

Investing is simple but not easy.

(Photo by USA International Trade Administration)

There are four simple but important facts that virtually every investor should bear in mind when choosing an investment strategy:

  • A low-cost S&P 500 index fund is likely to outperform at least 90-95% of all investors, net of costs, over 4-5 decades.  For the vast majority of investors, an index fund is the best option.  That’s why Warren Buffett consistently suggests index funds not only to small investors, but also to mega-rich individuals, institutions, and pension funds.
  • You can do a bit better than an index fund over time if you adopt a quantitative value approach.  Properly implemented, this is like a value index and should do at least 1-2% better per year, net of costs, than an S&P 500 index fund.  However, quantitative value sometimes trails the market for years in a row.  If you can’t stick with it during such a period, then it’s better to invest in an S&P 500 index fund.
  • If you’re pondering quantitative value investing, you should also consider quantitative microcap value.  That’s what the Boole Microcap Fund does.  By screening for cheap micro caps with improving fundamentals, you can reasonably expect to outperform the S&P 500 by roughly 7% (+/- 3%) per year on average.  (Compared to the S&P 500, this microcap strategy could do 4% better per year, 10% better, or anything in-between.  There’s a high degree of randomness in investing.  The important thing is to stick with it for at least 5-10 years.)
  • Determining which strategy, or mix of strategies, is best for you requires humility.  The trouble is that, generally, we’re overconfident.  If asked, most of us believe we’re above average across a variety of dimensions such as looks, smarts, driving skill, academic ability, future well-being, and even luck.  (Men suffer from overconfidence more than women, perhaps in part because overconfidence was useful for hunting.)  We also suffer from other cognitive biases, all of which are the result of evolution.

On the topic of overconfidence, Buffett’s partner Charlie Munger likes this quote from Demosthenes:

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

(Charlie Munger at the 2010 Berkshire Hathaway shareholders meeting.  Photo by Nick Webb)

Let’s consider each point in a bit more detail.

 

INDEX FUNDS

Would you like to do better than approximately 90-95% of all investors, net of costs, over the next 4-5 decades?  It is surprisingly simple to achieve this result:  invest in a low-cost broad market index fund.  That’s why Warren Buffett, arguably the best investor ever, consistently recommends such an index fund to small investors and also to mega-rich individuals, institutions, and pension funds.

If your investment time horizon is measured in decades, a low-cost index fund is the obvious choice.  Passive investors on the whole will match the market.  Therefore, active investors will also match the market, before costs.  After costs, active investors (on the whole) will trail the market by 2-3% per year.  (John Bogle has done a terrific job telling this simple truth for a long time.)

  • 2-3% per year really adds up over the course of decades.  For example, if the average active approach returns 6.5% per year (net of costs) over the next 30 years, then $1 million will become $6.61 million.  If an S&P 500 index fund returns 9% per year (net) over the next 30 years, the same $1 million will become $13.27 million, twice as much.  (Moreover, the index fund is well-diversified across 500 American businesses.)

Even over the course of one decade, a low-cost broad market index fund can produce excellent results.  Warren Buffett’s 10-year bet against Protégé Partners demonstrates clearly that a simple index fund can beat the vast majority of all investors: http://boolefund.com/warren-buffett-jack-bogle/

After nine years, a group of a few hundred hedge funds – managed by intelligent, honest people who are highly incentivized to maximize their performance – is up a bit over 22%, net of costs.  Buffett’s investment in a Vanguard S&P 500 index fund is up 85.4%, net of costs.  That’s 7.1% per year for the index fund versus 2.2% per year for highly intelligent hedge fund (and fund of hedge fund) managers.

This illustrates how investing is simple but not easy.  Even if you restrict your examination to the most intelligent 10% of all investors, the long-term results are the same:  the vast majority of these investors will trail an S&P 500 index fund, net of costs, over time.

In the 2016 Berkshire Hathaway Letter to Shareholders, Buffett writes that, in his own lifetime, he identified – early on – ten investors he thought would beat the market over the long term.  Buffett was right about these ten.  But that’s only ten out of hundreds, or even thousands, of similarly intelligent investors.  Buffett:

There are no doubt many hundreds of people – perhaps thousands – whom I have never met and whose abilities would equal those of the people I’ve identified.  The job, after all, is not impossible.  The problem simply is that the great majority of managers who attempt to over-perform will fail. 

See: http://berkshirehathaway.com/letters/2016ltr.pdf

In a nutshell, you can do better than about 90-95% of all investors over 4-5 decades by investing in an S&P 500 index fund.  This is purely a function of costs, which average 2-3% per year for active approaches.  Therefore, for the vast majority of investors, whether large or small, you should follow Warren Buffett’s advice:  simply invest in American business by investing in a low-cost broad market index fund.

(BNSF, owned by Berkshire Hathaway.  Photo by Winnie Chao.)

 

VALUE INDEX

A seminal paper on quantitative deep value investing is by Josef Lakonishok, Andrei Shleifer, and Robert Vishny (1994), “Contrarian Investment, Extrapolation, and Risk.”  Link: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf

LSV (Lakonishok, Schleifer, and Vishny) were so convinced by their research that they launched LSV Asset Management, which currently manages $105 billion.  LSV’s quantitative deep value strategies have beaten their respective benchmark indices by at least 1-2% per year over time.

 

QUANTITATIVE MICROCAP VALUE

(Illustration by Madmaxer.)

Check out 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

(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, microcap annual returns 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.  Still, 3% more per year than large caps really adds up over the course of decades.

  • Most professional investors ignore micro caps as too small for their portfolios.  This causes many micro caps to get very cheap.  And that’s why an equal weighted strategy – applied to micro caps – tends to work well.

Value Screen: +2-3%

By adding a value screen – e.g., low EV/EBIT or low P/E – to a microcap strategy, it is possible to add 2-3% per year.  To maximize the odds of achieving this additional margin of outperformance, you should adopt a quantitative approach.

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 the course of several decades, 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 a low-cost S&P 500 index fund.

 

COGNITIVE BIASES

(Illustration by Alain Lacroix.)

Human intuition often works remarkably well.  But when a good decision requires careful reasoning – using logic, math, or statistics – our intuition causes systematic errors.  I wrote about cognitive biases here: http://boolefund.com/cognitive-biases/

Munger’s treatment of misjudgment is more comprehensive: http://boolefund.com/the-psychology-of-misjudgment/

 

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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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.

Deep Value: Profiting from Mean Reversion

(Image:  Zen Buddha Silence by Marilyn Barbone.)

August 27, 2017

The essence of deep value investing is systematically buying stocks at low multiples in order to profit from future mean reversion.  Sometimes it seems that there are misconceptions about deep value investing.

  • First, deep value stocks have on occasion been called cheap relative to future growth.  But it’s often more accurate to say that deep value stocks are cheap relative to normalized earnings or cash flows.
  • Second, the cheapness of deep value stocks has often been said to be relative to “net tangible assets.”  However, in many cases, even including stocks at a discount to tangible assets, mean reversion relates to the future normalized earnings or cash flows that the assets can produce.
  • Third, typically more than half of deep value stocks underperform the market.  And deep value stocks are more likely to be distressed than average stocks.  Do these facts imply that a deep value investment strategy is riskier than average?  No…

Have you noticed these misconceptions?  I’m curious to hear your take.  Please let me know.

Here are the sections in this blog post:

  • Introduction
  • Mean Reversion as “Return to Normal” instead of “Growth”
  • Revenues, Earnings, Cash Flows, NOT Asset Values
  • Is Deep Value Riskier?
  • A Long Series of Favorable Bets
  • “Cigar Butt’s” vs. See’s Candies
  • Microcap Cigar Butt’s

 

INTRODUCTION

Deep value stocks tend to fit two criteria:

  • Deep value stocks trade at depressed multiples.
  • Deep value stocks have depressed fundamentals – they have generally been doing terribly in terms of revenues, earnings, or cash flows, and often the entire industry is doing poorly.

The essence of deep value investing is systematically buying stocks at low multiples in order to profit from future mean reversion.

  • Low multiples include low P/E (price-to-earnings), low P/B (price-to-book), low P/CF (price-to-cash flow), and low EV/EBIT (enterprise value-to-earnings before interest and taxes).
  • Mean reversion implies that, in general, deep value stocks are underperforming their economic potential.  On the whole, deep value stocks will experience better future economic performance than is implied by their current stock prices.

If you look at deep value stocks as a group, it’s a statistical fact that many will experience better revenues, earnings, or cash flows in the future than what is implied by their stock prices.  This is due largely to mean reversion.  The future economic performance of these deep value stocks will be closer to normal levels than their current economic performance.

Moreover, the stock price increases of the good future performers will outweigh the languishing stock prices of the poor future performers.  This causes deep value stocks, as a group, to outperform the market over time.

Two important notes:

  1. Generally, for deep value stocks, mean reversion implies a return to more normal levels of revenues, earnings, or cash flows.  It does not often imply growth above and beyond normal levels.
  2. For most deep value stocks, mean reversion relates to future economic performance and not to tangible asset value per se.

(1) Mean Reversion as Return to More Normal Levels

One of the best papers on deep value investing is by Josef Lakonishok, Andrei Shleifer, and Robert Vishny (1994), “Contrarian Investment, Extrapolation, and Risk.”  Link: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf

LSV (Lakonishok, Schleifer, and Vishny) correctly point out that deep value stocks are better identified by using more than one multiple.  LSV Asset Management currently manages $105 billion using deep value strategies that rely simultaneously on several metrics for cheapness, including low P/E and low P/CF.

  • In Quantitative Value (Wiley, 2012), Tobias Carlisle and Wesley Gray find that low EV/EBIT outperformed every other measure of cheapness, including composite measures.
  • However, James O’Shaughnessy, in What Works on Wall Street (McGraw-Hill, 2011), demonstrates – with great thoroughness – that, since the mid-1920’s, composite approaches (low P/S, P/E, P/B, EV/EBITDA, P/FCF) have been the best performers.
  • Any single metric may be more easily arbitraged away by a powerful computerized approach.  Walter Schloss once commented that low P/B was working less well because many more investors were using it.  (In recent years, low P/B hasn’t worked.)

LSV explain why mean reversion is the essence of deep value investing.  Investors, on average, are overly pessimistic about stocks at low multiples.  Investors understimate the mean reversion in future economic performance for these out-of-favor stocks.

However, in my view, the paper would be clearer if it used (in some but not all places) “return to more normal levels of economic performance” in place of “growth.”  Often it’s a return to more normal levels of economic performance – rather than growth above and beyond normal levels – that defines mean reversion for deep value stocks.

(2) Revenues, Earnings, Cash Flows NOT Net Asset Values

Buying at a low price relative to tangible asset value is one way to implement a deep value investing strategy.  Many value investors have successfully used this approach.  Examples include Ben Graham, Walter Schloss, Peter Cundill, John Neff, and Marty Whitman.

Warren Buffett used this approach in the early part of his career.  Buffett learned this method from his teacher and mentor, Ben Graham.  Graham called this the “net-net” approach.  You take net working capital minus ALL liabilities.  If the stock price is below that level, and if you buy a basket of such “net-net’s,” you can’t help but do well over time.  These are extremely cheap stocks, on average.  (The only catch is that there must be enough net-net’s in existence to form a basket, which is not always the case.)

Buffett on “cigar butts”:

…I call it the cigar butt approach to investing.  You walk down the street and you look around for a cigar butt someplace.  Finally you see one and it is soggy and kind of repulsive, but there is one puff left in it.  So you pick it up and the puff is free – it is a cigar butt stock.  You get one free puff on it and then you throw it away and try another one.  It is not elegant.  But it works.  Those are low return businesses.

Link: http://intelligentinvestorclub.com/downloads/Warren-Buffett-Florida-Speech.pdf

But most net-net’s are NOT liquidated.  Rather, there is mean reversion in their future economic performance – whether revenues, earnings, or cash flows.  That’s not to say there aren’t some bad businesses in this group.  For net-net’s, when economic performance returns to more normal levels, typically you sell the stock.  You don’t (usually) buy and hold net-net’s.

Sometimes net-net’s are acquired.  But in many of these cases, the acquirer is focused mainly on the earnings potential of the assets.  (Non-essential assets may be sold, though.)

In sum, the specific deep value method of buying at a discount to net tangible assets has worked well in general ever since Graham started doing it.  And net tangible assets do offer additional safety.  That said, when these particular cheap stocks experience mean reversion, often it’s because revenues, earnings, or cash flows return to “more normal” levels.  Actual liquidation is rare.

 

IS DEEP VALUE RISKIER?

According to a study done by Joseph Piotroski from 1976 to 1996 – discussed below – although a basket of deep value stocks clearly beats the market over time, only 43% of deep value stocks outperform the market, while 57% underperform.  By comparison, an average stock has a 50% chance of outperforming the market and a 50% chance of underperforming.

Let’s assume that the average deep value stock has a 57% chance of underperforming the market, while an average stock has only a 50% chance of underperforming.  This is a realistic assumption not only because of Piotroski’s findings, but also because the average deep value stock is more likely to be distressed (or to have problems) than the average stock.

Does it follow that the reason deep value investing does better than the market over time is that deep value stocks are riskier than average stocks?

It is widely accepted that deep value investing does better than the market over time.  But there is still disagreement about how risky deep value investing is.  Strict believers in the EMH (Efficient Markets Hypothesis) – such as Eugene Fama and Kenneth French – argue that value investing must be unambiguously riskier than simply buying an S&P 500 Index fund.  On this view, the only way to do better than the market over time is by taking more risk.

Now, it is generally true that the average deep value stock is more likely to underperform the market than the average stock.  And the average deep value stock is more likely to be distressed than the average stock.

But LSV show that a deep value portfolio does better than an average portfolio, especially during down markets.  This means that a basket of deep value stocks is less risky than a basket of average stocks.

  • A “portfolio” or “basket” of stocks refers to a group of stocks.  Statistically speaking, there must be at least 30 stocks in the group.  In the case of LSV’s study – like most academic studies of value investing – there are hundreds of stocks in the deep value portfolio.  (The results are similar over time whether you have 30 stocks or hundreds.)

Moreover, a deep value portfolio only has slightly more volatility than an average portfolio, not nearly enough to explain the significant outperformance.  In fact, when looked at more closely, deep value stocks as a group have slightly more volatility mainly because of upside volatility – relative to the broad market – rather than because of downside volatility.  This is captured not only by the clear outperformance of deep value stocks as a group over time, but also by the fact that deep value stocks do much better than average stocks in down markets.

Deep value stocks, as a group, not only outperform the market, but are less risky.  Ben Graham, Warren Buffett, and other value investors have been saying this for a long time.  After all, the lower the stock price relative to the value of the business, the less risky the purchase, on average.  Less downside implies more upside.

 

A LONG SERIES OF FAVORABLE BETS

Let’s continue to assume that the average deep value stock has a 57% chance of underperforming the market.  And the average deep value stock has a greater chance of being distressed than the average stock.  Does that mean that the average individual deep value stock is riskier than the average stock?

No, because the expected return on the average deep value stock is higher than the expected return on the average stock.  In other words, on average, a deep value stock has more upside than downside.

Put very crudely, in terms of expected value:

[(43% x upside) – (57% x downside)] > [avg. return]

43% times the upside, minus 57% times the downside, is greater than the return from the average stock (or from the S&P 500 Index).

The crucial issue relates to making a long series of favorable bets.  Since we’re talking about a long series of bets, let’s again consider a portfolio of stocks.

  • Recall that a “portfolio” or “basket” of stocks refers to a group of at least 30 stocks.

A portfolio of average stocks will simply match the market over time.  That’s an excellent result for most investors, which is why most investors should just invest in index funds: http://boolefund.com/warren-buffett-jack-bogle/

A portfolio of deep value stocks will, over time, do noticeably better than the market.  Year in and year out, approximately 57% of the deep value stocks will underperform the market, while 43% will outperform.  But the overall outperformance of the 43% will outweigh the underperformance of the 57%, especially over longer periods of time.  (57% and 43% are used for illustrative purposes here.  The actual percentages vary.)

Say that you have an opportunity to make the same bet 1,000 times in a row, and that the bet is as follows:  You bet $1.  You have a 60% chance of losing $1, and a 40% chance of winning $2.  This is a favorable bet because the expected value is positive: 40% x $2 = $0.80, while 60% x $1 = $0.60.  If you made this bet repeatedly over time, you would average $0.20 profit on each bet, since $0.80 – $0.60 = $0.20.

If you make this bet 1,000 times in a row, then roughly speaking, you will lose 60% of them (600 bets) and win 40% of them (400 bets).  But your profit will be about $200.  That’s because 400 x $2 = $800, while 600 x $1 = $600.  $800 – $600 = $200.

Systematically investing in deep value stocks is similar to the bet just described.  You may lose 57% of the bets and win 43% of the bets.  But over time, you will almost certainly profit because the average upside is greater than the average downside.  Your expected return is also higher than the market return over the long term.

 

“CIGAR BUTT’S” vs. SEE’S CANDIES

In his 1989 Letter to Shareholders, Buffett writes about his “Mistakes of the First Twenty-Five Years,” including a discussion of “cigar butt” (deep value) investing:

My first mistake, of course, was in buying control of Berkshire.  Though I knew its business – textile manufacturing – to be unpromising, I was enticed to buy because the price looked cheap.  Stock purchases of that kind had proved reasonably rewarding in my early years, though by the time Berkshire came along in 1965 I was becoming aware that the strategy was not ideal. 

If you buy a stock at a sufficiently low price, there will usually be some hiccup in the fortunes of the business that gives you a chance to unload at a decent profit, even though the long-term performance of the business may be terrible.  I call this the ‘cigar butt’ approach to investing.  A cigar butt found on the street that has only one puff left in it may not offer much of a smoke, but the ‘bargain purchase’ will make that puff all profit. 

Link: http://www.berkshirehathaway.com/letters/1989.html

Buffett has made it clear that cigar butt (deep value) investing does work.  In fact, fairly recently, Buffett bought at basket of cigar butts in South Korea.  The results were excellent.  But he did this in his personal portfolio.

This highlights a major reason why Buffett evolved from investing in cigar butts to investing in higher quality businesses:  size of investable assets.  When Buffett was managing a few hundred million dollars or less, which includes when he managed an investment partnership, Buffett achieved outstanding results in part by investing in cigar butts.  But when investable assets swelled into the billions of dollars at Berkshire Hathaway, Buffett began investing in higher quality companies.

  • Cigar butt investing works best for micro caps.  But micro caps won’t move the needle if you’re investing many billions of dollars.

The idea of investing in higher quality companies is simple:  If you can find a business with a sustainably high ROE – based on a sustainable competitive advantage – and if you can hold that stock for a long time, then your returns as an investor will approximate the ROE (return on equity).  This assumes that the company can continue to reinvest all of its earnings at the same ROE, which is extremely rare when you look at multi-decade periods.

  • The quintessential high-quality business that Buffett and Munger purchased for Berkshire Hathaway is See’s Candies.  They paid $25 million for $8 million in tangible assets in 1972.  Since then, See’s Candies has produced over $2 billion in (pre-tax) earnings, while only requiring a bit over $40 million in reinvestment.
  • See’s turns out more than $80 million in profits each year.  That’s over 100% ROE (return on equity), which is extraordinary.  But that’s based mostly on assets in place.  The company has not been able to reinvest most of its earnings.  Instead, Buffett and Munger have invested the massive excess cash flows in other good opportunities – averaging over 20% annual returns on these other investments (for most of the period from 1972 to present).

Furthermore, buying and holding stock in a high-quality business brings enormous tax advantages over time because you never have to pay taxes until you sell.  Thus, as a high-quality business – with sustainably high ROE – compounds value over many years, a shareholder who never sells receives the maximum benefit of this compounding.

Yet it’s extraordinarily difficult to find a business that can sustain ROE at over 20% – including reinvested earnings – for decades.  Buffett has argued that cigar butt (deep value) investing produces more dependable results than investing exclusively in high-quality businesses.  Very often investors buy what they think is a higher-quality business, only to find out later that they overpaid because the future performance does not match the high expectations that were implicit in the purchase price.  Indeed, this is what LSV show in their famous paper (discussed above) in the case of “glamour” (or “growth”) stocks.

 

MICROCAP CIGAR BUTTS

Buffett has said that you can do quite well as an investor, if you’re investing smaller amounts, by focusing on cheap micro caps.  In fact, Buffett has maintained that he could get 50% per year if he could invest only in cheap micro caps.

Investing systematically in cheap micro caps can often lead to higher long-term results than the majority of approaches that invest in high-quality stocks.

First, micro caps, as a group, far outperform every other category.  See the historical performance here: http://boolefund.com/best-performers-microcap-stocks/

Second, cheap micro caps do even better.  Systematically buying at low multiples works over the course of time, as clearly shown by LSV and many others.

Finally, if you apply the Piotroski F-Score to screen cheap micro caps for improving fundamentals, performance is further boosted:  The biggest improvements in performance are concentrated in cheap micro caps with no analyst coverage.  See: http://boolefund.com/joseph-piotroski-value-investing/

 

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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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.

Cognitive Biases

(Image:  Zen Buddha Silence by Marilyn Barbone.)

August 20, 2017

In the great book Thinking, Fast and Slow (2011), Daniel Kahneman explains in detail two different ways of thinking that human beings use.  Kahneman refers to them as System 1 and System 2, which he defines as follows:

System 1:   Operates automatically and quickly, with little or no effort or sense of voluntary control.  Makes instinctual or intuitive decisions – typically based on heuristics.

System 2:   Allocates attention to the effortful mental activities that demand it, including complex computations involving logic, math, or statistics.  The operations of System 2 are often associated with the subjective experience of agency, choice, and concentration.

Heuristics are simple rules we use – via System 1 – to form judgments or make decisions.  Heuristics are mental shortcuts whereby we simplify a complex situation in order to jump to a quick conclusion.

Most of the time, heuristics work well.  We can immediately notice a shadow in the grass, alerting us to the possible presence of a lion.  And we can automatically read people’s faces, drive a car on an empty road, do easy math, or understand simple language.  (For more on System 1, see the last section of this blog post.)

However, if we face a situation that requires the use of logic, math, or statistics to reach a good judgment or decision, heuristics lead to systematic errors.  These errors are cognitive biases.

Let’s examine some of the main cognitive biases:

  • anchoring effect
  • availability bias, vividness bias, recency bias
  • confirmation bias
  • hindsight bias
  • overconfidence
  • narrative fallacy
  • information and overconfidence
  • self-attribution bias

 

ANCHORING EFFECT

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.

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 7000 on average report 6762 doctors, while those with telephone numbers below 2000 arrived at an average 2270 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” (page 119, 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.  (Montier, page 120)

 

AVAILABILITY BIAS, VIVIDNESS BIAS, RECENCY BIAS

availability bias:   people tend to overweight evidence that comes easily to mind.

Related to the availability bias are vividness bias and recency bias.  People typically overweight facts that are vivid (e.g., plane crashes or shark attacks).   People also overweight facts that are recent (partly because they are more vivid).

Note:  It’s also natural for people to assume that hard-won evidence or insight must be worth more.  But often that’s not true, either.

 

CONFIRMATION BIAS

confirmation bias:   people tend to search for, remember, and interpret information in a way that confirms their pre-existing beliefs or hypotheses.

Confirmation bias makes it quite difficult for many people to improve upon or supplant their existing beliefs or hypotheses.   This bias also tends to make people overconfident about existing beliefs or hypotheses, since all they can see are supporting data.

We know that our System 1 (intuition) often errors when it comes to forming and testing hypotheses. First of all, System 1 always forms a coherent story (including causality), irrespective of whether there are truly any logical connections at all among various things in experience.  Furthermore, when System 1 is facing a hypothesis, it automatically looks for confirming evidence.

But even System 2 – the logical and mathematical system that humans possess and can develop – by nature uses a positive test strategy:

A deliberate search for confirming evidence, known as positive test strategy, is also how System 2 tests a hypothesis.  Contrary to the rules of philosophers of science, who advise testing hypotheses by trying to refute them, people (and scientists, quite often) seek data that are likely to be compatible with the beliefs they currently hold.  (page 81, Kahneman)

Thus, the habit of always looking for disconfirming evidence of our hypotheses – especially our “best-loved hypotheses” – is arguably the most important intellectual habit we could develop in the never-ending search for wisdom and knowledge.

Charles Darwin is a wonderful model for people in this regard.  Darwin was far from being a genius in terms of IQ.  Yet Darwin trained himself always to search for facts and evidence that would contradict his hypotheses.  Charlie Munger explains in “The Psychology of Human Misjudgment” (see Poor Charlie’s Alamanack: The Wit and Wisdom of Charles T.  Munger, expanded 3rd edition):

One of the most successful users of an antidote to first conclusion bias was Charles Darwin.  He trained himself, early, to intensively consider any evidence tending to disconfirm any hypothesis of his, more so if he thought his hypothesis was a particularly good one… He provides a great example of psychological insight correctly used to advance some of the finest mental work ever done. 

 

HINDSIGHT BIAS

Hindsight bias:   the tendency, after an event has occurred, to see the event as having been predictable, despite there having been little or no objective basis for predicting the event prior to its occurrence.

Hindsight bias is also called the “knew-it-all-along effect” or “creeping determinism.”  (See: http://en.wikipedia.org/wiki/Hindsight_bias)

Kahneman writes about hindsight bias as follows:

Your inability to reconstruct past beliefs will inevitably cause you to underestimate the extent to which you were surprised by past events.   Baruch Fischhoff first demonstrated this ‘I-knew-it-all-along’ effect, or hindsight bias, when he was a student in Jerusalem.  Together with Ruth Beyth (another of our students), Fischhoff conducted a survey before President Richard Nixon visited China and Russia in 1972.   The respondents assigned probabilities to fifteen possible outcomes of Nixon’s diplomatic initiatives.   Would Mao Zedong agree to meet with Nixon?   Might the United States grant diplomatic recognition to China?   After decades of enmity, could the United States and the Soviet Union agree on anything significant?

After Nixon’s return from his travels, Fischhoff and Beyth asked the same people to recall the probability that they had originally assigned to each of the fifteen possible outcomes.  The results were clear.  If an event had actually occurred, people exaggerated the probability that they had assigned to it earlier.  If the possible event had not come to pass, the participants erroneously recalled that they had always considered it unlikely.   Further experiments showed that people were driven to overstate the accuracy not only of their original predictions but also of those made by others.   Similar results have been found for other events that gripped public attention, such as the O.J. Simpson murder trial and the impeachment of President Bill Clinton.   The tendency to revise the history of one’s beliefs in light of what actually happened produces a robust cognitive illusion.  (pages 202-3, my emphasis)

Concludes Kahneman:

The sense-making machinery of System 1 makes us see the world as more tidy, simple, predictable, and coherent that it really is.  The illusion that one has understood the past feeds the further illusion that one can predict and control the future.  These illusions are comforting.   They reduce the anxiety we would experience if we allowed ourselves to fully acknowledge the uncertainties of existence.  (page 204-5, my emphasis)

 

OVERCONFIDENCE

Overconfidence is such as widespread cognitive bias among people that Kahneman devotes Part 3 of his book entirely to this topic.  Kahneman says in his introduction:

The difficulties of statistical thinking contribute to the main theme of Part 3, which describes a puzzling limitation of our mind:  our excessive confidence in what we believe we know, and our apparent inability to acknowledge the full extent of our ignorance and the uncertainty of the world we live in.   We are prone to overestimate how much we understand about the world and to underestimate the role of chance in events.   Overconfidence is fed by the illusory certainty of hindsight.   My views on this topic have been influenced by Nassim Taleb, the author of The Black Swan.  (pages 14-5)

Several studies have shown that roughly 90% of drivers rate themselves as above average.  For more on overconfidence, see: https://en.wikipedia.org/wiki/Overconfidence_effect

 

NARRATIVE FALLACY

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.  (page 63-4)

The narrative fallacy is central to many of the biases and misjudgments mentioned by Daniel Kahneman and Charlie Munger.  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.

 

INFORMATION AND OVERCONFIDENCE

In Behavioural Investing, James Montier explains a study done by Paul Slovic (1973).  Eight experienced bookmakers were shown a list of 88 variables found on a typical past performance chart on a horse.  Each bookmaker was asked to rank the piece of information by importance.

Then the bookmakers were given data for 40 past races and asked to rank the top five horses in each race.  Montier:

Each bookmaker was given the past data in increments of the 5, 10, 20, and 40 variables he had selected as most important.  Hence each bookmaker predicted the outcome of each race four times – once for each of the information sets.  For each prediction the bookmakers were asked to give a degree of confidence ranking in their forecast.  (page 136)

RESULTS:

Accuracy was virtually unchanged, regardless of the number of pieces of information the bookmaker was given (5, 10, 20, then 40).

But confidence skyrocketed as the number of pieces of information increased (5, 10, 20, then 40).

This same result has been found in a variety of areas.  As people get more information, the accuracy of their judgments or forecasts typically does not change at all, while their confidence in the accuracy of their judgments or forecasts tends to increase dramatically.

 

SELF-ATTRIBUTION BIAS

self-attribution bias:   people tend to attribute good outcomes to their own skill, while blaming bad outcomes on bad luck.

This ego-protective bias prevents people from recognizing and learning from their mistakes.  This bias also contributes to overconfidence.

 

MORE ON SYSTEM 1

When we are thinking of who we are, we use System 2 to define ourselves.  But, writes Kahneman, System 1 effortlessly originates impressions and feelings that are the main source of the explicit beliefs and deliberate choices of System 2.

Kahneman lists, “in rough order of complexity,” examples of the automatic activities of System 1:

  • Detect that one object is more distant than another.
  • Orient to the source of a sudden sound.
  • Complete the phrase “Bread and…”
  • Make a “disgust face” when shown a horrible picture.
  • Detect hostility in a voice.
  • Answer 2 + 2 = ?
  • Read words on large billboards.
  • Drive a car on an empty road.
  • Find a strong move in chess (if you are a chess master).
  • Understand simple sentences.
  • Recognize that “a meek and tidy soul with a passion for detail” resembles an occupational stereotype.

Kahneman writes that System 1 and System 2 work quite well generally:

The division of labor between System 1 and System 2 is highly efficient:  it minimizes effort and optimizes performance.   The arrangement works well most of the time because System 1 is generally very good at what it does:  its models of familiar situations are accurate, its short-term predictions are usually accurate as well, and its initial reactions to challenges are swift and generally appropriate.

“Thinking fast” usually works fine.  System 1 is remarkably good at what it does, thanks to evolution.  Kahneman:

System 1 is designed to jump to conclusions from little evidence.

However, when we face situations that are unavoidably complex, System 1 systematically jumps to the wrong conclusions.  In these situations, we have to train ourselves to “think slow” and reason our way to a good decision.

For the curious, here’s the most comprehensive list of cognitive biases I’ve seen: https://en.wikipedia.org/wiki/List_of_cognitive_biases

 

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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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.