Emotions and Biases

(Image:  Zen Buddha Silence by Marilyn Barbone.)

April 9, 2017

Meir Statman, an expert in behavioral finance, has written a good book, What Investors Really Want (McGraw-Hill, 2011).

Here is my brief summary of the important points:


enter essay analysis definition see url buy viagra in goa dissertation database http://v-nep.org/classroom/master-theises/04/ https://scfcs.scf.edu/review/domestic-violence-thesis/22/ research paper word count edinburgh uk viagra pages charles linskaill follow joke viagra gifts research paper thesis maker open box coursework go site https://bonusfamilies.com/lecture/speech-free/21/ how to write analysis paper generico viagra soft how to fix my aol email on my iphone https://211ventura.org/choice/essay-rules-writing-numbers/40/ https://nebraskaortho.com/docmed/power-pill-viagra/73/ bu creative writing difference between thesis and dissertation uk https://raseproject.org/treat/viagra-gold-100mg/97/ essay report conclusion bussiness articlesВ https://pacificainexile.org/students/ms-thesis-topics-computer-science/10/ proper way to write an essay title https://chanelmovingforward.com/stories/how-to-write-500-word-essay/51/ https://sigma-instruments.com/medicamento-viagra-para-mujeres-19180/ college essay help seattle show me an example of a report what is thinking UTILITY AND EMOTIONS

Statman argues that investments bring utilitarian benefits, expressive benefits, and emotional benefits.  The utilitarian benefits relate to being able to achieve financial goals, such as financial freedom or the ability to pay for the education of grandchildren.

Expressive benefits can convey to ourselves and others our values and tastes.  For instance, an investor is, in effect, saying, ‘I’m smart and can pick winning investments.’  Emotional benefits relate to how the activity makes you feel.  As Statman notes, Christopher Tsai said about his father Gerald Tsai, Jr. – a pioneer of the go-go funds in the 1960s:  “He loved doing transactions.  He loved the excitement of it.”

Statman tells the story of an engineer who learned that Statman is a professor of finance.  The engineer asked where he could buy the Japanese yen.  Statman asked him why, and the engineer said that the yen would zoom past the dollar based on macroeconomic fundamentals.  Statman replied:

Buying and selling Japanese yen, American stocks, French bonds, and all other investments is not like playing tennis against a practice wall, where you can watch the ball hit the wall and place yourself at just the right spot to hit it back when it bounces.  It is like playing tennis against an opponent you’ve never met before.  Are you faster than your opponent?  Will your opponent fool you by pretending to hit the ball to the left side, only to hit it to the right?  (page ix)

Later, Statman continues:

I tried to dissuade my fellow dinner guest from trading Japanese yen but I have probably failed.  Perhaps I failed to help my fellow dinner guest overcome his cognitive error, learn that trading should be framed as playing tennis against a possibly better player, and refrain from trading.  Or I might have succeeded in helping my fellow guest overcome his cognitive error and yet failed to dissuade him from trading because he wanted the expressive and emotional benefits of the trading game, the fun of playing and the thrill of winning.  (page xiii)

Statman explains that, in many fields of life, emotions are helpful in good decision-making.  Yet when it comes to areas such as investing, emotions tend to be harmful.

There is often a tension between what we should do and what we want to do.  And if we are stressed or fatigued, then it becomes even harder to do what we should do instead of what we want to do.

Moreover, our emotional reactions to changing stock prices generally mislead us.  When stocks are going up, we typically feel more confident and want to own more stocks.  When stocks are going down, we tend to feel less confident and want to own fewer stocks.  But this is exactly the opposite of what we should do if we want to maximize our long-term investment results.



Beat-the-market investors have always been searching for investments with returns higher than risks.  But such investments are much rarer than is commonly supposed.  For every investor who beats the market, another must trail the market.  And that is before fees and expenses.  After fees and expenses, there are very few investors who beat the market over the course of several decades.

Statman mentions a study of stock traders.  Those who traded the most trailed the index by more than 7 percent per year on average.  Those who traded the least trailed the index by only one-quarter of 1 percent.  Furthermore, a study of Swedish investors showed that heavy traders lose, on average, nearly 4 percent of their total financial wealth each year.



Framing means that people can react differently to a particular choice based on how it is presented.  Framing is everywhere in the world of investments.  Statman explains:

Some frames are quick and intuitive, but frames that come to mind quickly and intuitively are not always correct… The beat-the-market frame that comes to mind quickly and intuitively is that of tennis played against a practice wall, but the correct frame is tennis played against a possibly better player.  Incorrect framing of the beat-the-market game is one cognitive error that fools us into believing that beating the market is easy.  (page 18)

Statman has some advice for overcoming the framing error:

It is not difficult to overcome the framing error.  All we need to do is install an app in our minds as we install apps on our iPhones.  When we are ready to trade it would pipe in, asking, ‘Who is the idiot on the other side of the trade?  Have you considered the likelihood that the idiot is you?’  (page 21)

The broader issue (discussed below) is that most of us, by nature, are overconfident in many areas of life, including investing.  Overconfidence is the most widespread cognitive bias that we have.  Using procedures such as a checklist can help reduce errors from overconfidence.  Also, keeping a journal of every investment decision – what the hypothesis is, what the evidence is, and what ended up happening – can help you to improve over time, hopefully reducing cognitive errors such as overconfidence.



Heuristics are mental shortcuts that often work, but sometimes don’t.  There is a good discussion of the representativeness heuristic on Wikipedia: https://en.wikipedia.org/wiki/Representativeness_heuristic

Daniel Kahneman and Amos Tversky defined representativeness as:

the degree to which [an event] (i) is similar in essential characteristics to its parent population, and (ii) reflects the salient features of the process by which it is generated.

When people rely on representativeness to make judgments, they are likely to judge wrongly because the fact that something is more representative does not actually make it more likely.  The key issue is sample size versus base rate.

Many people mistakenly assume that a small sample – even as small as a single example – is representative of the relevant population.  This mistake is called the law of small numbers.

If you have a small sample, you cannot take it as representative of the entire population.  In other words, a small sample may differ significantly from the base rate.  If you have a large enough sample, then by the law of large numbers, you can conclude that the large sample approximates the base rate (the entire population).

For instance, if you flip a coin ten times and get 8 heads, you cannot conclude that flipping the same coin thousands of times will yield approximately 80% heads.  But if you flip a coin ten thousand times and get 5,003 heads, you can conclude that the base rate for heads is 50%.

If a mutual fund manager beats the market five years out of six, we conclude that it must be due to skill even though that is far too short a period for such a conclusion.  By randomness alone, there will be many mutual fund managers who beat the market five years out of six.



Our brains are good at finding patterns.  But when the data are highly random, our brains often find patterns that don’t really exist.

For example, there is no way to time the market.  Yet many investors try to time the market, jumping in and out of stocks.  Nearly everyone who tries market timing ends up trailing a simple index fund over time.

Part of the problem is that the brain only notices and remembers the handful of investors who were able to time the market successfully.  What investors should examine is the base rate:  Out of all investors who have tried market timing, how many have succeeded?  A very tiny percentage.



When our sentiment is positive, we expect our investments to bring returns higher than risk.  When our sentiment is negative, we expect our investments to bring returns lower than risk.

People expect the stocks of admired companies to do better than the stocks of spurned companies, but the opposite is true.  That’s a key reason deep value investing works:  on average, people are overly negative on out-of-favor or struggling companies, and people are overly positive on companies currently doing well.

People even expect higher returns if the name of a stock is easier to pronounce!

Finally, many investors think they can get rich from a new technological innovation.  In the vast majority of the cases, this is not true.  For every Ford, for every Microsoft, for every Google, for every Amazon, there are many companies in the same industry that failed.



A sense of control, like optimism, is generally beneficial, helping us to overcome challenges and feel happier.  A sense of control is good in most areas of life, but – like overconfidence – it is generally harmful in areas that involve much randomness, such as investing.

Statman explains:

A sense of control gained through lucky charms or rituals can be useful.  In a golfing experiment, some people were told they were receiving a lucky ball; others received the same ball and were told nothing.  Everyone was instructed to take ten putts.  Players who were told that their ball was lucky made 6.42 putts on average while those with the ordinary ball made only 4.75.  People in another experiment were asked to bring a personal lucky charm to a memory test.  Half of them kept the charm with them, but the charms of the other half were kept in another room.  People who had the charms with them reported that they had greater confidence that they would do well on the test than the people whose charms were kept away, and people who had the charms with them indeed did better on the memory test.

The outcomes of golf and memory tasks are not random; they are tasks that can be improved by concentration and effort.  A sense of control brought about by lucky charms or lucky balls can help improve performance if a sense of control brings real control.  But no concentration or effort can improve performance when outcomes are random, not susceptible to control, as is often true in much of investing and trading.  (page 50)

Statman describes one experiment involving traders who saw an index move up or down.  The task was to raise the index as much as possible by the end of each of four rounds.  Traders were also told that three keys on their keyboard have special effect.

In truth, movements in the index were random and the three keys had no effect on outcomes.  Any sense of control was illusory.  Still, some traders believed that they had much control while others believed that they had little.  It turned out that the traders with the highest sense of control displayed the lowest level of performance.  (page 51)



Statman also discusses cognitive biases.  He remarks that cognitive biases affect each one of us slightly differently.  Some may fall prey to hindsight bias more often.  Some have more trouble with availability.  Others may be more overconfident, and so forth.

Before examining some cognitive biases, it’s worth briefly reviewing Daniel Kahneman’s definition of two different mental systems that we have:

System 1:   Operates automatically and quickly;  makes instinctual decisions based on heuristics.

System 2:   Allocates attention (which has a limited budget) to the effortful mental activities that demand it, including complex computations involving logic, math, or statistics.

Kahneman writes – in Thinking, Fast and Slow – that System 1 and System 2 usually work quite well together:

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.

Yet in some circumstances – especially if a good judgment requires complex computations such as logic, math, or statistics – System 1 has cognitive biases, or systematic errors that it tends to make.

The systematic errors of System 1 happen predictably in areas such as investing or forecasting.  These areas involve so much randomness that the intuitive statistics of System 1 lead predictably and consistently to errors.



availability bias:   we tend to overweight evidence that comes easily to mind.

Related to the availability bias are vividness bias and recency bias.  We typically overweight facts that are vivid (e.g., plane crashes or shark attacks).   We also overweight facts that are recent (partly because they are more vivid).

Statman comments on the availability bias and on the near-miss effect:

Availability errors compound representativeness errors, misleading us further into the belief that beating the market is easy.  Casinos exploit availability errors.  Slot machines are quiet when players lose, but they jingle cascading coins when players win.  We exaggerate the likelihood of winning because the loud voice of winning is available to our minds more readily than the quiet voice of losing… Scans of the brains of gamblers who experience near-misses show activation of a reward-related brain circuitry, suggesting that near-misses increase the transmission of dopamine.  This makes gambling addiction similar to drug addiction.  (page 29)

Statman pens the following about mutual fund marketing:

Mutual fund companies employ availability errors to persuade us to buy their funds.  Morningstar, a company that rates mutual funds, assigns to each fund a number of stars that indicate its relative performance, one star for the bottom group, three stars for the average group, and five stars for the top group.  Have you ever seen an advertisement for a fund with one or two stars?  But we’ve all seen advertisements for four- and five-star funds.  Availability errors lead us to judge the likelihood of finding winning funds by the proportion of four- and five-start funds available to our minds.  (page 29-30)



confirmation bias:   we tend to search for, remember, and interpret information in a way that confirms our pre-existing beliefs or hypotheses.

Confirmation bias makes it quite difficult for many of us to improve upon or supplant our existing beliefs or hypotheses.  This bias also tends to make most of us overconfident about our existing beliefs or hypotheses, since all we can see are supporting data.

It’s clear that 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 our 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 we 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, Thinking, Fast and Slow)

Thus, the habit of always looking for disconfirming evidence of our hypotheses – especially our best-loved hypotheses (Charlie Munger’s term) – 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 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, 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.  (my emphasis)

As Statman states:

Confirmation errors contribute their share to the perception that winning the beat-the-market game is easy.  We commit the confirmation error when we look for evidence that confirms our intuition, beliefs, claims, and hypotheses, but overlook evidence that disconfirms them… The remedy for confirmation errors is a structure that forces us to consider all the evidence, confirming and disconfirming alike, and guides us to tests that tell us whether our intuition, beliefs, claims, or hypotheses are confirmed by the evidence or disconfirmed by it.

One manifestation of confirmation errors is the tendency to trim disconfirming evidence from stories… The fact that a forecast of an imminent stock market crash was made years before its coming is unappetizing, so we tend to trim it off our stock market stories.  (page 31)



Hindsight bias:   the tendency, after an event has occurred, to see the event as having been predictable, despite little or no objective basis for predicting the event prior to its occurrence.

This is a very powerful bias that we have.   Because we view the past as much more predictable than it actually was, we also view the future as much more predictable than it actually is.

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)

Statman elucidates:

So, if an introverted man marries a shy woman, it must be because, as we have known all along, ‘birds of a feather flock together’ and if he marries an outgoing woman, it must be because, as we have known all along, ‘opposites attract.’  Similarly, if stock prices decline after a prolonged rise, it must be, as we have known all along, that ‘trees don’t grow to the sky’ and if stock prices continue to rise, it must be, as we have equally known all along, that ‘the trend is your friend.’  Hindsight errors are a serious problem for all historians, including stock market historians.  Once an event is part of history, there is a tendency to see the sequence that led to it as inevitable.  In hindsight, poor choices with happy endings are described as brilliant choices, and unhappy endings of well-considered choices are attributed to horrendous choices.  (page 33)

Statman later writes about Warren Buffett’s understanding of hindsight bias:

Warren Buffett understands well the distinction between hindsight and foresight and the temptation of hindsight.  Roger Lowenstein mentioned in his biography of Buffett the events surrounding the increase in the Dow Jones Industrial Index beyond 1,000 in early 1966 and its subsequent decline by spring.  Some of Buffett’s partners called to warn him that the market might decline further.  Such calls, said Buffett, raised two questions:

If they knew in February that the Dow was going to 865 in May, why didn’t they let me in on it then; and

If they didn’t know what was going to happen during the ensuing three months back in February, how do they know in May?

Statman concludes:  We will always be normal, never rational, but we can increase the ratio of smart normal behavior to stupid normal behavior by recognizing our cognitive errors and devising methods to overcome them.

One of the best ways to minimize errors from cognitive bias is to use a fully automated investment strategy.  A low-cost broad market index fund will allow you to beat at least 90% of all investors over several decades.  If you adopt a quantitative value approach, you can do even better.



Overconfidence is such as widespread cognitive bias among people that Kahneman devotes Part 3 of his book, Thinking, Fast and Slow, 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)

As Statman describes:

Investors overestimate the future returns of their investments relative to the returns of the average investor.  Investors even overestimate their past returns relative to the returns of the average investor.  Members of the American Association of Individual Investors overestimated their own investment returns by an average of 3.4 percentage points relative to their actual returns, and they overestimated their own returns relative to those of the average investor by 5.1 percentage points.  The unrealistic optimism we display in the investment arena is similar to the unrealistic optimism we display in other arenas.  (page 45)

Statman also warns that stockbrokers and stock exchanges have good reasons to promote overconfidence because unrealistically optimistic investors trade far more often.



self-attribution bias:   we tend to attribute good outcomes to our own skill, while blaming bad outcomes on bad luck.

This ego-protective bias prevents us from recognizing and learning from our mistakes.  This bias also contributes to overconfidence.

As with the other cognitive biases, often self-attribution bias makes us happier and stronger.  But we have to learn to slow ourselves down and take extra care in areas – like investing – where overconfidence will hurt us.



In Behavioural Investing (Wiley, 2007), 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)

Here are the 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.



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.

Thanks to evolution, System 1 is usually right when it assumes causality.  For example, there was movement in the grass, probably caused by a predator, so run.  And even in the modern world, as long as cause-and-effect is straightforward and not statistical, System 1 is amazingly 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.  (Kahneman)

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 (based on current knowledge).  In these areas, System 1 is often very wrong when it creates coherent stories or makes predictions.  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.



anchoring effect:   we 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.

Kahneman and 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 ran his own experiment on anchoring.   People were asked to write down the last four digits of their phone number.   Then they were 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 reported 6762 doctors, while those with telephone numbers below 2000 arrived at an average 2270 doctors.  (Behavioural Investing, 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)



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

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

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

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


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

My e-mail: jb@boolefund.com




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

Our Process and Vision

(Image:  Zen Buddha Silence by Marilyn Barbone.)

April 2, 2017

If you’re investing small sums, you can earn the highest returns by focusing on microcap stocks.  That’s why many top value investors started in micro caps.  For instance, Warren Buffett concentrated on micro caps when he managed his partnership starting in 1957, which produced the highest returns of his career.  And Buffett has repeatedly said that in today’s market, he could get 50% per year if he could invest in micro caps.

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.

  • 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.



By adding a value screen to a microcap strategy, it is possible to add at least 2-3% per year.  There are several ways to measure cheapness, such as low EV/EBIT, low P/E, and low P/CF.

Tobias Carlisle and Wesley Gray tested these and other measures of cheapness from 1964 to 2011 – see Quantitative Value (Wiley, 2013).  They found that EV/EBIT outperformed all the other measures of cheapness.

Furthermore, Carlisle and Gray tested simple EV/EBIT, based on trailing one-year figures, against various combinations (including multi-year).  Simple EV/EBIT was still the best performer.



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/



In sum, over the course of several decades, a systematic value strategy – applied to cheap microcap stocks with improving fundamentals – has high odds of returning at least 7-9% more per year than a low-cost S&P 500 Index fund.



An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.

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

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

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



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

My e-mail: jb@boolefund.com



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

How To Master Yourself As An Investor

(Image:  Zen Buddha Silence by Marilyn Barbone.)

March 12, 2017

James Montier, an expert in behavioral finance, has written several excellent books, including The Little Book of Behavioral Investing: How Not to Be Your Own Worst Enemy (Wiley, 2010).

Montier begins with a quote from the father of value investing, Ben Graham, who clearly understood (decades before behavioral finance was invented) that overcoming your own emotions is often the greatest challenge for an investor:

The investor’s chief problem – and even his worst enemy – is likely to be himself.

The problem, as Daniel Kahneman observes in his great book Thinking, Fast and Slow, is that we can easily recognize cognitive errors in others, but not in ourselves.  Yet we all suffer from cognitive biases.

If you can learn to recognize when your brain may make a cognitive error and if you can learn systems to overcome that, then you can do much better as an investor.  Here are three systems that can help you to minimize the impact of cognitive biases in order to maximize your long-term investment results:

  • Invest in low-cost broad market index funds. If you do this, then you’ll do better than 90% of all investors after several decades.  Also, this approach takes very little time to implement and maintain.
  • Invest in a quantitative value fund (or in several such funds). This is a fund that automatically buys the statistically cheapest stocks, year in and year out.  If done properly, this approach should do better than broad market index funds over time.  One of the most successful quantitative value investors is LSV Asset Management.  See: http://lsvasset.com/
  • Do value investing on your own. If you really enjoy learning about various businesses and if you enjoy the process of value investing, then with the right systems, you can learn to do well over time.  As an individual investor, you can look at tiny stocks overlooked by most professionals.  Also, you can easily concentrate your portfolio if you happen to come across any extremely cheap stocks.  (A young Warren Buffett once put his entire personal portfolio into one stock: GEICO.)

Note that the first two systems involve fully automated investing, which drastically reduces the number of decisions you make and therefore almost entirely removes the possibility of cognitive error.  The vast majority of investors are much better off using a fully automated approach, whether index funds or quantitative value funds.

(There are some actively managed value funds whose managers possess great skill, but usually it’s not possible for an ordinary investor to invest with them.  Also, many of these funds have gotten quite large, so performance over the next decade or two will likely be noticeably lower.)



It is now broadly recognized that there are two different systems operating in the human brain:

System 1:  Operates automatically and quickly; makes instinctual decisions based on heuristics.

System 2:  Allocates attention (which has a limited budget) to the effortful mental activities that demand it, including logic, statistics, and complex computations.

As Daniel Kahneman points out (see Thinking, Fast and Slow), System 1 is amazingly good at what it does.  Its models of familiar situations are accurate.  Its short-term predictions are usually accurate.  And its initial reactions to challenges are swift and generally appropriate.  System 1 does all of these things automatically, and without needing any help from System 2.

The problem is that there are some situations in modern life – especially if a good decision requires the proper use of logic or statistics – where System 1 suffers from cognitive biases, causing systematic errors.  Most people, even if highly educated, rely largely on System 1.

System 2 can be trained over time to do logic, math, and statistics.  But in the presence of high levels of stress or fatigue, System 2 can easily be derailed or not activated at all.  Self-aware people recognize this, and can learn countermeasures such as meditation (which can dramatically reduce stress) or postponing important decisions (if possible).

To show how most people rely on System 1, even for math and logic, Montier describes the Cognitive Reflection Test (CRT), invented by Shane Frederick.

If you’re reading this, try answering these three questions:

  1. A bat and a ball together cost $1.10 in total. The bat costs a dollar more than the ball.  How much does the ball cost?
  2. If it takes five minutes for five machines to make five widgets, how long would it take 100 machines to make 100 widgets?
  3. 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 to cover half the lake?

Montier writes that only 17% out of 3,500 people tested by Frederick got all three questions right.  33% got none right!  Even among the best performing group – MIT students – only 48% managed to get all three right.

Montier gave the CRT to 600 professional investors.  Only 40% got all three right.  10% didn’t get any right.

Not doing well on the CRT is correlated with many behavioral errors, says Montier.  Doing well in investing is more about temperament and rationality than it is about IQ.  Montier quotes Buffett:

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.

It takes years of work to improve as an investor.  But the great thing is that you can improve at investing your entire life.  All the knowledge and experience is cumulative, as Buffett says.



The empathy gap is our inability to predict how we will behave while under emotional strain.  In the world of investing, many react emotionally to negative news or to falling stock prices, which leads to poor decisions.

In order to prevent ourselves from making poor decisions, we need to pre-commit to a plan, or (even better) follow a fully automated investment strategy.

Sir John Templeton explains how to do well as an investor:

The time of maximum pessimism is the best time to buy, and the time of maximum optimism is the best time to sell.

To do this consistently, we need to pre-commit to a plan or use fully automated investing.  System 1 reacts to dropping prices with fear and loss aversion.  Fear causes people to ignore great bargains when stock prices are plummeting.  And fearful people become even more fearful if they have already suffered from losses.  Many investors just say, ‘Get me out of the market [or the stock] at any price’ in order to relieve their pain.  But what they should be doing is buying (or holding) the cheapest stocks available.

In order to overcome his own cognitive biases, John Templeton pre-committed to buying cheap stocks during a bear market: he placed buy orders for many stocks well below their current prices.

Typically during a bear market, the investors who have large cash positions fail to invest until after they’ve already missed much of the market recovery.  Again, only by having pre-committed to a battle plan before a bear market can you increase the odds of buying when you should be buying – when stocks are cheap.  As Jeremy Grantham wrote (quoted by Montier):

There is only one cure for terminal paralysis: you absolutely must have a battle plan for reinvestment and stick to it.  Since every action must overcome paralysis, what I recommend is a few large steps, not many small ones….

It is particularly important to have a clear definition of what it will take for you to be fully invested.  Without a similar program, be prepared for your committee’s enthusiasm to invest (and your own for that matter) to fall with the market.  You must get them to agree now – quickly before rigor mortis sets in… Finally, be aware that the market does not turn when it sees light at the end of the tunnel.  It turns when all looks black, but just a subtle shade less black than the day before.

The legendary value investor Seth Klarman describes a bear market as follows (quoted by Montier):

The chaos is so extreme, the panic selling so urgent, that there is almost no possibility that sellers are acting on superior information; indeed, in situation after situation, it seems clear that investment fundamentals do not factor into their decision making at all… While it is always tempting to try and time the market and wait for the bottom to be reached (as if it would be obvious when it arrived), such a strategy proved over the years to be deeply flawed.  Historically, little volume transacts at the bottom or on the way back up and competition from other buyers will be much greater when the markets settle down and the economy begins to recover.  Moreover, the price recovery from a bottom can be very swift.  Therefore, an investor should put money to work amidst the throes of a bear market, appreciating that things will likely get worse before they get better.

So a battle plan for reinvestment is a “schedule of pre-commitments” that can help you overcome the fear induced by plummeting prices.  Such a plan will require that you buy stocks when they are cheap.  Such a plan will require that you average down when stocks continue to get cheaper.

Although this discussion has focused mostly on a broad market sell-off, sometimes there are sell-offs (for non-fundamental, short-term reasons) in particular industries or in individual stocks.  In these cases, too, having a battle plan can help you to ignore the crowd – which often is focused only on the short term – and buy the stocks that are likely the cheapest based on long-term fundamentals.



The majority of drivers say they are above average.  Most students say they will finish in the top half of their class.  Indeed, overconfidence is the pattern for nearly all human activities.  Montier asked 600 professional fund managers how many were above average, and 74% said they were.

Earlier I mentioned Shane Frederick’s CRT test.  People who ace that test tend to suffer from fewer cognitive biases.  However, this does not apply to overconfidence, which is a deep-seated and very wide-spread cognitive bias.  Nearly everyone suffers from overconfidence, although some people learn to tame it.

The trouble is that knowing a great deal about overconfidence does not remove the bias.  Daniel Kahneman, one of the best psychologists, especially when it comes to cognitive biases such as overconfidence, admits that he himself is still “wildly overconfident” as a default setting.  Unless there is a serious threat – a possible predator in the grass – our System 1 automatically makes us feel at ease and overconfident about nearly everything.

Now, in most spheres of human activity, overconfidence and optimism are actually good things.  Overconfident and optimistic people deal with problems better, are happier, and live longer.  Perhaps we developed overconfidence a long time ago when hunting.  Fearless hunters were generally better hunters.

The problem is that in fields (like investing) that require the proper use of logic or statistics, overconfidence leads to poor decisions and often disasters.

There are several ways that we can overcome overconfidence.  Committing to a fully automated approach to investing is the best way to minimize all cognitive biases.

  • Buying and holding a low-cost broad market index fund will allow you to do better than 90% of all investors over several decades.
  • A quantitative value strategy can be even better than an index fund. Such an approach automatically buys the statistically cheapest stocks.

If you want to do value investing yourself because you enjoy it, then using a checklist and other specific processes can greatly improve your results over time.  Guy Spier has outstanding discussions of systems and processes in The Education of a Value Investor.  See: http://boolefund.com/the-education-of-a-value-investor/

Montier also mentions some good questions you should ask if you’re doing value investing for yourself.  “Must I believe this?” is a better question than “Can I believe this?”  In other words, what would invalidate the investment hypothesis?

It’s important to understand that System 1 automatically looks for confirming evidence for all of its views.  Even more importantly, System 2 uses a positive test strategy, which means that System 2 also looks for confirming evidence rather than disconfirming evidence.

Yet we know that the advance of science depends directly on the search for disconfirming evidence.  Charles Darwin, for instance, trained himself always to look for disconfirming evidence.  In this careful, painstaking manner, Darwin, though not at all brilliant, was eventually able to produce some of the finest mental work ever done.  The bottom line for investors is simple:

Always look for disconfirming evidence rather than for confirming evidence.

Many of the best value investors have developed the habit of always looking for disconfirming evidence.  If you look the investment management firms run by Seth Klarman, Richard Pzena, and Ray Dalio – to give just a few examples out of many – there are processes in place to ensure that disconfirming evidence is ALWAYS sought.  For instance, if one analyst is responsible for the bullish case on a stock, another analyst will be assigned to argue the bearish case.  Investors who have a deeply ingrained process of searching for disconfirming evidence have generally enjoyed a boost to their long-term performance as a result.

Montier also writes that “Why should I own this investment?” is a better question than “Why shouldn’t I own this investment?”  The default should be non-ownership until the investment hypothesis has been formed and tested.  One major reason Buffett and Munger are among the very best investors is because of their extreme discipline and patience.  They are super selective, willing to pass on a hundred possibilities in order to find the one investment that is the most supported by the evidence.  (This was even more true when they were managing less capital.  These days, they have so much capital – as a result of their great successes over time – that they have to overlook many of the best opportunities, which involve companies too tiny to move the needle at Berkshire Hathaway.)



By nature, we tend to like people who sound confident.  Financial commentators typically sound very confident on TV.  But when it comes to financial forecasting, no one can do it well.  Some forecasters will be right on occasion, but you never know ahead of time who that will be.  And six months from now, some other forecasters – different ones – will be right.

The best thing, as strongly advised by many top investors including Warren Buffett, is to ignore all financial forecasts.  Either stick with a fully automated investment program – whether index fund or quantitative value – or stay focused on individual companies if you’re doing value investing for yourself.

If you’re doing value investing yourself, then even when the broader market is overvalued, you can usually find tiny companies that are extremely cheap.  Most professional investors never look at microcap stocks, which is why it’s usually the best place to look for the cheapest stocks.

Warren Buffett has never relied on a financial forecast while creating the greatest 57-year (and counting) track record of all time:

I have no use whatever for projections or forecasts.  They create an illusion of apparent precision.  The more meticulous they are, the more concerned you should be.

I make no effort to predict the course of general business or the stock market.  Period.

Anything can happen anytime in markets.  And no advisor, economist, or TV commentator – and definitely not Charlie nor I – can tell you when chaos will occur.  Market forecasters will fill your ear but will never fill your wallet.

Similarly, the great investor Peter Lynch never relied on financial forecasts:

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.

The way you lose money in the stock market is to start off with an economic picture. I also spend fifteen minutes a year on where the stock market is going.

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:

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.

Furthermore, as noted by Montier (page 46), when people are told that someone is an expert, they partially switch off the part of the brain associated with logic and statistics.  (This has been shown in experiments.)



There are still quite a few investors who continue to listen to financial forecasters, despite the fact that financial forecasts on the whole do not add value.  There are several cognitive errors happening here.  Many investors are governed mostly by emotions.  They watch the daily fluctuations of stock prices – which have no meaning at all for long-term investors (except when they are low enough to create a buying opportunity).  They listen to financial experts giving mostly useless forecasts.  And they anchor on various forecasts.

Part of the problem is that many people will believe or do almost anything if it comes from a perceived authority.

Stanley Milgram ran a famous experiment about authority because he was trying to understand why so many previously good people decided to commit evil during WWII by joining the Nazi’s.

In Milgram’s experiment, subjects were told they would administer electric shocks to a “learner.”  If the learner answered a question incorrectly, the subject was told to deliver the shock.  The subjects met the learners before the experiment, but the learners were in a separate room during the experiment.  The subjects could hear the learners, however, as they answered questions and as they were shocked.  Behind the subject stood an authority figure wearing a white lab coat, carrying a clipboard, and telling the subject when to give the shock.

Before running the experiment, Milgram asked a panel of 40 psychiatrists what they thought the subjects would do.  The psychiatrists thought that only 1% of the subjects would go to the maximum shock level (450 volts with labels of “extreme danger” and “XXX”).  After all, they reasoned, the subjects were Americans and Americans wouldn’t do that, right?

100% of the subjects went up to 135 volts (at which point the learner is asking to be released).  80% of the subjects were willing to go up to 285 volts (at which point all they could hear were screams of agony).  And more than 62% of the subjects were willing to administer the maximum 450 volts (despite the labels of “extreme danger” and “XXX”) – at which point there was no sound at all from the learner.

Milgram ran several variants of his experiment, but the results were essentially the same.



A major reason why financial forecasters continue to forecast, despite adding no value on the whole, is simply that there are enough fearful people who need to hear financial forecasts.

But you might wonder: If a forecaster has been wrong so often, how can that person continue to forecast, even when there is a demand for it?

Montier discusses a landmark, 20-year study done by the psychologist Philip Tetlock.  (See Expert Political Judgment: How Good Is It? How Can We Know?)  Tetlock gathered over 27,000 expert predictions, and he found that they were little better than pure chance.

Tetlock also found that forecasters tend to use the same excuses over and over.  The five most common excuses for failed forecasts were the following:

The “If only” defense – If only the Federal Reserve had raised rates, then the prediction would have been true.  Effectively, the experts claim that they would have been correct if only their advice had been followed.

The ceteris paribus” defense – Something outside of the model of analysis occurred, which invalidated the forecast; therefore it isn’t my fault.

The “I was almost right” defense – Although the predicted outcome didn’t occur, it almost did.

The “It just hasn’t happened yet” defense – I wasn’t wrong, it just hasn’t occurred yet.

The “Single prediction” defense – You can’t judge me by the performance of a single forecast.

How can forecasters continue to be confident in their forecasts after years or decades of mostly being wrong?  Typically, they invoke one of the five defenses just listed.  As a result, most forecasters maintain just as high a level of overconfidence, even after a long string of mostly incorrect forecasts.



A fully automated deep value investing strategy will probably yield excellent results over time (especially if focused on microcap stocks), when compared with most other investment strategies.  If you have doubts about that, then low-cost broad market index funds are a great choice for your long-term investing.

If you’re picking individual stocks using a value investing approach, then the key is to be a learning machine.  And there are many books you should read, including all the books I’ve written about previously on this weekly blog.

There’s one book in particular that I recommend because it essentially uses Ben Graham’s conservative approach to picking individual stocks.  The book is Value Investing: From Graham to Buffett and Beyond (Wiley, 2004), by Professor Bruce Greenwald (and others).

If you could forecast the future free cash flows of a given company, and if you had a reasonable estimate of the discount rate, then you could value that company.  The problem with this DCF approach is that very tiny changes in certain variables, like long-term growth rates or the discount rate, can dramatically change the intrinsic value estimate.

Greenwald explains two approaches that depend entirely on the currently known facts about the company:

  • Net asset value
  • Earnings power value

Many value investors over the years have done extraordinarily well by estimating net asset value and then buying below that value.  Peter Cundill, for example, is a great value investor who always focused on buying below liquidation value.  See: http://boolefund.com/peter-cundill-discount-to-liquidation-value/

Other value investors have done extremely well by estimating the earnings power value – or normalized earnings – and consistently buying below that level.

Often the earnings power value of a company will exceed its net asset value.  So in a sense, net asset value is a conservative estimate of intrinsic value, while earnings power value may represent potential upside.  But even with earnings power value, there is zero forecasting involved.  There is no estimate of future growth.  This is especially helpful for value investors because the studies have shown that the stocks of ‘high-growth’ companies underperform, while the stocks of ‘low-growth’ or ‘no-growth’ stocks outperform.  See: http://boolefund.com/the-ugliest-stocks-are-the-best-stocks/



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

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

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

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


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

My e-mail: jb@boolefund.com




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

The Ugliest Stocks Are the Best Stocks

(Image:  Zen Buddha Silence by Marilyn Barbone.)

March 5, 2017

Quantitative value investor Tobias Carlisle has written an excellent book entitled Deep Value: Why Activists and Other Contrarians Battle for Control of LOSING Corporations (Wiley, 2014).

The book has many important and counterintuitive lessons for quantitative value investors.  This blog post is a brief summary of some chief lessons.



Companies with low growth or no growth that are trading at cheap valuations significantly outperform companies with high growth.  In other words, as a group, companies that have been doing terribly and that are trading at cheap prices – seemingly justifiably – do much better than companies that have been doing well and growing fast.

It’s important to note that these findings apply to groups of stocks, not individual stocks.  Cheap value stocks, as a group (and as a portfolio), significantly outperform companies that have been doing well and whose stocks have been doing well.  Moreover, in states of the world including bear markets and recessions, value stocks do better than growth stocks.  So value stocks are less risky as a group than growth stocks.

On an individual level, a value stock is riskier than an average stock.  Whereas an average stock has a 50% chance of underperforming the market, a value stock – if it is just chosen based on cheapness alone, without additional criteria – has a greater than 50% chance of underperforming the market.  But as a group (and as a portfolio), value stocks – when compared to either growth stocks or average stocks – are less risky and perform better over time.

If you are following a deep value approach, there are additional criteria that you can apply in the stock selection process to reduce the percentage of deep value stocks that underperform the market.  One example is the Piotroski F-Score, which identifies companies that show improving fundamentals (e.g., increased cash flows or reduced debt levels).  Joseph Piotroski came up with the F-Score because he discovered that while value stocks outperform as a group, there are many individual value stocks dragging down the overall performance of the value portfolio.

In sum, a portfolio of ugly value stocks far outperforms the market over time.  Remarkably, this already significant outperformance can be noticeably improved by using the Piotroski F-Score to cut off the left tail of the return distribution.  See: http://boolefund.com/joseph-piotroski-value-investing/



If you only look at value stocks, which as a group outperform, doesn’t it make sense to focus on the cheap stocks where the companies have been doing well – in terms of growth – rather than the cheap stocks where the companies have been doing terribly?  No.  Carlisle comments:

This is a fascinating finding.  Intuitively, we are attracted to high growth and would assume that high-growth value stocks are high-quality stocks available at a bargain price.  The data show, however, that the low- or no-growth value stocks are the better bet.  It seems that the uglier the stock, the better the return, even when the valuations are comparable.  (page 133)

This same logic also applies to excellent, A+ companies versus unexcellent, D companies.  Carlisle again:

Buying well-run companies with good businesses seems to make so much sense.  Buying well-run companies with good businesses at bargain prices seems to make even more sense.  The research shows, however, that the better investment – rather than the better company – is the value stock, the scorned, the unexcellent, the Ds, the loss-making net nets.  And the better value stock, according to Lakonishok, Shleifer, and Vishny’s research, is the low- or ­no-growth value stock, what they describe as ‘contrarian value,’ and what I regard as deep value; the ugliest of the ugly.  (page 140)

Link to the famous 1994 paper by Josef Lakonishok, Andrei Schleifer, and Robert Vishny: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf?m=1360042367



Investing in general is difficult to do effectively, which is why Warren Buffett advises most people to invest in low-cost index funds.

Deep value investing can be even more difficult because it requires consistently buying what everyone else hates – the ugliest, the worst, the cheapest stocks available.  Deep value stocks are almost always facing enormous business problems, and quite often the industries in which deep value stocks are found are doing horribly (for example, oil-related stocks with the oil price down from over $100 a barrel to below $30 or $40 a barrel).

Thus, the best way for most investors to benefit from deep value stocks is to use a quantitative (statistical) approach.  Carlisle explains:

Most deeply undervalued, fundamentally weak stocks are that way because their futures appear uncertain – they are losing money or marginally profitable – and, on an individual basis, don’t appear to be good candidates for purchase.  We know, however, that in aggregate they provide excellent returns, outperforming the market in the long run and suffering fewer down years than the market.  This is an area where our native intuition fails us.  As we have seen, no matter how well trained we are, humans tend to have difficulty with probabilistic, uncertain, and random processes… Since the 1950s, social scientists have been comparing the predictive abilities of traditional experts and what are known are statistical prediction rules.  The studies have found almost uniformly that statistical prediction rules are more consistently accurate than the very best experts.  (page 141)

I wrote about this here:  http://boolefund.com/simple-quant-models-beat-experts-in-a-wide-variety-of-areas/

The conclusion is that, for a surprisingly wide range of prediction problems – including investing – statistical prediction rules are more reliable than human experts.  Many people have objected that experts could do better than simple statistical prediction rules if they had the ability to override the rule in specific cases.  But this turns out not to be true.  The statistical prediction rules are a ceiling from which the expert detracts rather than a floor to which the expert adds.

As Daniel Kahneman explains so well in his book Thinking, Fast and Slow – see especially Part III – we humans are generally very overconfident about our ability to predict the future.  Philip Tetlock did a landmark, 20-year study of experts making political and economic predictions.  What Tetlock found based on more than 27,000 predictions over the course of two decades is that the experts were little better than chance.  See Tetlock’s Expert Political Judgment: How Good Is It? How Can We Know? (Princeton, 2005).

People, especially experts, are simply way overconfident about their ability to predict many future events.  Even Kahneman himself, after spending most of his life studying overconfidence, admits that he is “wildly overconfident” by nature, just like most people.  Overconfidence is related to many cognitive biases that people have, especially hindsight bias: http://boolefund.com/cognitive-biases/

Statistical Prediction Rules Applied to Deep Value Investing

If you don’t understand value investing – or if trailing the market for a couple of years would make you abandon a value strategy – then your best long-term investment is a low-cost broad market index fund.  Such an index fund will allow you to beat 85-90% of all investors over the course of several decades.  And it takes very little time to implement and maintain this approach.

If you understand value investing, then you should consider a quantitative value fund.  A quantitative value fund – like the Boole Microcap Fund – is a fund that systematically picks cheap stocks.  Typically, systematic stock selection is fully automated, thereby maximizing long-term results by minimizing human error.



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

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

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

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


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

My e-mail: jb@boolefund.com




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

Best Performers: Microcap Stocks

(Image:  Zen Buddha Silence by Marilyn Barbone.)

February 26, 2017

Are you a long-term investor?  If so, are you interested in maximizing long-term results without taking undue risk?

Warren Buffett, arguably the best investor ever, has repeatedly said that most people should invest in a low-cost broad market index fund.  Such an index fund will allow you to do better than at least 90% of all investors, net of costs, after several decades.

Buffett has also said that you can do better than an index fund by investing in microcap stocks – as long as you have a sound method.  Take a 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.

  • 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.



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.  If you would like to maximize the odds of achieving this additional margin of outperformance, then you should adopt a systematic, quantitative investment strategy.



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/



In sum, over the course of several decades, a systematic 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.



An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.

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

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

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


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

My e-mail: jb@boolefund.com



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


Are Humans Rational?

(Image:  Zen Buddha Silence by Marilyn Barbone.)

February 12, 2017

I’ve always deeply admired both great scientists and great artists.  Science and art – each in its own way – attempt to grasp the nature of ultimate reality, or at least the nature of reality as far as we currently can see it.  As Einstein wrote in his essay, “The World As I See It”:

The ideals which have lighted me on my way and time after time have given me new courage to face life cheerfully, have been Truth, Goodness, and Beauty.  Without the sense of fellowship with people of like mind, of preoccupation with the objective, the eternally unattainable in the field of art and science, life would have seemed to me empty.

The pursuit of truth, goodness, and beauty is really everything that makes life worthwhile.  (This includes, by definition, friendship and love.)

As a business person, I hasten to add that having the freedom to pursue one’s passion is obviously essential.  As science moves forward and as the world economy evolves, ever more people are gaining a measure of freedom (including better access to healthcare and education).  But there is still a long way to go.

What does this have to do with investing?  Only that the scientific study of human nature – including biology, genetics, psychology, neuroscience, economics, and sociology – is as fascinating as other areas in science, such as physics and astronomy.



When people make investment decisions, they are making decisions under uncertainty – decisions where there are unknown future states of the world.  Many economists have assumed that people make rational decisions under uncertainty.  Most of these economists have held that people always maximize their expected utility.  Maximizing expected utility means that people assign a probability and a reward to each possible future state, and then make a decision (e.g., an investment) that will maximize the expected utility.  Expected utility is just the sum of each probability times each reward.

Expected Utility Example:  Atwood Oceanics

The Boole Microcap Fund bought stock in Atwood Oceanics, Inc. (NYSE: ATW) at $6.46 in late January.  Currently, ATW is at roughly $11.70, but it is still probably quite cheap, at least from a 3- to 5-year point of view.

Here is how to apply the expected utility framework.  First note that under “normal” economic conditions, the market clearing price of oil is about $60-70.  A market clearing price just means a price where the daily supply of oil meets the daily demand for oil.  Let’s consider three scenarios, any of which could occur over the next 3 to 5 years, however long it takes for the oil markets to return to “normal”:

  • Oil at $55 and ATW earns $3.00 per share. Under this scenario, ATW may be worth $30 per share (a P/E of 10).
  • Oil at $65 and ATW earns $4.50 per share. Under this scenario, ATW may be worth $45 per share (a P/E of 10).
  • Oil at $75 and ATW earns $6.00 per share. Under this scenario, ATW may be worth $60 per share (a P/E of 10).

Assume that each scenario is equally likely, so there is a 33.3% chance for each scenario to occur.  To get the expected value of an investment in ATW today at $11.70, for each scenario we take the probability times the outcome (stock price).  Then we add those numbers together.  So we have $30 x .333 = $10, plus $45 x .333 = $15, plus $60 x .333 = $20.  So we expect ATW to hit $45 = $10 + $15 + $20, and currently we can buy the stock for $11.70.  That would be an expected return of almost 300% (or nearly double that if we were lucky enough to buy the stock at $6).

We could adjust the probabilities, oil prices, earnings, and P/E ratio’s for each scenario (as long as the probabilities add up to 100%).  We could also decide to use more (or less) than three scenarios.

A Few More Expected Utility Examples

An obvious business example of expected utility is whether a company should invest in Product A.  One tries to guess different scenarios (and their associated probabilities), perhaps including one that is highly profitable and another involving large losses.  One also must compare an investment in Product A with the next best investment opportunity.

In theory, we can apply the expected utility framework to nearly any situation where the future states are unknown, even life and death. 

I don’t think George Washington explicitly did an expected utility calculation.  But like many young men of his day, Washington was eager to serve in the military.  He had a couple of horses shot from under him.  And there were several occasions when bullets went whizzing right past his head (which he thought sounded “charming”).  In short, Washington was lucky to survive, and the luck of Washington has arguably reverberated for centuries.

Had Washington been able to do an expected utility calculation about his future war experience, it might have been something like this (crudely):

  • 25% chance of death (even when he was a general, Washington often charged to the front)
  • 75% chance of honor and wealth

A more realistic expected utility calculation – but not what Washington could have guessed – might have been the following:

  • 50% chance of death
  • 50% chance of great honor (due to great service to his future country) and wealth

(I’m oversimplifying in order to illustrate the generality of the expected utility framework.)

Von Neumann and Morgenstern

What’s fascinating is that the expected utility framework does a very good job describing how any rational agent should make decisions.  The genius polymath John von Neumann and the economist Oskar Morgenstern invented this framework.  (For some details on the most general form of the framework, see: https://en.wikipedia.org/wiki/Von_Neumann%E2%80%93Morgenstern_utility_theorem)

But ever since, many economists have assumed that people actually do make decisions with full rationality.  Economists have argued that rationalist economics is the best approximation for how humans behave.

Others scientists and observers have long suspected (or, indeed, realized) that people are NOT fully rational.  But it’s one thing to hold the view that much human behavior is irrational.  It’s another thing to prove human irrationality scientifically.

The psychologists Daniel Kahneman and Amos Tversky did hundreds of experiments over the course of decades of people making decisions under uncertainty.  What Kahneman and Tversky demonstrated conclusively is that most human beings are NOT fully rational when they make decisions under uncertainty.

Behavioral economics – based on the research of Kahneman, Tversky, and many others – is a fascinating field (and the reason I went to graduate school).  But it has not yet developed to the point where its models can predict a wide range of human behavior better than the rationalist economic models.  The rationalist economic models are still the best approximation for much human behavior.



In his great book Misbehaving: The Making of Behavioral Economics (W. W. Norton, 2015), Richard Thaler discusses the development of behavioral economics.  According to Kahneman, Richard Thaler is “the creative genius who invented the field of behavioral economics.”

Thaler defines “Econs” as the fully rational human beings that traditional economists have always assumed for their models.  “Humans,” on the other hand, are the actual people who make various decisions, including often less than fully rational decisions under uncertainty.

For this blog post, I will focus on Part VI (Finance, pages 203-253).  But first, a quotation Thaler has at the beginning of his book:

The foundation of political economy and, in general, of every social science, is evidently psychology.  A day may come when we shall be able to deduce the laws of social science from the principles of psychology.  – Vilfredo Pareto, 1906



Chicago economist Eugene Fama coined the term efficient market hypothesis, or EMH for short.  Thaler writes that the EMH has two (related) components:

  • the price is right – the idea is that any asset will sell for its intrinsic value.  If the rational valuation of a company – based on normalized earnings or net assets – is $100 million, then the company’s market cap (as reflected by its stock price) will be $100 million.
  • no free lunch– EMH holds that all publically available information is already reflected in current stock prices, thus there is no reliable way to “beat the market” over time.

NOTE:  If prices are always right, that means that assets can never be overvalued (there are no bubbles) or undervalued.

Thaler observes that finance did not become a mainstream topic in economics departments before the advent of cheap computer power and great data.  The University of Chicago was the first to develop a comprehensive database of stock prices going back to 1926.  After that, research took off, and by 1970 EMH was well-established.

Thaler also points out that the economist J. M . Keynes was “a true forerunner of behavioral finance.”  Keynes, who was also an investor, thought that animal spirits – emotions – played an important role in financial markets.

Keynes also thought that professional investors were playing an intricate guessing game, similar to picking out the prettiest faces from a set of photographs:

… It is not a case of choosing those which, to the best of one’s judgment, are really the prettiest, nor even those which average opinion genuinely thinks the prettiest.  We have reached the third degree where we devote our intelligences to anticipating what average opinion expects average opinion to be.  And there are some, I believe, who practice the fourth, fifth, and higher degrees.



On average, investors overreact to recent poor performance for low P/E stocks, which is why the P/E’s are low.  And, on average, investors overreact to recent good performance for high P/E stocks, which is why the P/E’s are high.

Having said that, Thaler is quick to quote a warning by Ben Graham (the father of value investing) about timing: ‘Undervaluations caused by neglect or prejudice may persist for an inconveniently long time, and the same applies to inflated prices caused by overenthusiasm or artificial stimulus.’  Thaler gives the example of the late 1990s:  for years, Internet stocks just kept going up, while value stocks just kept massively underperforming.

According to Thaler, most academic financial economists overlooked Graham’s work:

It was not so much that anyone had refuted Graham’s claim that value investing worked;  it was more that the efficient market theory of the 1970s said that value investing couldn’t work.  But it did.  Late that decade, accounting professor Sanjoy Basu published a thoroughly competent study of value investing that fully supported Graham’s strategy.  However, in order to get such papers published at the time, one had to offer abject apologies for the results.  (page 221)

Thaler and his research partner Werner De Bondt came up with the following.  Suppose that investors are overreacting.  Suppose that investors are overly optimistic about the future growth of high P/E stocks, thus driving the P/E’s “too high.”  And suppose that investors are excessively pessimistic about low P/E stocks, thus driving the P/E’s “too low.”  Then subsequent high returns from value stocks and low returns from growth stocks represent simple reversion to the mean.  But EMH says that:

  • The price is right: Stock prices cannot diverge from intrinsic value
  • No free lunch: Because all information is already in the stock price, it is not possible to beat the market. Past stock prices and the P/E cannot predict future price changes

Thaler and De Bondt took all the stocks listed on the New York Stock Exchange, and ranked their performance over three to five years.  They isolated the worst performing stocks, which they called “Losers.”  And they isolated the best performing stocks, which they called “Winners.”  Writes Thaler:

If markets were efficient, we should expect the two portfolios to do equally well.  After all, according to the EMH, the past cannot predict the future.  But if our overreaction hypothesis were correct, Losers would outperform Winners.  (page 223)

What did they find?

The results strongly supported our hypothesis.  We tested for overreaction in various ways, but as long as the period we looked back at to create the portfolios was long enough, say three years, then the Loser portfolio did better than the Winner portfolio.  Much better.  For example, in one test we used five years of performance to form the Winner and Loser portfolios and then calculated the returns of each portfolio over the following five years, compared to the overall market.  Over the five-year period after we formed our portfolios, the Losers outperformed the market by about 30% while the Winners did worse than the market by about 10%.



In response to widespread evidence that ‘Loser’ stocks (low P/E) – as a group – outperform ‘Winner’ stocks, defenders of EMH were forced to argue that ‘Loser’ stocks are riskier as a group.

NOTE: On an individual stock basis, a low P/E stock may be riskier.  But a basket of low P/E stocks generally far outperforms a basket of high P/E stocks.  The question is whether a basket of low P/E stocks is riskier than a basket of high P/E stocks.  Furthermore, one can use other metrics such as low P/B (low price to book) and get the same results as the low P/E studies.

According to the CAPM (Capital Asset Pricing Model), the measure of the riskiness of a stock is its correlation with the rest of the market, or beta.  If a stock has a beta of 1.0, then its volatility is similar to the volatility of the whole market.  If a stock has a beta of 2.0, then its volatility is double the volatility of the whole market (e.g., if the whole market goes up or down by 10%, then this individual stock will go up or down by 20%).

According to CAPM, if the basket of Loser stocks subsequently outperforms the market while the basket of Winner stocks underperforms, then the Loser stocks must have high betas and the Winner stocks must have low betas.  But Thaler and De Bondt found the opposite.  Loser stocks (value stocks) were much less risky as measured by beta.

Eventually Eugene Fama himself, along with research partner Kenneth French, published a series of papers documenting that, indeed, both value stocks and small stocks earn higher returns than predicted by CAPM.  In short, “the high priest of efficient markets” (as Thaler calls Fama) had declared that CAPM was dead.

But Fama and French were not ready to abandon the EMH (Efficient Market Hypothesis).  They came up with the Fama-French Three Factor Model.  They showed that value stocks are correlated – a value stock will tend to do well when other value stocks are doing well.  And they showed that small-cap stocks are similarly correlated.

The problem, again, is that there is no evidence that a basket of value stocks is riskier than a basket of growth stocks.  And there is no theoretical reason to believe that value stocks, as a group, are riskier.

According to Thaler, the debate was settled by the paper ‘Contrarian Investment, Extrapolation, and Risk’ published in 1994 by Josef Lakonishok, Andrei Shleifer, and Robert Vishny.  This paper shows clearly that value stocks outperform, and value stocks are, if anything, less risky than growth stocks.  Lakonishok, Shleifer, and Vishny launched the highly successful LSV Asset Management based on their research.  Here is a link to the paper: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf

(Recently Fama and French have introduced a five-factor model, which includes profitability.  Profitability was one of Ben Graham’s criteria.)



If you held a stock forever, it would be worth all future dividends discounted back to the present.  Even if you sold the stock, as long as you held it for a very long time, the distant future sales price (discounted back to the present) would be a negligible part of the intrinsic value of the stock.  The stock price is really the present value of all expected future dividend payments.

Bob Shiller collected data on stock prices and dividends starting in 1871:

… for each year he computed what he called the ‘ex post rational’ forecast of the stream of future dividends that would accrue to someone who bought a portfolio of the stocks that existed at that time.  He did this by observing the actual dividends that got paid out and discounting them back to the year in question.  After adjusting for the well-established trend that stock prices go up over long periods of time, Shiller found that the present value of dividends was… highly stable.  But stock prices, which we should interpret as attempts to forecast the present value of dividends, are highly variable….  (231-232)

October 1987 provides yet another example of stock prices moving much more than fundamental values.  The U.S. stock market dropped more than 25% from Thursday, October 15, 1987 to Monday, October 19, 1987.  This happened in the absence of any important news, financial or otherwise.  Writes Thaler:

If prices are too variable, then they are in some sense ‘wrong.’  It is hard to argue that the price at the close of trading on Thursday, October 15, and the price at the close of trading the following Monday – which was more than 25% lower – can both be rational measures of intrinsic value, given the absence of news.



It’s very important to note (again) that although the assumption of rationality and the EMH have been demonstrated not to be true, behavioral economists have not invented a model of human behavior that can supplant rationalist economics.  Therefore, rationalist economics, and not behaviorist economics, is still the chief basis by which economists attempt to predict human behavior.

In other words, scientists must learn much more – genetics, neuroscience, psychology, etc. – in order to better predict human behavior.  But even then, individual human behavior (to say nothing of group behavior) may remain partly unpredictable for a long period of time.

At the sub-atomic level, most top physicists believe that a part of reality is inherently unpredictable.  However, it’s always possible that this “inherent unpredictability” is a result of current limitations in theoretical physics.  Once physicists make further advances, it’s at least possible that what is currently “inherently unpredictable” may turn out to be predictable after all.  Einstein may be right when it comes to sub-atomic physics: “God does not play dice.”

Similarly with human behavior.  What will be the state of genetics, neuroscience, psychology, physics, artificial intelligence, etc. one thousand years from today?  Human behavior may be largely predictable.  But it then seems to get paradoxical.  How could one accurately predict one’s own behavior all the time, given that one could choose to act differently from one’s own predictions?  Or, if only a super-advanced artificial intelligence could aggregate enough data to predict all the behavior of a human being, when would it be legal to do so?  And who would own the super-advanced artificial intelligence, or would it be a benevolent entity guarding the universe?

In any case, rationalist economic models may continue to be useful for a long time.  In fact, rationalist models, including game theory, may also be central to predicting how various artificially intelligent agents will compete against one another.



In an efficient market, the same asset cannot sell simultaneously for two different prices.  Thaler gives the standard example of gold selling for $1,000 an ounce in New York and $1,010 an ounce in London.  If transaction costs were small enough, a smart trader could buy gold in New York and sell it in London.  This would eventually cause the two prices to converge.

But there is one obvious example that violates this law of one price:  closed-end funds, which had already been written about by Ben Graham.

For an open-end fund, all trades take place at NAV (net asset value).  Investors can purchase a stake in an open-end fund on the open market, without there having to be a seller.  So the total amount invested in an open-end fund can vary depending upon what investors do.

But for a closed-end fund, there is an initial amount invested in the fund, say $100 million, and then there can be no further investments and no withdrawals.  A closed-end fund is traded on an exchange.  So an investor can buy partial ownership of a closed-end fund, but this means that a previous owner must sell that stake to the buyer.

According to EMH, closed-end funds should trade at NAV.  But in the real world, many closed-end funds trade at prices different from NAV (sometimes a premium and sometimes a discount).  This is an obvious violation of the law of one price.

Charles Lee, Andrei Shleifer, and Richard Thaler wrote a paper on closed-end funds in which they identified four puzzles:

  • Closed-end funds are often sold by brokers with a sales commission of 7%. But within six months, the funds typically sell at a discount of more than 10%.  Why do people repeatedly pay $107 for an asset that in six months is worth $90?
  • More generally, why do closed-end funds so often trade at prices that differ from the NAV of its holdings?
  • The discounts and premia vary noticeably across time and across funds. This rules out many simple explanations.
  • When a closed-end fund, often under pressure from shareholders, changes its structure to an open-end fund, its price often converges to NAV.

The various premia and discounts on closed-end funds simply make no sense.  These mispricings would not exist if investors were rational because the only rational price for a closed-end fund is NAV.

Lee, Shleifer, and Thaler discovered that individual investors are the primary owners of closed-end funds.  So Thaler et al. hypothesized that individual investors have more noticeably shifting moods of optimism and pessimism.  Says Thaler:

We conjectured that when individual investors are feeling perky, discounts on closed-end funds shrink, but when they get depressed or scared, the discounts get bigger.  This approach was very much in the spirit of Shiller’s take on social dynamics, and investor sentiment was clearly one example of ‘animal spirits.’  (pages 241-242)

In order to measure investor sentiment, Thaler et al. used the fact that individual investors are more likely than institutional investors to own shares of small companies.  Thaler et al. reasoned that if the investor sentiment of individual investors changes, it would be apparent both in the discounts of closed-end funds and in the relative performance of small companies (vs. big companies).  And this is exactly what they found upon doing the research.  The greater the discounts to NAV for closed-end funds, the more the returns for small stocks lagged (on average) the returns for large stocks.



Years later, Thaler revisited the law of one price with a Chicago colleague, Owen Lamont.  Owen had spotted a blatant violation of the law of one price involving the company 3Com.  3Com’s main business was in networking computers using Ethernet technology, but through a merger they had acquired Palm, which made a very popular (at the time) handheld computer the Palm Pilot.

In the summer of 1999, as most tech stocks seemed to double almost monthly, 3Com stock seemed to be neglected.  So management came up with the plan to divest itself of Palm.  3Com sold about 4% of its stake in Palm to the general public and 1% to a consortium of firms.  As for the remaining 95% of Palm, each 3Com shareholder would receive 1.5 shares of Palm for each share of 3Com they owned.

Once this information was public, one could infer the following:  As soon as the initial shares of Palm were sold and started trading, 3Com shareholders would in a sense have two separate investments.  A single share of 3Com included 1.5 shares of Palm plus an interest in the remaining parts of 3Com – what’s called the “stub value” of 3Com.  Note that the remaining parts of 3Com formed a profitable business in its own right.  So the bottom line is that one share of 3Com should equal the “stub value” of 3Com plus 1.5 times the price of Palm.

When Palm started trading, it ended the day at $95 per share.  So what should one share of 3Com be worth?  It should be worth the “stub value” of 3Com – the remaining profitable businesses of 3Com (Ethernet tech, etc.) – PLUS 1.5 times the price of Palm, or 1.5 x $95, which is $143.

Again, because the “stub value” of 3Com involves a profitable business in its own right, this means that 3Com should trade at X (the stub value) plus $143, so some price over $143.

But what actually happened?  The same day Palm started trading, ending the day at $95, 3Com stock fell to $82 per share.  Thaler writes:

That means that the market was valuing the stub value of 3Com at minus $61 per share, which adds up to minus $23 billion!  You read that correctly.  The stock market was saying that the remaining 3Com business, a profitable business, was worth minus $23 billion.  (page 246)

Thaler continues:

Think of it another way.  Suppose an Econ is interested in investing in Palm.  He could pay $95 and get one share of Palm, or he could pay $82 and get one share of 3Com that includes 1.5 shares of Palm plus an interest in 3Com.

Thaler observes that two things are needed for such a blatant violation of the law of one price to emerge and persist:

  • You need some traders who want to own shares of the now publicly traded Palm, traders who appear not to realize the basic math of the situation. These traders are called noise traders, because they are trading not based on real information (or real news), but based purely on “noise.”  (The term noise traders was invented by Fischer Black.  See:  http://www.e-m-h.org/Blac86.pdf)
  • There also must be something preventing smart traders from driving prices back to where they are supposed to be. After all, the sensible investor can buy a share of 3Com for $82, and get 1.5 shares of Palm (worth $143) PLUS an interest in remaining profitable businesses of 3Com.  Actually, the rational investor would go one step further:  buy 3Com shares (at $82) and then short an appropriate number of Palm shares (at $95).  When the deal is completed and the rational investor gets 1.5 shares of Palm for each share of 3Com owned, he can then use those shares of Palm to repay the shares he borrowed earlier when shorting the publicly traded Palm stock. This was a CAN’T LOSE investment.  Then why wasn’t everyone trying to do it?

The problem was that there were very few shares of Palm being publicly traded.  Some smart traders made tens of thousands.  But there wasn’t enough publicly traded Palm stock available for any rational investor to make a huge amount of money.  So the irrational prices of 3Com and Palm were not corrected.

Thaler also tells a story about a young Benjamin Graham.  In 1923, DuPont owned a large number of shares of General Motors.  But the market value of DuPont was about the same as its stake in GM.  DuPont was a highly profitable firm.  So this meant that the stock market was putting the “stub value” of DuPont’s highly profitable business at zero.  Graham bought DuPont and sold GM short.  He made a lot of money when the price of DuPont went up to more rational levels.

In mid-2014, says Thaler, there was a point when Yahoo’s holdings of Alibaba were calculated to be worth more than the whole of Yahoo.

Sometimes, as with the closed-end funds, obvious mispricings can last for a long time, even decades.  Andrei Shleifer and Robert Vishny refer to this as the limits of arbitrage.



What are the implications of these examples?  If the law of one price can be violated in such transparently obvious cases such as these, then it is abundantly clear that even greater disparities can occur at the level of the overall market.  Recall the debate about whether there was a bubble going on in Internet stocks in the late 1990s….

Despite many market inefficiencies, the EMH – a part of rationalist economics – is still very useful:

So where do I come down on the EMH?  It should be stressed that as a normative benchmark of how the world should be, the EMH has been extraordinarily useful.  In a world of Econs, I believe that the EMH would be true.  And it would not have been possible to do research in behavioral finance without the rational model as a starting point.  Without the rational framework, there are no anomalies from which we can detect misbehavior.  Furthermore, there is not as yet a benchmark behavioral theory of asset prices that could be used as a theoretical underpinning of empirical research.  We need some starting point to organize our thoughts on any topic, and the EMH remains the best one we have.  (pages 250-251)

When it comes to the EMH as a descriptive model of asset markets, my report card is mixed.  Of the two components, using the scale sometimes used to judge claims made by political candidates, I would judge the no-free-lunch component to be ‘mostly true.’  There are definitely anomalies:  sometimes the market overreacts, and sometimes it underreacts.  But it remains the case that most active money managers fail to beat the market…

Most investors agree with Thaler on the no-free-lunch component of the EMH.  In practice, it is very difficult for any investor to beat the market over time.

But do prices always accurately reflect intrinsic value?

I have a much lower opinion about the price-is-right component of the EMH, and for many important questions, this is the more important component…

My conclusion:  the price is often wrong, and sometimes very wrong.  Furthermore, when prices diverge from fundamental value by such wide margins, the misallocation of resources can be quite big.  For example, in the United States, where home prices were rising at a national level, some regions experienced especially rapid price increases and historically high price-to-rental ratios.  Had both homeowners and lenders been Econs, they would have noticed these warning signals and realized that a fall in home prices was becoming increasingly likely.  Instead, surveys by Shiller showed that these were the regions in which expectations about the future appreciation of home prices were the most optimistic.  Instead of expecting mean reversion, people were acting as if what goes up must go up even more.

Thaler adds that policy-makers should realize that asset prices are often wrong, and sometimes very wrong, instead of assuming that prices are always right.



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

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

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

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


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

My e-mail: jb@boolefund.com




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

Lions and Tigers and Bears, Oh My!

(Image:  Zen Buddha Silence by Marilyn Barbone.)

January 29, 2017

As humans, the ancient part of our brains – including the amygdala – alerts us subconsciously if there is the slightest sight or sound of something that could be dangerous (e.g., a lion, a tiger, or a bear).  Such an alert happens so fast that our body prepares for fight or flight long before we are even conscious of the potential threat.

Unfortunately, plummeting stock prices very often trigger the same fear response in our ancient brains.  This often causes us to make bad decisions.



One key to investing is to learn to minimize the number of mistakes we make.  A great way to minimize our mistakes is to follow an automatic system for investment decisions.

For many investors, the best automatic approach is simply to buy and hold low-cost broad market index funds.  These investors, if they stick with it over several decades, will do better than 90-95% of all investors.  And this winning strategy requires very little time to implement.

For investors who want a proven, relatively low-risk way to beat the market, a quantitative value investment approach is a good option.  One of the best research papers on why a value investing strategy beats the market over time, without taking more risk, is “Contrarian Investment, Extrapolation, and Risk,” by Josef Lakonishok, Andrei Schleifer, and Robert Vishny.  Link: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf?m=1360042367

Lakonishok, Schleifer, and Vishny were so convinced by the evidence that they started LSV Asset Management, which today has over $87 billion in assets under management.  LSV uses a quantitative value investment approach which automatically selects a portfolio of stocks based on the factors supported by their research, including low price to earnings and low price to cash flow.  See:  http://lsvasset.com/



The truth is that most of us do not know ourselves very well at all.  But recent advances in psychology and neuroscience have made it possible for us to know ourselves much better.  Slowly but surely, studying our own mistakes and studying the best psychology can help us to make better decisions over time.

When it comes to decision making under uncertainty – and this includes investing – there are many great books that are absolute must-reads, including the following:

  • Daniel Kahneman, Thinking, Fast and Slow (Farrar, Straus and Giroux, 2013, 2011)
  • Charles T. Munger, Poor Charlie’s Almanack, edited by Peter Kaufman (Walsworth Publishing Company, Expanded Third Edition, 2005)

There are many more great authors on decision making under uncertainty, including Michael Mauboussin, James Montier, and Richard Thaler, to name just a few.  But in the interest of brevity, this blog post focuses on a chapter from James Montier’s Value Investing (Wiley, 2009), Chapter 13: The Psychology of Bear Markets.



Montier writes that Abraham Lincoln relayed the following story:

Solomon once charged his wise men to invent him a sentence, to be ever in view, which should be true and appropriate in all times and situations.  They presented him with the words ‘And this, too, shall pass away’.  How much it expresses!  How chastening in the hour of pride.  How consoling in the depths of affliction…  (page 123)

Unfortunately, says Montier, when it comes to investing, we are often overwhelmed by fear or greed instead of training ourselves to slow down and to use careful logical analysis.  We have two mental systems, one that is automatic and intuitive, and the other that is capable of deliberate, rational thinking.  The automatic system is the more ancient system, and we share it with many other animals.

The problem is that paper stock losses cause fear.  The fear response is very powerful and largely unconscious initially, originating in the amygdala.  It prepares the body for fight or flight even before the conscious mind has had time to analyze the situation.

Most of the time in our evolutionary history, a fear response to movement in the grass was appropriate for maximizing the chances of survival.  A rapid response to a potential threat had a low cost in the case of a false positive, notes Montier.  But a false negative – not reacting to the movement in the grass, but having it turn out to be a lion – was potentially fatal.

Yet a fear response to paper stock losses is almost always harmful for long-term investors.  When stocks drop a lot (10-40%), many long-term investors with time horizons of at least ten or twenty years are overwhelmed by fear and sell when they should be buying.  For instance, many long-term investors were overwhelmed by fear (and loss aversion) in the bear market of late 2008 to early 2009.  Long-term investors who sold missed out on a more than 200% recovery (from the lows) for the S&P 500 Index.

Conversely, long-term investors who have mastered their fears – by having an automatic investment system, or by having trained their own responses, or both – bought in late 2008 and early 2009 and are much better off as a result.



James Montier describes a study by Shiv et al. (2005).  It is a game at the start of which each player receives $20.  The game has 20 rounds.  Each round, the player can decide to bet $1 or not.  Then a fair coin is flipped.  If it comes up heads, the player who bet $1 gets $2.50 back.  If it comes up tails, the player who bet $1 loses the $1.

It is optimal for the player to bet $1 in each of the 20 rounds because the game has a clear positive expected value – the wins will outweigh the losses on average.  Also, the decision to bet $1 each round should not depend upon whether one won or lost in the previous round.  Each flip is independent of the previous flip.

What actually happened, however, was that people invested less than 40% of the time after suffering a loss.  Furthermore, the longer the game continued, the lower the percentage of players who decided to bet.

As losses add up, people become more and more fearful, and less and less willing to invest even though the best time to invest is during a period of extended losses (like late 2008 and early 2009).  Montier writes:

The parallels with bear markets are (I hope) obvious.  The evidence above suggests that it is outright fear that drives people to ignore bargains when they are available in the market, if they have previously suffered a loss.  The longer they find themselves in this position the worse their decision making appears to become.  (page 125)



Montier observes:

Investors should consider trying to adopt the Buddhist approach to time.  That is to say, the past is gone and can’t be changed, the future is unknown, and so we must focus on the present.  The decision to invest or not should be a function of the current situation (from my perspective the degree of value on offer) not governed by our prior experiences (or indeed our future hopes).  However, this blank slate is mentally very hard to achieve.  Our brains seem to be wired to focus on the short term and to fear loss in an extreme fashion.  These mental hurdles are barriers to sensible investment decision making in a bear market.  (page 127)

A bear market can occur at virtually any time.  At some point in the next five or so years – possibly even this year – there will likely be a bear market in which stocks drop at least 20-25% (and possibly much more).  It’s essential for all investors that we have an automatic, checklist-like approach to plummeting stock prices in order to maximize rational decisions and minimize irrational decisions.  It’s also essential for many of us as investors that we develop a keen understanding of our own emotional responses to plummeting stock prices.

Knowing in detail how we will feel in the face of plummeting stock prices, and having a rational set of responses (ideally automatic and preprogrammed), is one of the keys to successful long-term investing.



One important rule for long-term investors is to check stock prices as infrequently as humanly possible.  A flashing, plummeting stock price is a subliminal message that very often will activate our subconscious fear response – SELL!  SELL!

For a long-term index fund investor, it is possible to check stock prices only a few times per year or less.  Similarly for a long-term value investor, one should check prices as infrequently as possible.

If we are tempted to check stock prices, especially when prices are plummeting, there are simple techniques we can use to avoid the temptation.  Obviously being deeply engaged in a work project is ideal.  Or we can read, watch a movie, or even go to the beach.  Many long-term value investors recommend going to the beach – ideally with a stack of great books or annual reports – instead of checking or worrying about cratering prices.

Only buy something that you’d be perfectly happy to hold if the market shut down for 10 years.  – Warren Buffett

Invest only if you would be comfortable owning a stock even if you had no way of knowing its daily share price.  – Ben Graham

When we own stocks, we are part owners of the underlying businesses.  So if we are happy with the businesses we own, we should never waste time worrying about or checking falling prices.  If we understand and believe in the underlying business, then a dropping stock price is a wonderful opportunity to increase our ownership of the business.  A good rule is to add to our position for each 10% or 15% drop in price.

If we own a low-cost S&P 500 index fund, then we are part owners of five hundred American businesses.  As long as we believe the U.S. economy will do well over the coming decades – and it will do well, even if grows a bit more slowly than in the past – then when the index drops a great deal, it’s a wonderful opportunity to add to our position as a part owner of American businesses.

As long as we have uncorrelated or negatively correlated positions in our overall portfolio – such as cash, cash equivalents, or gold – it is simple to implement an automatic rebalancing rule.  For instance, during or after a period of falling stock prices, we would use some cash (or gold) to increase our ownership of the businesses we like best.  Ideally, this rebalancing is preprogrammed to occur at the end of a specified time period (e.g., quarter or year).

Just for fun, a 30-second link from the Wizard of Oz:  https://www.youtube.com/watch?v=NecK4MwOfeI



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

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

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

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


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

My e-mail: jb@boolefund.com




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

Security Analysis (Graham & Dodd)

(Image:  Zen Buddha Silence by Marilyn Barbone.)

January 22, 2017

This week, I am reviewing Security Analysis (McGraw Hill, 6th edition), by Benjamin Graham and David L. Dodd.  This blog post contains even more quotations than usual because the book is 730 pages (though I only read 75% of it, and have yet to look at the accompanying CD).

As with the other great value investors, it’s worth spending a long time soaking up the lessons of Graham and Dodd.



David Abrams, in his Introduction to Part VII, writes that the most important point from Security Analysis is this:  “look at the numbers and think for yourself.  All the great investors do, and that’s what makes them great.” (p. 631)

Or as Ben Graham has said:

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



I’ve always been deeply impressed by the kindness and generosity of many value investors, including Warren Buffett and Charlie Munger.  This is a part of the value investing tradition started by Ben Graham and David Dodd.  As Warren Buffett writes in the Forward:

In the end, that’s probably what I admire most about the two men.  It was ordained at birth that they would be brilliant; they elected to be generous and kind.

The way they behaved made as deep an impression on me – and many of my classmates – as did their ideas.  We were being taught not only how to invest wisely; we were also being taught how to live wisely.



Seth Klarman writes in the Preface to the 6th Edition – The Timeless Wisdom of Graham and Dodd:

In 1992, Tweedy, Browne Company LLC, a well-known value investment firm, published a compilation of 44 research studies entitled, ‘What Has Worked in Investing.’  The study found that what has worked is fairly simple: cheap stocks (measured by price-to-book values, price-to-earnings ratios, or dividend yields)  reliably outperform expensive ones, and stocks that have underperformed (over three- and five-year periods) subsequently beat those that have lately performed well.  In other words, value investing works!  I know of no long-time practitioner who regrets adhering to a value philosophy; few investors who embrace the fundamental principles ever abandon this investment approach for another.  (xvii)

Klarman says value investing is an art, not a science.  And there are always aspects of the future that nobody can foresee.  Even the best investors tend to be wrong about one-third of the time.  “In the end, the most successful value investors combine detailed business research and valuation work with endless discipline and patience, a well-considered sensitivity analysis, intellectual honesty, and years of analytical and investment experience.” (xviii-xix)

Klarman observes that not all value investors are alike.  Some invest in obscure micro caps, while others invest in large caps.  Some invest globally, while others focus on a single market sector like energy.  Some use a quantitative approach, while others assess “private market value.”  Some are activists, while others look for catalysts already in place (such as a spin-off, asset sale, repurchase plan, or new management).

Finally, human nature never changes.  Capital market manias regularly occur on a grand scale… Even highly capable investors can wither under the relentless message from the market that they are wrong.  The pressures to succumb are enormous;  many investment managers fear they’ll lose business if they stand too far apart from the crowd.  Some also fail to pursue value because they’ve handcuffed themselves (or been saddled by clients) with constraints preventing them from buying stocks selling at low dollar prices, small-cap stocks, stocks of companies that don’t pay dividends or are losing money, or debt instruments with below investment-grade ratings.  Many also engage in career management techniques like ‘window dressing’ their portfolios at the end of calendar quarters or selling off losers (even if they are undervalued) while buying more of the winners (even if overvalued).  Of course, for those value investors who are truly long term oriented, it is a wonderful thing that many potential competitors are thrown off course by constraints that render them unable or unwilling to effectively compete.  (xxi-xxii)

While bargains still occasionally hide in plain sight, securities today are most likely to become mispriced when they are either accidentally overlooked or deliberately avoided.  Consequently, value investors have bad to become more thoughtful about where to focus their analysis…. (xxiii)

Today’s value investors also find opportunity in the stocks and bonds of companies stigmatized on Wall Street because of involvement in protracted litigation, scandal, accounting fraud, or financial distress.  The securities of such companies sometimes trade down to bargain levels, where they become good investments for those who are able to remain stalwart in the face of bad news.

Value investors, therefore, should not try to time the market or guess whether it will rise or fall in the near term.  Rather, they should rely on a bottom-up approach, sifting the financial markets for bargains and then buying them, regardless of the level or recent direction of the market or economy.  Only when they cannot find bargains should they default to holding cash. (xxiv-xxv, my emphasis)


Another important factor for value investors to take into account is the growing propensity of the Federal Reserve to intervene in the financial markets at the first sign of trouble.  Amidst severe turbulence, the Fed frequently lowers interest rates to prop up securities prices and restore investor confidence.  While the intention of Fed officials is to maintain orderly capital markets, some money managers view Fed intervention as a virtual license to speculate.  Aggressive Fed tactics, sometimes referred to as the ‘Greenspan put’ (now the ‘Bernanke put’), create a moral hazard that encourages speculation while prolonging overvaluation…


…Selling is more difficult because it involves securities that are closer to fully priced.  As with buying, investors need a discipline for selling.  First, sell targets, once set, should be regularly adjusted to reflect all currently available information.  Second, individual investors must consider tax consequences.  Third, whether or not an investor is fully invested may influence the urgency of raising cash from a stockholding as it approaches full valuation.  The availability of better bargains might also make one a more eager seller.  Finally, value investors should completely exit a security by the time it reaches full value;  owning overvalued securities is the realm of speculation.  Value investors typically begin selling at a 10% to 20% discount to their assessment of underyling value – based on the liquidity of the security, the possible presence of a catalyst for value realization, the quality of management, the riskiness and leverage of the underlying business, and the investors’ confidence level regarding the assumptions underlying the investment.  (xxxviii)

Value investing is a get rich slow approach.  Because of innate psychological tendencies on the part of many investors, value investing is likely to continue to work over time:

The foibles of human nature that result in the mass pursuit of instant wealth and effortless gain seem certain to be with us forever.  So long as people succumb to this aspect of their natures, value investing will remain, as it has been for 75 years, a sound and low-risk approach to successful long-term investing.  (xl)



James Grant writes about the historical backdrop in the Introduction to the Sixth Edition:

Security analysis itself is a cyclical phenomenon;  it, too, goes in and out of fashion, Graham observed.  It holds a strong, intuitive appeal for the kind of businessperson who thinks about stocks the way he or she thinks about his or her own family business.  What would such a fount of common sense care about earnings momentum or Wall Street’s pseudo-scientific guesses about the economic future?  Such an investor, appraising a common stock, would much rather know what the company behind it is worth.  That is, he or she would want to study its balance sheet.  Well, Graham relates here, that kind of analysis went out of style when stocks started levitating without reference to anything except hope and prophecy.  So, by about 1927, fortune-telling and chart-reading had displaced the value discipline by which he and his partner were earning a very good living.  It is characteristic of Graham that his critique of the ‘new era’ method of investing is measured and not derisory… (11)

Grant mentions Graham’s critique of John Burr Williams’s The Theory of Investment Value.  “The rub, he pointed out, was that, in order to apply Williams’s method, one needed to make some very large assumptions about the future course of interest rates, the growth of profit, and the terminal value of the shares when growth stops.”  As Graham writes:

One wonders whether there may not be too great a discrepancy between the necessarily hit-or-miss character of these assumptions and the highly refined mathematical treatment to which they are subjected.

Grant, in similar manner to Buffett, notes the unmistakable humanity of Graham (and Dodd):

Graham’s technical accomplishments in securities analysis, by themselves, could hardly have carried Security Analysis through its five editions.  It’s the book’s humanity and good humor that, to me, explain its long life and the adoring loyalty of a certain remnant of Graham readers, myself included.  Was there ever a Wall Street moneymaker better steeped than Graham in classical languages and literature and in the financial history of his own time?  I would bet ‘no’ with all the confidence of a value investor laying down money to buy an especially cheap stock.  (18-19)



Roger Lowenstein authored the Introduction to Part I:

In 25 years as a financial journalist, virtually all of the investors of this writer’s acquaintance who have consistently earned superior profits have been Graham-and-Dodders.  (40)

It took Graham 20 years – which is to say, a complete cycle from the bull market of the Roaring Twenties through the dark, nearly ruinous days of the early 1930s – to refine his investment philosophy into a discipline that was as rigorous as the Euclidean theorems he had studied in college.  (41)

The changes in the marketplace have been so profound that it might seem astonishing that an investment manual written in the 1930s would have any relevance today.  But human nature doesn’t change.  People still oscillate between manic highs and depressive lows, and in their hunger for instant profits, their distaste for the hard labor of serious study and for independent thought, modern investors look very much like their grandfathers and even their great-grandfathers.  Then as now, it takes discipline to overcome the demons (largely emotional) that impede most investors.  And the essentials of security analysis have not much changed.  (42-43)

Finding Bargains

Individual stocks are often cheap when a whole industry or group of securities has been sold down indiscriminately. (50)

In 2001, for instance, energy stocks were cheap (as was the price of oil).  Graham and Dodd would not have advised speculating on the price of oil – which is dependent on myriad uncertain factors from OPEC to the growth rate of China’s economy to the weather.  But because the industry was depressed, drilling companies were selling for less than the value of their equipment.  Ensco International was trading at less than $15 per share, while the replacement value of its rigs was estimated at $35.  Patterson-UTI Energy owned some 350 rigs worth about $2.8 billion.  Yet its stock was trading for only $1 billion.  Investors were getting the assets at a huge discount.  Though the subsequent oil price rise made these stocks home runs, the key point is that the investments weren’t dependent on the oil price.  Graham and Dodd investors bought into these stocks with a substantial margin of safety. (51)

Forecasting Flows

In estimating future earnings (for any sort of business), Security Analysis provides two vital rules.  One, as noted, is that companies with stable earnings are easier to forecast and hence preferable.

The second point relates to the tendency of earnings to fluctuate, at least somewhat, in a cyclical pattern.  Therefore, Graham and Dodd made a vital (and oft-overlooked) distinction.  A firm’s average earnings can provide a rough guide to the future; the earnings trend is far less reliable…

An investor in U.S. securities thus faces a challenge unimaginable to Graham and Dodd.  Where the latter suffered a paucity of information, investors today confront a surfeit…



Graham defines intrinsic value as follows:

In general terms it is understood to be that value which is justified by the facts, e.g., the assets, earnings, dividends, definite prospects, as distinct, let us say, from market quotations established by artificial manipulation or distorted by psychological excess.  But it is a great mistake to imagine that intrinsic value is as definite and as determinable as is the market price. (64)

For Graham, net asset value and earnings power were two primary ways of estimating intrinsic value.  And net asset value often meant liquidation value, largely because Graham was writing during the Great Depression.  He returns to this concept later in the book.

By earnings power, Graham means normal earnings, or what today is referred to as normalized earnings:  what the company can safely be assumed to earn in a “normal” economic environment.  Net asset value – especially liquidation value – is not necessarily more precise than earnings power.  But net asset value is less subject to change than earnings power.

Regarding earnings power, Graham writes:

But the phrase ‘earnings power’ must imply a fairly confident expectation of certain future results.  It is not sufficient to know what the past earnings have averaged, or even that they disclose a definite line of growth or decline.  There must be plausible grounds for believing that this average or this trend is a dependable guide to the future.  Experience has shown only too forcibly that in many instances this is far from true.  This means that the concept of ‘earnings power,’ expressed as a definite figure, and the derived concept of intrinsic value, as something equally definite and ascertainable, cannot be safely accepted as a general premise of security analysis.  (65)

Intrinsic value, whether based on asset value or earnings power, is always an estimate.  But in many cases, an estimate is all that is needed:

The essential point is that security analysis does not seek to determine exactly what is the intrinsic value of a given security.  It needs only to establish either that the value is adequate – e.g., to protect a bond or to justify a stock purchase – or else that the value is considerably higher or considerably lower than the market price.  For such purposes an indefinite and approximate measure of the intrinsic value may be sufficient.  To use a homely simile, it is quite possible to decide by inspection that a woman is old enough to vote without knowing her age or that a man is heavier than he should be without knowing his exact weight.  (66)

It would follow that even a very indefinite idea of the intrinsic value may still justify a conclusion if the current price falls far outside either the maximum or minimum appraisal.  (67)

Human emotion often plays a significant role in determining stock prices:

…the market is not a weighing machine, on which the value of each issue is recorded by an exact and impersonal mechanism, in accordance with specific qualities.  Rather should we say that the market is a voting machine, whereon countless individuals register choices which are the product partly of reason and partly of emotion.  (70)



Nearly every issue might conceivably be cheap in one price range and dear in another. (80)

Not only can one overpay for a good business.  But what appears to be a good business may not remain a good business.

Many of the leading enterprises of yesterday are today far back in the ranks.  Tomorrow is likely to tell a similar story.  The most impressive illustration is afforded by the persistent decline in the relative investment position of the railroads as a class during the past two decades.

Graham always emphasizes skepticism and independent thinking:

The analyst must pay respectful attention to the judgment of the market place and to the enterprises which it strongly favors, but he must retain an independent and critical viewpoint.  Nor should he hesitate to condemn the popular and espouse the unpopular when reasons sufficiently weighty and convincing are at hand. (81)

Graham writes that quantitative factors are more easily analyzed than qualitative factors:

Broadly speaking, the quantitative factors lend themselves far better to thoroughgoing analysis than do the qualitative factors.  The former are fewer in number, more easily obtainable, and much better suited to the forming of definite and dependable conclusions.  Furthermore the financial results will themselves epitomize many of the qualitative elements, so that a detailed study of the latter may not add much of importance to the picture.  (81-82)

The qualitative factors upon which most stress is laid are the nature of the business and the character of management.  These elements are exceedingly important, but they are also exceedingly difficult to deal with intelligently.  (83)

Most businesses and industries experience reversion to the mean, according to Graham:

Abnormally good or abnormally bad conditions do not last forever.  This is true not only of general business but of particular industries as well.  Corrective forces are often set in motion which tend to restore profits where they have disappeared, or to reduce them where they are excessive in relation to capital.

The best measurement of management is a superior track record over time.  It’s important not to double count the quality of management:

The most convincing proof of capable management lies in a superior comparative record over a period of time.  But this brings us back to the quantitative data.

There is a strong tendency in the stock market to value the management factor twice in its calculations.  Stock prices reflect the large earnings which the good management has produced, plus a substantial increment for ‘good management’ considered separately.  This amounts to ‘counting the same trick twice,’ and it proves a frequent cause of overvaluation. (84)

But while a trend shown in the past is a fact, a ‘future trend’ is only an assumption.  The factors that we mentioned previously as militating against the maintenance of abnormal prosperity or depression are equally opposed to the indefinite continuance of an upward or downward trend.  By the time the trend has become clearly noticeable, conditions may well be ripe for a change. (84)

During the Great Depression, many companies were trading below liquidation value because their earnings were weak or inconsistent.  Given this extended bad economic environment, it stands to reason that Graham emphasized definite values – such as liquidation values – as opposed to future earnings.  Again here:

Analysis is concerned primarily with values which are supported by the facts and not with those which depend largely upon expectations.  In this respect the analyst’s approach is diametrically opposed to that of the speculator, meaning thereby one whose success turns upon his ability to forecast or to guess future developments.  Needless to say, the analyst must take possible future changes into account, but his primary aim is not so much to profit from them as to guard against them.  Broadly speaking, he views the business future as a hazard which his conclusions must encounter rather than as the source of his vindication. (86)

It follows that the qualitative factor in which the analyst should properly be most interested is that of inherent stability.  For stability means resistance to change and hence greater dependability for the results shown in the past…. in our opinion stability is really a qualitative trait, because it derives in the first instance from the character of the business and not from its statistical record.  A stable record suggest that the business is inherently stable, but this suggestion may be rebutted by other considerations.  (87)

In the mathematical phrase, a satisfactory statistical exhibit is a necessary though by no means a sufficient condition for a favorable decision by the analyst.  (88)



It must never be forgotten that a stockholder is an owner of the business and an employer of its officers.  He is entitled not only to ask legitimate questions but also to have them answered, unless there is some persuasive reason to the contrary.

Insufficient attention has been paid to this all-important point.  The courts have generally held that a bona fide stockholder has the same right to full information as a partner in a private business.  This right may not be exercised to the detriment of the corporation, but the burden of proof rests upon the management to show an improper motive behind the request or that disclosure of the information would work an injury to the business.

Compelling a company to supply information involves expensive legal proceedings and hence few shareholders are in a position to assert their rights to the limit.  Experience shows, however, that vigorous demands for legitimate information are frequently acceded to even by the most recalcitrant managements.  This is particularly true when the information asked for is no more than that which is regularly published by other companies in the same field.  (98)



An investment operation is one which, upon thorough analysis, promises safety of principal and a satisfactory return.  Operations not meeting these requirements are speculative.

… We speak of an investment operation rather than an issue or a purchase, for several reasons.  It is unsound to think always of investment character as inhering in an issue per se.  The price is frequently an essential element, so that a stock (and even a bond) may have investment merit at one price level but not at another.  Furthermore, an investment might be justified in a group of issues, which would not be sufficiently safe if made in any one of them singly.  In other words, diversification might be necessary to reduce the risk involved in the separate issues to the minimum consonant with the requirements of investment.  (This would be true, in general, of purchases of common stocks for investment.)

In our view it is also proper to consider as investment operations certain types of arbitrage and hedging commitments which involve the sale of one security against the purchase of another.  In these operations the element of safety is provided by the combination of purchase and sale.  This is an extension of the ordinary concept of investment, but one which appears to the writers to be entirely logical.  (106)



J. Ezra Merkin, in the Introduction to Part III, writes that a new valuation benchmark was established in 1958:

A few years later, in 1958, equity dividend yields fell below bond yields for the first time.  A sensible investor putting money to work at the time could hardly credit the change as part of a permanent new reality.  To the contrary, it must have seemed a mandate to short the stock market.  Think of all the money lost over all the years by the true believers who have argued: ‘This time is different.’  Yet the seasoned professionals of that time were cautious and wrong, and the irreverent optimists were right.  This time, it really was different.  From the safe perspective of a half century, it seems incontrovertible that a new valuation benchmark had been established. (287-288)

In 1963, Ben Graham gave a fascinating lecture.  Graham stated that, because of a permanently more stimulative policy by the U.S. government, the U.S. stock market should be valued 50% higher than before.  Here is a link to the lecture:  http://www.jasonzweig.com/wp-content/uploads/2015/03/BG-speech-SF-1963.pdf

This is relevant today.  If U.S. interest rates return to 3%, 4%, 5%, or more within the next decade or so, then the S&P 500 Index today is quite overvalued (based on measures such as the CAPE and the q-ratio).  However, if U.S. interest rates stay near 0% for several decades, then the S&P 500 Index today may not be overvalued at all.  Very low rates, if extended far enough into the future, would actually make the S&P 500 undervalued today.  As Warren Buffett has noted, near-zero rates for many decades would eventually lead to a normal P/E level of 40, 50, or even higher.

Currently the high debt levels and relatively slow growth in the U.S. mean that rates could stay near zero for a long time.  (This may be more true for Europe and Japan.)  On the other hand, there could be an economically significant innovation explosion – perhaps driven by artificial intelligence, renewable energy, and/or space colonization – in which case the U.S. would grow faster, debts would come down, and interest rates would move higher again.



In the Introduction to Part IV, Bruce Berkowitz explains how to calculate the intrinsic value of any business:

… That’s the amount of cash an owner can pocket after paying all expenses and making whatever investments are necessary to maintain the business.  This free cash flow is the well from which all returns are drawn, whether they are dividends, stock buybacks, or investments capable of enhancing future returns.  (339)

Free cash flow is what Buffett calls owner earnings, because it represents what can be taken out of the business without impairing its competitive position.  Intrinsic value can thus be estimated by all future free cash flow or by all future dividends.  Berkowitz writes:

Graham and Dodd were among the first to apply careful financial analysis to common stocks… With bonds, the returns consist of specific payments made under contractual commitments.  With stocks, the returns consist of dividends that are paid from the earnings of the business, or cash that could have been used to pay dividends that was instead reinvested in the business.

By examining the assets of a business and their earnings (or cash flow) power, Graham and Dodd argued that the value of future returns could be calculated with reasonable accuracy.  (340)

To value equities, we at Fairholme begin by calculating free cash flow.  We start with net income as defined under Generally Accepted Accounting Principles (GAAP).  Then we add back noncash charges such as depreciation and amortization, which are formulaic calculations based on historical costs (depreciation for tangible assets, amortization for intangibles) and may not reflect a reduction in those assets’ true worth.

Even so, most assets deteriorate in value over time, and we have to account for that.  So we subtract an estimate of the company’s cost of maintaining tangible assets such as the office, plant, inventory, and equipment;  and intangible assets like customer traffic and brand identity.  Investment at this level, properly deployed, should keep the profits of the business in a steady state.

That is only the beginning.  For instance, companies often misstate the costs of employees’ pension and postretirement medical benefits…

Companies often lowball what they pay management.  For instance, until the last several years, most companies did not count the costs of stock option grants as employee compensation, nor did the costs show up in any other line item…

Another source of accounting-derived profits comes from long-term supply contracts.  For instance, when now-defunct Enron entered into a long-term trading or supply arrangement, the company very optimistically estimated the net present value of future profits from the deal and put that into the current year’s earnings even though no cash was received…

Some companies understate free cash flow because they expense the cost of what are really investments in growth…

All of these noncash accounting conventions illustrate the difficulty of identifying a company’s current free cash flow.  Still, we are far from done.  My associates and I next want to know (a) how representative is current cash flow of average past flow, and (b) is it increasing or decreasing – that is, does the company face headwinds or ride on tailwinds?  (341-342)



Speculation, in its etymology, meant looking forward;  investment was allied to ‘vested interests’ – to property rights and values taking root in the past.  The future was uncertain, therefore speculative;  the past was known, therefore the source of safety.  (354)

Another useful approach to the attitude of the prewar common-stock investor is from the standpoint of taking an interest in a private business.  The typical common-stock investor was a business man, and it seemed sensible to him to value any corporate enterprise in much the same manner as he would value his own business.  This meant that he gave at least as much attention to the asset values behind the shares as he did to their earnings records.  It is essential to bear in mind the fact that a private business has always been valued primarily on the basis of the ‘net worth’ as shown by its statement.  A man contemplating the purchase of a partnership or stock interest in a private undertaking will always start with the value of that interest as shown ‘on the books,’ i.e., the balance sheet, and will then consider whether or not the record and prospects are good enough to make such a commitment attractive.  An interest in a private business may of course be sold for more or less than its proportionate asset value;  but the book value is still invariably the starting point of the calculation, and the deal is finally made and viewed in terms of the premium or discount from book value involved. (355)

Broadly speaking, the same attitude was formerly taken in an investment purchase of a marketable common stock.  The first point of departure was the par value, presumably representing the amount of cash or property originally paid into the business;  the second basal figure was the book value, representing the par value plus a ratable interest in the accumulated surplus.  Hence in considering a common stock, investors asked themselves: ‘Is this issue a desirable purchase at the premium above book value, or the discount below book value, represented by the market price?’

… We thus see that investment in common stocks was formerly based upon the threefold concept of:  (1) a suitable and established dividend return, (2) a stable and adequate earnings record, and (3) a satisfactory backing of tangible assets.  Each of these three elements could be made the subject of careful analytical study, viewing the issue both by itself and in comparison with others of its class.  Common-stock commitments motivated by any other viewpoint were characterized as speculative, and it was not expected that they should be justified by a serious analysis. (356)

In the bull market leading up to 1929, people had developed a completely different attitude.  In the new-era theory, the value of a stock depended entirely on what it would earn in the future.  From this dictum the following corollaries were drawn:

  • That the dividend rate should have slight bearing upon the value.
  • That since no relationship apparently existed between assets and earnings power, the asset value was entirely devoid of importance.
  • That past earnings were significant only to the extent that they indicated what changes in the earnings were likely to take place in the future.

One reason for the new-era theory of common stocks was that a long historical record of dividends or earnings was not found to be a good guide to the future of a business.  Some businesses – after a decade of prosperity – went into insolvency.  Other companies – after being small or unsuccessful or of doubtful repute – quickly became large businesses with strong earnings and the highest rating.  As Graham explains:

In the face of all this instability it was inevitable that the threefold basis of common-stock investment should prove totally inadequate.  Past earnings and dividends could no longer be considered, in themselves, an index of future earnings and dividends.  Furthermore, these future earnings showed no tendency whatever to be controlled by the amount of the actual investment in the business – the asset values – but instead depended entirely upon a favorable industrial position and upon capable or fortunate managerial policies.  In numerous cases of receivership, the current assets dwindled, and the fixed assets proved almost worthless.  Because of this absence of any connection between both assets and earnings and between assets and realizable values in bankruptcy, less and less attention came to be paid either by financial writers or by the general public to the formerly important question of ‘net worth,’ or ‘book value’;  and it may be said that by 1929 book value had practically disappeared as an element in determining the attractiveness of a security issue. (357-358)

Another part of the new-era theory of common stocks was that common stocks were the most profitable long-term investment.  The record showed that common stocks produced both higher income and greater principal profit than standard bonds.  Thus, according to Graham, the new-era theory was as follows:

  • The value of a common stock depends on what it can earn in the future.
  • Good common stocks are those which have shown a rising trend of earnings.
  • Good common stocks will prove sound and profitable investments.

Graham comments:

These statements sound innocent and plausible.  Yet they concealed two theoretical weaknesses that could and did result in untold mischief.  The first of these defects was that they abolished the fundamental distinctions between investment and speculation.  The second was that they ignored the price of a stock in determining whether or not it was a desirable purchase. (359)

In essence, then, the new-era theory of investment was “old-style speculation”:  Common stocks were preferred to bonds, capital gains were preferred to dividends, and future estimates were more important than past records.  And most incredibly of all, the desirability of a common stock “was entirely independent of its price.”  Paying $100 per share for earnings of $2.50 per share was typical.  And the same reasoning was used as a basis to pay $200, $1,000, or any price for the same $2.50 per share in earnings.  Graham continues:

An alluring corollary of this principle was that making money in the stock market was now the easiest thing in the world.  It was only necessary to buy ‘good’ stocks, regardless of price, and then to let nature take her upward course…  (360)

Graham found it ironic that the investment trusts of the day completed adopted the new-era view of investments.

…The investment process consisted merely of finding prominent companies with a rising trend of earnings and then buying their shares regardless of price.  Hence the sound policy was to buy only what every one else was buying – a select list of highly popular and exceedingly expensive issues, appropriately known as ‘blue chips.’  The original idea of searching for the undervalued and neglected issues dropped completely out of sight.  Investment trusts actually boasted that their portfolios consisted exclusively of the active and standard (i.e., the most popular and highest priced) common stocks.  With but slight exaggeration, it might be asserted that under this convenient technique of investment, the affairs of a ten-million dollar investment trust could be administered by the intelligence, the training and the actual labors of a single thirty-dollar-a-week clerk.

The man in the street, having been urged to entrust his funds to the superior skill of investment experts – for substantial compensation – was soon reassuringly told that the trusts would be careful to buy nothing except what the man in the street was buying himself.

Thus, investors were deceiving themselves based on the long-term superior record of stocks as compared to bonds.  Here is the problem with that argument, says Graham:

… This would be true, typically, of a stock earning $10 and selling at 100.  But as soon as the price was advanced to a much higher price in relation to earnings, this advantage disappeared, and with it disappeared the entire theoretical basis for investment purchases of common stocks.  When in 1929 investors paid $200 per share for a stock earning $8, they were buying an earning power no greater than the bond-interest rate, without the extra protection afforded by a prior claim.  Hence in using the past performances of common stocks as the reason for paying prices 20 to 40 times their earnings, the new-era exponents were starting with a sound premise and twisting it into a woefully unsound conclusion. (362)



Is it possible to tell when a stock price is too high?

… We think that there is no good answer to this question – in fact we are inclined to think that even if one knew for a certainty just what a company is fated to earn over a long period of years, it would still be impossible to tell what is a fair price to pay for it today.  It follows that once the investor pays a substantial amount for the growth factor, he is inevitably assuming certain kinds of risks;  viz., that the growth will be less than he anticipates, that over the long pull he will have paid too much for what he gets, that for a considerable period the market will value the stock less optimistically than he does.

Graham argues that it’s advantageous to buy unpopular stocks at prices that are low or at prices that accord with private market value (what a prudent business man would pay for the business if it were private):

On the other hand, assume that the investor strives to avoid paying a high premium for future prospects by choosing companies about which he is personally optimistic, although they are not favorites of the stock market.  No doubt this is the type of judgment that, if sound, will prove most remunerative.  But, by the very nature of the case, it must represent the activity of strong-minded and daring individuals rather than investment in accordance with accepted rules and standards.

… we repeat that this method may be followed successfully if it is pursued with skill, intelligence and diligent study.  If so, is it appropriate to call such purchases by the name ‘investment’?  Our answer is ‘yes,’ provided that two factors are present:  the first, already mentioned, that the elements affecting the future are examined with real care and a wholesome scepticism, rather than accepted quickly via some easy generalization;  the second, that the price paid be not substantially different from what a prudent business man would be willing to pay for a similar opportunity presented to him to invest in a private undertaking over which he could exercise control. (371-372)



In the Introduction to Part V, Glenn Greenberg writes:

[Security Analysis] is the more remarkable because it was written during the uniquely depressed circumstances of 1934, a nation of 25% unemployment with most businesses struggling to survive.  Yet Graham and Dodd were able to codify the principles that have inspired great investors through 75 years of remarkable prosperity.  Their insights are as applicable now as ever.  (396)

Graham holds that past earnings are an important, but not very reliable, guide to the future:

… This is at once the most important and the least satisfactory aspect of security analysis.  It is the most important because the sole practical value of our laborious study of the past lies in the clue it may offer to the future;  it is the least satisfactory because this clue is never thoroughly reliable and it frequently turns out to be quite valueless.  These shortcomings detract seriously from the value of the analyst’s work, but they do not destroy it.  The past exhibit remains a sufficiently dependable guide, in a sufficient proportion of cases, to warrant its continued use as the chief point of departure in the valuation and selection of securities.  (472)

When Graham discusses earnings power, he means normal earnings:

The concept of earnings power has a definite and important place in investment theory.  It combines a statement of actual earnings over a period of years, with a reasonable expectation that these will be approximated in the future, unless extraordinary conditions supervene.  The record must cover a number of years, first because a continued or repeated performance is always more impressive than a single occurrence and secondly because the average of a fairly long period will tend to absorb and equalize the distorting influences of the business cycle.

A distinction must be drawn, however, between an average that is the mere arithmetical resultant of an assortment of disconnected figures and an average that is ‘normal’ or ‘modal,’ in the sense that the annual results show a definite tendency to approximate the average. (472)

According to Graham, a qualitative study of the nature of the business – e.g., its competitive position – is an important part of determining normal earnings:

In order for a company’s business to be regarded as reasonably stable, it does not suffice that the past record should show stability.  The nature of the undertaking, considered apart from any figures, must be such as to indicate an inherent permanence of earning power.  (474)

Given the importance of normal earnings, it follows that current earnings are not a primary basis for estimating intrinsic value.  Graham writes:

The market level of common stocks is governed more by their current earnings than by their long-term average.  This fact accounts in good part for the wide fluctuations in common-stock prices, which largely (though by no means invariably) parallel the changes in their earnings between good years and bad.  Obviously the stock market is quite irrational in thus varying its valuation of a company proportionately with the temporary changes in its reported profits.  A private business might easily earn twice as much in a boom year as in poor times, but its owner would never think of correspondingly marking up or down the value of his capital investment.

This is one of the most important lines of cleavage between Wall Street practice and the canons of ordinary business.  Because the speculative public is clearly wrong in its attitude on this point, it would seem that its errors should afford profitable opportunities to the more logically minded to buy common stocks at the low prices occasioned by temporarily reduced earnings and to sell them at inflated levels created by abnormal prosperity.

… We have here the long-accepted and classical formula for ‘beating the stock market.’  Obviously it requires strength of character in order to think and to act in opposite fashion from the crowd and also patience to wait for opportunities that may be spaced years apart.  But there are still other considerations that greatly complicate this apparently simple rule for successful operations in stocks.  In actual practice the selection of suitable buying and selling levels becomes a difficult matter…. (476-477)

Graham makes it clear that most of the time, abnormally low current earnings later recover to normal levels, while abnormally high current earnings later revert to more normal levels.  However, sometimes low earnings do not recover, and sometimes high earnings remain high or go higher.  Thus, the analyst must carefully assess each situation in order to determine the approximate level of normal earnings, and whether this level has changed from before.  Yet to be conservative, argues Graham, if normal earnings are higher than in the past, the analyst should use the past level as a basis for estimating intrinsic value.

If earnings show a downward trend, that often will create an irrationally low stock price.  In these cases, Graham argues that one should view such a business as a sensible businessman would:  considering the pros and cons, what would the enterprise be worth to a private owner?



A given common stock is generally considered to be worth a certain number of times its current earnings.

Subsequent to 1932 there developed a tendency for prices to rule higher in relation to earnings because of the sharp drop in long-term interest rates. (497)

Intrinsic value is an estimate rather than an exact figure.  Net asset value is an estimate.  And current earnings change all the time.  Moreover, investor emotions are a component of stock prices:

… Hence the prices of common stocks are not carefully thought out computations but the resultants of a welter of human reactions.  The stock market is a voting machine rather than a weighing machine.  It responds to factual data not directly but only as they affect the decisions of buyers and sellers.  (497)

Graham explains the conditions under which current earnings may be considered normal earnings.  Graham also explains when the analyst may even set future normal earnings as higher than at any time in the past:

… His fundamental basis of appraisal must be an intelligent and conservative estimate of the future earning power.  But his measure of future earnings can be conservative only if it is limited by actual performance over a period of time.  We have suggested, however, that the profits of the most recent year, taken singly, might be accepted as the gage of future earnings, if (1) general business conditions in that year were not exceptionally good, (2) the company has shown an upward trend of earnings for some years past and (3) the investor’s study of the industry gives him confidence in its continued growth.  In a very exceptional base, the investor may be justified in counting on higher earnings in the future than at any time in the past.  This might follow from developments involving a patent or the discovery of new ore in a mine or some similar specific and significant occurrence.  But in most instances he will derive the investment value of a common stock from the average earnings of a period between five and ten years.  This does not mean that all common stocks with the same average earnings should have the same value.  The common-stock investor (i.e., the conservative buyer) will properly accord a more liberal valuation to those issues which have current earnings above the average or which may reasonably be considered to possess better than average prospects or an inherently stable earnings power.  But it is the essence of our viewpoint that some moderate upper limit must in every case be placed on the multiplier in order to stay within the bounds of conservative valuation.  We would suggest that about 20 times average earnings is as high a price as can be paid in an investment purchase of a common stock.

Most of the time, average earnings based on five or ten years is a reasonable estimate for normal earnings.  But the analyst should also make adjustments for companies with better than average prospects and for companies with more stable earnings.

Graham suggests that 20 times earnings is as high a price as a conservative investor should ever pay for an investment (as opposed to a speculation):

… it is difficult to see how average earnings of less than 5% upon the market price could ever be considered as vindicating that price.

Given that 20 times earnings is the upper limit, it is natural to ask what the typical multiple might be.  Graham answers:

… This suggests that about 12 or 12 ½ times average earnings may be suitable for the typical case of a company with neutral prospects.  We must emphasize also that a reasonable ratio of market price to average earnings is not the only requisite for a common-stock investment.  It is a necessary but not a sufficient condition.  The company must be satisfactory also in its financial set-up and management, and not unsatisfactory in its prospects. (499)



… there is some tendency for speculatively capitalized enterprises to sell at relatively high values in the aggregate during good times or good markets.  Conversely, of course, they may be subject to a greater degree of undervaluation in depression.  There is, however, a real advantage in the fact that such issues, when selling on a deflated basis, can advance much further than they can decline. (512-513)

Graham gives an example of 400-bagger (American Water Works and Electric Company).  Because the company was highly indebted, when gross revenues grew about 160%, per share earnings increased dramatically more.

Highly indebted companies tend to sell at very low prices when their earnings are abnormally low, which often creates an investment opportunity:

The overdeflation of a speculative issue … in unfavorable markets creates the possibility of an amazing price advance when conditions improve, because the earnings per share then show so violent an increase. (517)

In effect, the equity holder of a highly indebted enterprise operates with relatively little capital relative to the debt holders.  The equity holder in this case may have a similar downside to the debt holder, but dramatically higher upside.  Graham says the equity holder has a “cheap call” on the future profits of the highly indebted enterprise.



Low-priced stocks seem to have an “inherent arithmetical advantage” because they can increase much more than they can decrease.  What often creates a low-priced stock is when current profits are low in relation to the size of the enterprise.  Thus, like an equity stub, a low-priced stock can be thought of as a “cheap call” on the future profits of the enterprise.

Graham notes that unusually high operating or production costs can have the same effect as high debt levels:

The speculative or marginal position may arise from any cause that reduces the percentage of gross available for the common to a subnormal figure and that therefore serves to create a subnormal value for the common stock in relation to the volume of business.  Unusually high operating or production costs have the identical effect as excessive senior charges in cutting down the percentage of gross available for common…. (525)



In the Introduction to Part VI, Bruce Greenwald notes how Graham and Dodd define intrinsic value:

that value which is justified by the facts, e.g., the assets, earnings, dividends, [and] definite prospects, as distinct, let us say, from market quotations established by market manipulation or distorted by psychological excesses. (64)

If perfect information were possible, then intrinsic value would be identical to true value.  But Graham and Dodd understood that there are always uncertainties, both with regard to current information and with regard to the future.  Therefore, intrinsic value must be an estimate.  But even as an estimate, intrinsic value plays a vital role, as Greenwald explains:

… It served first of all to organize examination and use of the available information, ensuring that the relevant facts would be brought to bear and irrelevant noise ignored.  Second, it would produce an appreciation of the range of uncertainty associated with any particular intrinsic value calculation.  Graham and Dodd recognized that even a very imperfect intrinsic value would be useful in making investment decisions. 

The purchase of securities should then be made only at prices far enough below the intrinsic value to provide a margin of safety that would offer appropriate protection against this ‘indistinctness’ in the calculated intrinsic value.  In essence, what Graham and Dodd required was that an investor, as opposed to a speculator, should know as far as possible the value of any security purchased and also the degree of uncertainty attached to that value.  (536)

Greenwald identifies four areas that Graham and Dodd describe as being useful for balance sheet analysis:

…First, the balance sheet identifies the quantity and nature of resources tied up in a business.  For an economically viable enterprise, these resources are the basis of its returns.  In a competitive environment, a firm without resources cannot generally expect to earn any significant profits.  If an enterprise is not economically viable, then the balance sheet can be used to identify the resources that can be recovered in liquidation and how much cash the resources might return.

Second, the resources on a balance sheet provide a basis for analyzing the nature and stability of sources of income… earnings estimates will be more realistic and accurate if they are supported by appropriate asset values…

Third, the liabilities side of the balance sheet, which identifies sources of funding, describes the financial condition of the firm…

Fourth, the evolution of the balance sheet over time provides a check on the quality of earnings…

A balance sheet is a snapshot of a company’s assets and liabilities at a particular time.  It can be checked for accuracy and value at that moment.  This places significant constraints on the degree to which the assets and liabilities can be manipulated.  In contrast, flow variables such as revenue and earnings measure changes over time that by their nature are evanescent.  If they are to be monitored, they must be monitored over an extended period.  In 1934, and today, this fundamental difference accounts for the superior reliability (in theory) of balance sheet figures.  (538-539)

Greenwald discusses the net-net approach advocated and used by Graham and Dodd.  If current assets minus all liabilities is positive, and if the stock can be purchased below that level, then the downside is typically limited, while the upside is often substantial.  There were many net-nets during the Great Depression, but there are far fewer these days, although occasionally they show up during bear markets and/or in foreign markets.  (Value investors bought net-nets in South Korea some years ago.)  Despite the virtual disappearance of net-nets, Greenwald observes that the general lessons still hold:

However, the broader lessons that led Graham and Dodd to focus on the balance sheets of firms continue to apply, with extensions that are much within the spirit of their original approach.  First, it is now recognized that for economically viable firms, assets wear out or become obsolete and have to be replaced.  Thus, replacement value – the lowest possible cost of reproducing a firm’s net assets by the competitors who are best positioned to do it – continues to serve the role that Graham and Dodd recognized.  If projected profit levels for a firm imply a return on assets well above the cost of capital, then competitors will be drawn in.  That, in turn, will drive down profits and with them the value of the firm.  Thus, earnings power unsupported by asset values – measured as reproduction values – will, absent special circumstances, always be at risk from erosion due to competition.  Both ‘safety of principal’ and the promise of a ‘satisfactory return,’ therefore, require that ‘thorough’ investors support their earnings projections with a careful assessment of the replacement values of a firm’s assets.  Investors who do this will have an advantage over those who do not, and they should outperform these less thorough investors in the long run.  (541-542)



Book value often means asset value, or tangible asset value, or tangible book value.  Graham introduces his definition of net-nets based upon current-asset value, which is current assets minus all liabilities.  A net-net is when the stock can be purchased below current-asset value.  Later, Graham discusses financial reasoning versus business reasoning:

We have here the point that brings home more strikingly perhaps than any other the widened rift between financial thought and ordinary business thought.  It is an almost unbelievable fact that Wall Street never asks, ‘How much is the business selling for?’  Yet this should be the first question on considering a stock purchase.  If a business man were offered a 5% interest in some concern for $10,000, his first mental process would be to multiply the asked price by 20 and thus establish a proposed value of $200,000 for the entire undertaking.  The rest of his calculation would turn about the question whether or not the business was a ‘good buy’ at $200,000.  (555-556)

Graham explains the reasons why buying above tangible book is often not a good idea, while buying below tangible book is often a good idea:

There are indeed certain presumptions in favor of purchases made far below asset value and against those made at a high premium above it.  (It is assumed that in the ordinary case the book figures may be accepted as roughly indicative of the actual cash invested in the enterprise.)  A business that sells at a premium does so because it earns a large return upon its capital;  this large return attracts competition, and, generally speaking, it is not likely to continue indefinitely.  Conversely in the case of a business selling at a large discount because of abnormally low earnings.  The absence of new competition, the withdrawal of old competition from the field, and other natural economic forces may tend eventually to improve the situation and restore a normal rate of profit on the investment.  (557)

Graham then points out that while this is often true, it is not certain enough to be used categorically.  Rather, Graham advises that the analyst work to understand each individual case in order to act sensibly.



Graham begins by noting that with regards to unprofitable businesses, the liquidation of private businesses is “infinitely more frequent” than the liquidation of public businesses.  When Security Analysis was written, during the Great Depression, there were many public businesses selling below liquidation value.

Graham outlines a conservative way to calculate liquidation value:

A company’s balance sheet does not convey exact information as to its value in liquidation, but it does supply clues or hints which may prove useful.  The first rule in calculating liquidating value is that the liabilities are real but the value of the assets must be questioned.  This means that all true liabilities shown on the books must be deducted at their face amount.  The value to be ascribed to the assets, however, will vary according to their character.  The following schedule indicates fairly well the relative dependability of various types of assets in liquidation.  (560)

In a table, Graham explains that cash assets are valued at 100% of book value, receivables at roughly 80%, inventories at roughly 66.7%, and fixed assets at approximately 15%.  Of course, notes Graham, there is a wide range in most of these categories depending upon the business or industry in question.

Graham tries to explain the wide availability of businesses below liquidation values:

…Evidently the phenomena of 1932 (and 1938) were the direct out-growth of the new-era doctrine which transferred all the tests of value to the income account and completely ignored the balance-sheet picture.  In consequence, a company without current earnings was regarding as having very little real value, and it was likely to sell in the market for the merest fraction of its realizable resources.  Most of the sellers were not aware that they were disposing of their interest at far less than its scrap value.  Many, however, who might have known the fact would have justified the low price on the ground that the liquidating value was of no practical importance, since the company had no intention of liquidating.  (563)

Graham holds that the wide availability of businesses selling below liquidation values was highly illogical.  If a business is worth more than its liquidation value, then steps should be taken to realize this higher value.  If a business is worth more in liquidation than as a going concern, then it should be liquidated.

Part of the problem is that there is a conflict of interest between the managers and the owners (stockholders).  The managers of a given business typically prefer that the business be continued rather than liquidated, since they want to retain their jobs and benefits.  On the other hand, unless steps can be taken to improve the value of the business as a going concern, the stockholders benefit if the business is liquidated.

As far as the attractiveness of investing in net-nets, Graham observes that the chief danger is that the net asset value will be dissipated.  But this only happens occasionally.  Usually something happens that causes a net-net to be a profitable investment.  Often the normal earnings power of the company is restored, either by general improvement in the industry or by a change in operating policies (as well as, in some cases, new management).  Sometimes a sale or merger occurs because another business is able to utilize the assets more efficiently.  Sometimes the company is liquidated, either partially or fully.

Net-nets are statistically very good investments, but the analyst still must be careful, says Graham:

… the securities analyst should exercise as much discrimination as possible in the choice of issues falling within this category.  He will lean toward those for which he sees a fairly imminent prospect of some one of the favorable developments listed above.  Or else he will be partial to such as reveal other attractive statistical features besides their liquid-asset position, e.g., satisfactory current earnings and dividends or a high average earning power in the past.  The analyst will avoid issues that have been losing their current assets at a rapid rate and show no definite signs of ceasing to do so.  (568-569)



It is a notorious fact… that the typical American stockholder is the most docile and apathetic animal in captivity.  He does what the board of directors tell him to do and rarely thinks of asserting his individual rights as owner of the business and employer of its paid officers.  The result is that the effective control of many, perhaps most, large American corporations is exercised not by those who together own a majority of the stock but by a small group known as ‘the management.’… (575-576)

… Certain elementary facts, once well-nigh forgotten, might well be emphasized here:  Corporations are in law the mere creatures and property of stockholders who own them;  the officers are only paid employees of the stockholders;  the directors, however chosen, are virtually trustees, whose legal duty it is to act solely in behalf of the owners of the business.

To make these general truths more effective in practice, it is necessary that the stock-owning public be educated to a clearer idea of what are the true interests of the stockholders in such matters as dividend policies, expansion policies, the use of corporate cash to repurchase shares, the various methods of compensating management, and the fundamental question of whether the owners’ capital shall remain in the business or be taken out by them in whole or in part.  (590)



In the Introduction to Part VII, David Abrams writes:

… as Graham and Dodd understood, how markets work, how companies are run, and how people – both investors and corporate managers – tend to act in certain situations never change.  (617)

… every successful investor I’ve ever known makes a calculation that compares an asset’s purchase price to its present or future value.  (618)

Abrams describes his own evolution from overconfident and ignorant to humble and knowledgeable:

I realized that, in all likelihood, the guy on the other side was probably smarter than I was.  Embarrassed by my own ignorance, I vowed to wade into new situations with a greater respect for those on the other side of the trade and with more humility about the limits of my own knowledge.  Never again would I be the patsy.  That approach has served me well throughout my career.  (624)

Graham and Dodd knew that market forecasting doesn’t work:

In market analysis there are no margins of safety;  you are either right or wrong, and if you are wrong, you lose money. (703)

Abrams says that the most important point in Security Analysis is the following:

look at the numbers and think for yourself.  All the great investors do, and that’s what makes them great.



Long before behavioral economics was invented, Graham and Dodd (and also Keynes) understood the significant role of human emotions in determining market prices:

Our exposition of the technique of security analysis has included many different examples of overvaluation and undervaluation.  Evidently the processes by which the securities market arrives at its appraisals are frequently illogical and erroneous.  These processes, as we pointed out in our first chapter, are not automatic or mechanical but psychological, for they go on in the minds of people who buy or sell.  The mistakes of the market are thus the mistakes of groups or masses of individuals.  Most of them can be traced to one or more of three basic causes:  exaggeration, oversimplification or neglect.  (669)

How does one find cheap stocks?

Since we have emphasized that analysis will lead to a positive conclusion only in the exceptional case, it follows that many securities must be examined before one is found that has real possibilities for the analyst.  By what practical means does he proceed to make his discoveries?  Mainly by hard and systematic work.  (669)

Graham writes of opportunities in obscure or ignored stocks:

… although overvaluation or undervaluation of leading issues occurs only at certain points in the stock-market cycle, the large field of ‘nonrepresentative’ or ‘secondary’ issues is likely to yield instances of undervaluation at all times.  When market leaders are cheap, some of the less prominent common stocks are likely to be a good deal cheaper.

Graham gives the example of a market leader (Great Atlantic and Pacific Tea Company) that ended up selling below liquidation value:

… Here, then, was a company whose spectacular growth was one of the great romances of American business, a company that was without doubt the largest retail enterprise in America and perhaps in the world, that had an uninterrupted record of earnings and dividends for many years – and yet was selling for less than its net current assets alone.  Thus one of the outstanding businesses of the country was considered by Wall Street in 1938 to be worth less as a going concern than if it were liquidated.  Why?  First, because of chain-store tax threats;  second, because of a recent decline in earnings;  and, third, because the general market was depressed.

We doubt that a better illustration can be found of the real nature of the stock market, which does not aim to evaluate businesses with any exactitude but rather to express its likes and dislikes, its hopes and fears, in the form of daily changing quotations.  There is indeed enough sound sense and selective judgment in the market’s activities to create on most occasions some degree of correspondence between market prices and ascertainable or intrinsic value… But, on enough occasions to keep the analyst busy, the emotions of the stock market carry it in either direction beyond the limits of sound judgment.  (673-674)

As noted earlier by Seth Klarman, litigation, scandal, accounting fraud, or financial distress often create bargains for the intrepid – those who can ignore bad news and remain focused on long-term fundamentals.  Graham writes about these types of dislocations.  Here is Graham on litigation:

The tendency of Wall Street to go to extremes is illustrated… by its tremendous dislike of litigation.  A lawsuit of any significance casts a damper on the securities affected, and the extent of the decline may be out of all proportion to the merits of the case.  (681)

Graham gives several examples where the potential impact of the lawsuit was tiny, and yet the stock had irrationally declined a large amount.



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

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

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

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


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

My e-mail: jb@boolefund.com




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

Index Funds Beat 90-95% of All Investors Over Time

(Image:  Zen Buddha Silence by Marilyn Barbone.)

January 8, 2017

John Bogle wrote The Little Book of Common Sense Investing in 2007 (Wiley).  It’s a terrific book for all investors.  Here are some key points:


INTRODUCTION – Don’t Allow a Winner’s Game to Become a Loser’s Game

Owning a slice of all American businesses is a positive-sum game, especially over the course of many decades.  On the whole, the capitalist system creates enormous wealth for its owners over the very long term due to innovation and growth.

Trying to do better than long-term owners of all American businesses—that is, trying to beat the broad market index over time—is a zero-sum game.  For each investor who does better than the broad market index, another investor must do worse.  Beating the stock market is a zero-sum game, but that is BEFORE the deduction of costs.

AFTER the deduction of costs, beating the market is a loser’s game.  Bogle explains:

So who wins?  You know who wins.  The man in the middle (actually, the men and women in the middle, the brokers, the investment bankers, the money managers, the marketers, the lawyers, the accountants, the operations departments of our financial system) is the only sure winner in the game of investing.  Our financial croupiers always win.  In the casino, the house always wins.  In horse racing, the track always wins.  In the powerball lottery, the state always wins.  Investing is no different.

The vast majority of investors who invest directly in stocks trail the market, net of costs, over the longer term.  Even more so, the vast majority of investors who invest in mutual funds trail the market, net of costs, over the longer term.

The basic arithmetic has a simple bottom line:  The vast majority of all investors would be best off, over the longer term (several decades or more), by simply buying and holding low-cost broad market index funds.

Many top investors, including Warren Buffett and Charlie Munger, have emphatically agreed with John Bogle’s conclusion.


A PARABLE – The Gotrocks Family

Warren Buffett tells the parable of the Gotrocks family in the 2005 Berkshire Hathaway Annual Letter: http://berkshirehathaway.com/letters/2005ltr.pdf

Bogle gives his version of the parable:  At the beginning, a large family called the Gotrocks owns 100 percent of every stock in the United States, thus benefitting from the long-term productivity of all businesses.

Their investment had compounded over the decades, creating enormous wealth, because the Gotrocks family was playing a winner’s game.

But then a few ‘fast-talking Helpers’ arrive, and they convince some ‘smart’ Gotrocks cousins that they can earn a larger share than their relatives by selling some of their shares to other family members and by buying some shares from others in return.

The net result is that the family wealth starts compounding at a slower rate overall because some of the return is now given to the Helpers as trading fees.

The cousins decide that they need more help in order to actually do better than their relatives, so they hire another set of Helpers to pick which stocks to buy and which to sell.  Now the family wealth overall compounds at an even slower rate, since it is reduced by two layers of fees to Helpers.

So the cousins decide to hire even more Helpers, who promise to help the cousins pick the right stock-picking Helpers.  Once again, the family wealth overall now compounds even more slowly.

After some period of time, the family calls a meeting.  They ask:

“How is it that our original 100 percent share of the pie—made up each year of all those dividends and earnings—has dwindled to just 60 percent?”

The wisest family member, an old uncle, notes that the entire reduction in overall returns is exactly equal to the fees and expenses related to all of the Helpers.  Thus, in order for the family to again benefit from 100 percent of the growth of all American businesses, they should simply get rid of all the Helpers.

The family follows the uncle’s advice and soon they are again benefitting 100 percent from the wealth created by all American businesses over time.

Owning a low-cost index fund is exactly the same idea.  Bogle quotes Jack R. Meyer, former president of Harvard Management Company:

Most people think they can find managers who can outperform, but most people are wrong.  I will say that 85 to 90 percent of managers fail to match their benchmarks.  Because managers have fees and incur transaction costs, you know that in the aggregate they are deleting value.

Burton Malkiel:

Active management as a whole cannot achieve gross returns exceeding the market as a whole and therefore they must, on average, underperform the indexes by the amount of these expense and transaction costs disadvantages.


RATIONAL EXUBERANCE – Business Reality Trumps Market Expectations

Warren Buffett:

The most that owners in the aggregate can earn between now and Judgment Day is what their businesses in the aggregate earn.

Bogle adds:

… History, if only we would take the trouble to look at it, reveals the remarkable, if essential, linkage between the cumulative long-term returns earned by business—the annual dividend yield plus the annual rate of earnings growth—and the cumulative returns earned by the U.S. stock market.

From 1900 to 2005, the average annual return on stocks was 9.6 percent, nearly equal to the investment return of 9.5 percent—4.5 percent from dividend yield and 5 percent from earnings growth.

The extra 0.1 percent can be related to a speculative return, or it may reflect “an upward long-term trend in stock valuations.”

The results from 106 years of compounding:

Each dollar initially invested in 1900 at an investment return of 9.5 percent grew by the close of 2005 to $15,062.

The initial amount is thus multiplied over 15,000 times.  Bogle calls this “the ultimate winner’s game.”

Borrowing from John Maynard Keynes, Bogle says there are two components that drive long-term stock market returns:  a fundamental component and a speculative component.  The fundamental driver of long-term stock market returns has averaged 9.5 percent, 4.5 percent from dividends and 5 percent from earnings growth.

The speculative component of long-term stock market returns is determined by changes in P/E multiples.  Some periods see P/E multiples rise, causing the average annual returns to be higher than what the fundamental component alone would dictate.  Other periods see P/E multiples fall, causing the average annual returns to be lower than what the fundamental component alone would dictate.

These speculative swings are due to many factors, including investor confidence, the level of interest rates, and changes in general price levels (high inflation or deflation).  However, the speculative swings cannot be predicted at all.  Bogle says:

After more than 55 years in this business, I have absolutely no idea how to forecast these swings… I’m not alone.  I don’t know anyone who has done so successfully, or even anyone who knows anyone who has done so.  In fact, 70 years of financial research shows that no one has done so.

Great investors such as Warren Buffett and Peter Lynch have made the same point: No one can forecast accurately with any sort of consistency.  By chance alone, there will always be some forecasters in any given period who turn out to be correct.  But it’s impossible to say ahead of time which forecasters those will be.  And when one does find, after the fact, the forecasters who were correct, that says nothing about which forecasters will be correct in the next period.

By the mid 1990’s, P/E multiples had been increasing for at least a decade and were high by historical standards, so one would have thought they would then go lower.  But they kept increasing until they reached a new record in 2000, far higher than ever before.  Similarly, in 2012 to 2013, assuming history was any guide, one would have thought that P/E multiples would decline for awhile, but that hasn’t been true at all.

In fact, if one assumes that most major central banks are going to keep rates very low for decades, then P/E multiples will continue to be much higher on average than has been the case historically.  As Warren Buffett remarked recently, near-zero rates indefinitely would mean P/E multiples of 50 or more.

Moreover, the return on invested capital—on the whole—may be structurally higher, since technology companies have become much more important in the U.S. and global economies.  All else equal, this also implies higher P/E multiples than has been the case historically.

Some of the smartest have been predicting lower U.S. stocks since 2012-2013, but the opposite has happened.  This is just one example out of many throughout history where very smart folks made highly confident financial forecasts that turned out to be completely wrong.

The rational investor simply buys and holds for the longer term, keeping fees and expenses at an absolute minimum, without trying to time the market.  This approach, buying and holding low-cost broad market index funds, will allow any investor to beat roughly 90-95% of all investors over the course of decades.  Writes Bogle:

So how do you cast your lot with business?  Simply by buying a portfolio that owns the shares of every business in the United States and then holding it forever.  It is a simple concept that guarantees you will win the investment game played by most other investors who—as a group—are guaranteed to lose.



After subtracting the costs of financial intermediation—all those management fees, all those brokerage commissions, all those sales loads, all those advertising costs, all those operating costs—the returns of investors as a group must, and will, and do fall short of the market return by an amount precisely equal to the aggregate amount of those costs.

It’s obvious based on humble arithmetic that:

(1) Beating the market BEFORE costs is a zero-sum game;

(2) Beating the market AFTER costs is a loser’s game.

Most individual investors choose mutual funds rather than individual stocks.  Bogle describes the costs:

In equity mutual funds, management fees and operating expenses—combined, called the expense ratio—average about 1.5 percent per year of fund assets.  Then add, say, another 0.5 percent in sales charges, assuming that a 5 percent initial sales charge is spread over a 10-year holding period.  If the shares are held for five years, the cost would be twice that figure—1 percent per year.

But then add a giant additional cost, all the more pernicious by being invisible.  I am referring to the hidden cost of portfolio turnover, estimated at a full 1 percent per year.

Bottom line

… the ‘all-in’ cost of equity fund ownership can come to as much as 3 percent to 3.5 percent per year.

Nonetheless, observes Bogle, most investors continue to ignore this reality, perhaps because “it flies in the face of their deep-seated beliefs, biases, overconfidence, and uncritical acceptance of the way that financial markets have worked, seemingly forever.”

Of course, financial intermediaries on the whole have very strong financial incentives to ignore the fact that low-cost index funds would be best for the vast majority of investors.  Bogles paraphrases Upton Sinclair:

It’s amazing how difficult it is for a man to understand something if he’s paid a small fortune not to understand it.

Moreover, it’s easy for investors to ignore the costs of investing, writes Bogle.  Many costs (including transaction costs, sales charges, and taxes) are largely hidden from view.  Moreover, if it has been a good period for the market—with reasonably high returns—then the ‘all-in’ costs of 2.5 or 3 percent per year can be much less noticeable.  Finally, investors tend to focus on short-term returns (a few years).  But it’s only after several decades that the enormous superiority of a low-cost index fund—versus the average equity mutual fund—is obvious.

Bogle gives an example: Assume, reasonably enough, that the stock market total return will be 8 percent per year over the coming 50 years.  The costs of the average mutual fund can be estimated at 2.5 percent per year.  After the first ten years, an index investment will be $21,600, while the average mutual fund investment will reach $17,100.  Many investors may not really notice it at this point.  But after 50 years, a simple index investment will reach $469,000, while the average mutual fund investment will reach $145,400.

Thus, after 50 years, costs for the mutual fund investor will have taken almost 70 percent of the index returns:

The investor, who put up 100 percent of the capital and assumed 100 percent of the risk, earned only 31 percent of the market return.  The system of financial intermediation, which put up zero percent of the capital and assumed zero percent of the risk, essentially confiscated 70 percent of that return—surely the lion’s share.  What you see here—and please don’t ever forget it!—is that over the long term, the miracle of compounding returns is overwhelmed by the tyranny of compounding costs.


THE GRAND ILLUSION – Surprise!  The Returns Reported by Mutual Funds Aren’t Actually Earned by Mutual Fund Investors

Historically, while mutual funds have trailed the index by roughly 2.5 percent per year on average, the average investor in mutual funds has done much worse (typically 2-3 percent worse, meaning the average investor in mutual funds trailed the market by 4.5 to 5.5 percent per year).

The average investor in mutual funds has repeatedly made two large mistakes: (1) buying after an extended period during which the stock market has done well, and selling after the stock market has done poorly; (2) buying mutual funds that have done well recently, and selling mutual funds that have done poorly recently.  This is just the opposite of what generally would help individual investors.  The late 1990’s are a good example given by Bogle:

As the market soared, investors poured ever larger sums of money into equity funds.  They invested a net total of only $18 billion in 1990 when stocks were cheap, but $420 billion in 1999 and 2000, when stocks were overvalued… What’s more, they also chose overwhelmingly the highest-risk growth funds, to the virtual exclusion of more conservative value-oriented funds.  While only 20 percent of their money went into risky aggressive growth funds in 1990, they poured fully 95 percent into such funds when they peaked during 1999 and early 2000.

Bogle continues:

When the annual returns of these aggressive funds are compounded over the full period, the deterioration is stunning: a cumulative fund return averaging more than 112 percent; a cumulative shareholder return averaging negative 4.5 percent.  That’s a lag of more than 117 percentage points!  This astonishing penalty, then, makes clear the perils of fund selection and timing.  It also illustrates the value of indexing and the necessity of setting a sound course and then sticking to it, come what may.

One great virtue of buying and holding low-cost index funds is that it can neutralize cognitive biases.  Cognitive biases are largely innate, and if left unchecked, they inevitably lead most investors to make the certain mistakes again and again, such as overconfidently extrapolating the recent past.  For more on cognitive biases, see: http://boolefund.com/cognitive-biases/

Another virtue of index fund investing, as mentioned, is that it requires very little effort or time to implement.  The index fund investor need not worry about individual stocks or mutual funds, nor need she waste time trying to predict what the stock market or the economy will do in any given year.

Bogle quotes Charles Schwab, who himself prefers index funds:

It’s fun to play around… it’s human nature to try to select the right horse… (But) for the average person, I’m more of an indexer… The predictability is so high… For 10, 15, 20 years you’ll be in the 85th percentile of performance.  Why would you screw it up?

Finally, for investors with taxable accounts, index fund investors have had roughly a third the taxes as the average mutual fund investor.  This is due to the much lower turnover of index funds.



Bogle looks back (from 2007) to 1970 to see how many mutual funds from that year have done well over time.  In 1970, there were 355 mutual funds.  223 of those were no longer in business by 2007.  In addition to the 223 mutual funds that closed, there was another 60 mutual funds that significantly underperformed the S&P 500 Index by more than 1% per year.  So together, 283 funds—almost 80% of the original 355—were not long-term winners.  And another 48 funds provided returns that essentially matched the S&P 500 Index.

So that leaves only 24 mutual funds—one out of every 14—that did better than the S&P 500 Index from 1970 to 2007.  Writes Bogle:

Let’s face it: those are terrible odds!  What’s more, the margin of superiority of 15 of those 24 funds over the S&P 500 Index was less than 2 percentage points per year, a superiority that may be due as much to luck as skill.

Bogle says that there were nine long-term winners, funds that outpaced the market by 2 percentage points or more of annual return over 35 years.  But Bogle points out that six out of those nine long-term winners achieved their superior records many years ago, often when they were of much smaller size.  Increasing fund size is an inevitable drag on long-term investment performance.  Bogle says that one of these funds peaked in 1982, and two others peaked in 1983.  The other three peaked in 1993.

So there are only three funds out of the original 355—only 8/10 of 1%—that both survived and created an excellent long-term record that is still ongoing (as of 2007).

For the individual investor looking at the next 35 years, what are the odds of picking a mutual fund that will be able to clearly outpace the broad market, net of costs?  If the last 35 years are any guide, the odds may be 1-3% (or even less).

Thus, writes Bogle, a low-cost index fund tends to grow its margin of superiority over time.  After several decades, an investor in low-cost index funds is likely to be better off, net of costs, than roughly 90% of all investors.  After 50 years, an investor in low-cost index funds is likely to be better off, net of costs, than roughly 95% (or more) of all investors.

If one can be virtually guaranteed, by simple arithmetic, to outpace roughly 90-95% (or more) of all investors over time, with very little time or effort required, why wouldn’t one choose this option?  It is, in fact, the best option for the vast majority of investors.



Bogle shows a fascinating chart of the top 10 performing mutual funds from 1997 to 1999.  These top performing mutual funds had the following rankings in 2000 to 2002, out of a total of 851 mutual funds altogether:

  • 841
  • 832
  • 845
  • 791
  • 801
  • 798
  • 790
  • 843
  • 851
  • 793

If you think those rankings are bad, the average investor in these funds, on a cumulative basis, did much worse!  Over the six year period of 1997 to 2002, these funds had a net gain of 13 percent overall.  But investors in these funds, by piling in largely after the funds had gone up, and by selling largely after the funds had gone down, incurred a net loss of 57 percent overall.

The biggest mistake that most investors make is overconfidently extrapolating the recent past, i.e., buying after a fund has done well and selling after a fund has done poorly.  But market timing very rarely works except by luck.  (One approach that can work well is to have a portion in stocks and a portion in fixed-income and cash – with the portions decided ahead of time based on risk tolerance and age – and then rebalance to the target allocations periodically.)



Warren Buffett himself, arguably the best investor ever, has very consistently argued that the vast majority of all investors would be best off just buying and holding a simple, low-cost index fund.

By periodically investing in an index fund, for example, the know-nothing investor can actually out-perform most investment professionals. Paradoxically, when ‘dumb’ money acknowledges its limitations, it ceases to be dumb. — Warren Buffett, 1993 Letter to shareholders.

Link: http://www.berkshirehathaway.com/letters/1993.html

See also the 2014 Berkshire Letter, such as this passage (page 19):

If the investor, instead, fears price volatility, erroneously viewing it as a measure of risk, he may, ironically, end up doing some very risky things.  Recall, if you will, the pundits who six years ago bemoaned falling stock prices and advised investing in ‘safe’ Treasury bills or bank certificates of deposit.  People who heeded this sermon are now earning a pittance on sums they had previously expected would finance a pleasant retirement.  (The S&P 500 was then below 700; now it is about 2,100.)  If not for their fear of meaningless price volatility, these investors could have assured themselves of a good income for life by simply buying a very low-cost index fund whose dividends would trend upward over the years and whose principal would grow as well (with many ups and downs, to be sure).

Investors, of course, can, by their own behavior, make stock ownership highly risky.  And many do.  Active trading, attempts to ‘time’ market movements, inadequate diversification, the payment of high and unnecessary fees to managers and advisors, and the use of borrowed money can destroy the decent returns that a life-long owner of equities would otherwise enjoy.  Indeed, borrowed money has no place in the investor’s tool kit:  Anything can happen anytime in markets.  And no advisor, economist, or TV commentator—and definitely not Charlie nor I—can tell you when chaos will occur.  Market forecasters will fill your ear but will never fill your wallet.

The commission of the investment sins listed above is not limited to ‘the little guy.’  Huge institutional investors, viewed as a group, have long underperformed the unsophisticated index-fund investor who simply sits tight for decades.  A major reason has been fees: Many institutions pay substantial sums to consultants who, in turn, recommend high-fee managers.  And that is a fool’s game.

There are a few investment managers, of course, who are very good—though in the short run, it’s difficult to determine whether a great record is due to luck or talent.  Most advisors, however, are far better at generating high fees than they are at generating high returns.  In truth, their core competence is salesmanship.  Rather than listen to their siren songs, investors—large and small—should instead read Jack Bogle’s The Little Book of Common Sense Investing.

Link: http://berkshirehathaway.com/letters/2014ltr.pdf



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

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

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

The 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 Most Important Thing – Howard Marks

(Image:  Zen Buddha Silence by Marilyn Barbone.)

December 25, 2017

Howard Marks is one of the great value investors.  The Most Important Thing is a book Marks created based on his memos to clients.  Marks noticed that in his meetings with clients, he would often say, “The most important thing is X,” and then a bit later say, “The most important thing is Y,” and so on.  So the book, The Most Important Thing, has many “most important things,” all of which truly are important according to Marks.

Outstanding books are often worth reading at least four or five times.  The Most Important Thing is clearly outstanding, and is filled with investment wisdom.  As a result, this blog post is longer than usual.  It’s worth spending a long time to absorb the wisdom of Howard Marks.



“Where does an investment philosophy come from?  The one thing I’m sure of is that no one arrives on the doorstep of an investment career with his or her philosophy fully formed.  A philosophy has to be the sum of many ideas accumulated over a long period of time from a variety of sources.  One cannot develop an effective philosophy without having been exposed to life’s lessons.  In my life I’ve been quite fortunate in terms of both rich experiences and powerful lessons.” (page xi)

Marks writes:  “Good times teach only bad lessons: that investing is easy, that you know its secrets, and that you needn’t worry about risk.  The most valuable lessons are learned in tough times.”



Marks first points out how variable the investing landscape is: “No rule always works.  The environment isn’t controllable, and circumstances rarely repeat exactly.  Psychology plays a major role in markets, and because it’s highly variable, cause-and-effect relationships aren’t reliable.” (page 1)

The goal for an investor is to do better than the market over time.  Otherwise, the best option for most investors is simply to buy and hold low-cost broad market index funds.  Doing better than the market requires an identifiable edge:

“Since other investors may be smart, well-informed and highly computerized, you must find an edge they don’t have.  You must think of something they haven’t thought of, see things they miss or bring insight they don’t possess.  You have to react differently and behave differently.  In short, being right may be a necessary condition for investment success, but it won’t be sufficient.  You must be more right than others… which by definition means your thinking has to be different.”

Marks gives some examples of second-level thinking:

  • “First-level thinking says, ‘It’s a good company; let’s buy the stock.’ Second-level thinking says, ‘It’s a good company, but everyone thinks it’s a great company, and it’s not.  So the stock’s overrated and overpriced; let’s sell.’
  • First-level thinking says, ‘The outlook calls for low growth and rising inflation. Let’s dump our stocks.’   Second-level thinking says, ‘The outlook stinks, but everyone else is selling in panic.  Buy!’
  • First-level thinking says, ‘I think the company’s earnings will fall; sell.’ Second-level thinking says, ‘I think the company’s earnings will fall less than people expect, and the pleasant surprise will lift the stock; buy.’”

Marks explains that first-level thinking is generally simplistic.  By contrast, second-level thinking requires thinking of the full range of possible future outcomes, along with estimating probabilities for each possible outcome.  Second-level thinking means understanding what the consensus thinks, why one has a different view, and the likelihood that one’s contrarian view is correct.  Marks observes that second-level thinking is far more difficult than first-level thinking, thus few investors truly engage in second-level thinking.  First-level thinkers cannot expect to outperform the market.  “To outperform the average investor, you have to be able to outthink the consensus.  Are you capable of doing so?  What makes you think so?” (page 5)

“The upshot is simple: to achieve superior investment results, you have to hold nonconsensus views regarding value, and they have to be accurate.  That’s not easy.”



Marks holds a view of market efficiency similar to that of Buffett:  The market is usually efficient, but it is far from always efficient.  Marks says that the market reflects the consensus view, but the consensus is not always right:

“In January 2000, Yahoo sold at $237.  In April 2001 it was $11.  Anyone who argues that the market was right both times has his or her head in the clouds; it has to have been wrong on at least one of those occasions.  But that doesn’t mean many investors were able to detect and act on the market’s error.” (page 8)

Marks summarizes his view:

“The bottom line for me is that, although the more efficient markets often misvalue assets, it’s not easy for any one person – working with the same information as everyone else and subject to the same psychological influences – to consistently hold views that are different from the consensus and closer to being correct.  That’s what makes the mainstream markets awfully hard to beat – even if they aren’t always right.” (page 9)

Marks makes an important point about riskier investments:

“Once in a while we experience periods when everything goes well and riskier investments deliver the higher returns they seem to promise.  Those halcyon periods lull people into believing that to get higher returns, all they have to do is make riskier investments.  But they ignore something that is easily forgotten in good times: this can’t be true, because if riskier investments could be counted on to produce higher returns, they wouldn’t be riskier.” (page 10)

Marks notes that inefficient prices imply that for each investor who buys at a cheap price, another investor must sell at that cheap price.  Inefficiency essentially implies that each investment that beats the market implies another investment that trails the market by an equal amount.

Generally it is exceedingly difficult to beat the market.  To highlight this fact, Marks asks a series of questions:

  • Why should a bargain exist despite the presence of thousands of investors who stand ready and willing to bid up the price of anything that is too cheap?
  • If the return appears so generous in proportion to the risk, might you be overlooking some hidden risk?
  • Why would the seller of the asset be willing to part with it at a price from which it will give you an excessive return?
  • Do you really know more about the asset than the seller does?
  • If it’s such a great proposition, why hasn’t someone else snapped it up?

Market inefficiency alone, argues Marks, is not a sufficient condition for outperformance: “All that means is that prices aren’t always fair and mistakes are occurring: some assets are priced too low and some too high.  You still have to be more insightful than others in order to regularly buy more of the former than the latter.  Many of the best bargains at any point in time are found among the things other investors can’t or won’t do.” (page 14)

Marks ends this section by saying that a key turning point in his career was when he concluded that he should focus on relatively inefficient markets.  (Note:  micro-cap stocks is one area that is relatively inefficient, which is why I created the Boole Microcap Fund.)

A few notes about deep value (contrarian value) investing:

In order to buy a stock that is very cheap in relation to its intrinsic value, some other investor must be willing to sell the stock at such an irrationally low price.  Sometimes such sales happen due to forced selling.  The rest of the time, the seller must be making a mistake in order for the value investor to make a market-beating investment.

Many deep value approaches are fully quantitative, however.  (Deep value is also called contrarian value.)  The quantitative deep value investor is not necessarily making an exceedingly detailed judgment on each individual deep value stock – a judgment which would imply that the value investor is correct in this particular case, and that the seller is wrong.  Rather, the quantitative deep value investor forms a portfolio of the statistically cheapest 30 or more stocks.  All of the studies have shown that a basket of quantitatively cheap stocks does better than the market over time, and is less risky (especially during down markets).

One of the best papers on quantitative deep value investing is Lakonishok, Shleifer, and Vishny (1994), “Contrarian Investment, Extrapolation, and Risk.”  Link: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf

A concentrated deep value approach, by contrast, typically involves the effort to select the most promising and the cheapest stocks available.  Warren Buffett and Charlie Munger both followed this approach when they were managing smaller amounts of capital.  They would typically have between 3 and 8 positions making up nearly the entire portfolio.  (Joel Greenblatt also used this approach when he was managing smaller amounts.  Greenblatt produced a ten-year record of 50.0% gross per year using a concentrated value approach focused on special situations.  See Greenblatt’s book, You Can Be a Stock Market Genius.)



Marks begins by saying that “buy low; sell high” is one of the oldest rules in investing.  But since selling will occur in the future, how can one figure out a price today that will be lower than some future price?  What’s needed is an ability to accurately assess the intrinsic value of the asset.  The intrinsic value of a stock can be derived from the price that an informed buyer would pay for the entire company, based on the net asset value or the earnings power of the company.  Writes Marks:

“The quest in value investing is for cheapness.  Value investors typically look at financial metrics such as earnings, cash flow, dividends, hard assets and enterprise value and emphasize buying cheap on these bases.  The primary goal of value investors, then, is to quantify the company’s current value and buy its securities when they can do so cheaply.” (page 19)

Marks also notes that successful value investing requires an estimate of current net asset value, or the current earnings power, that is unrecognized by the consensus.  Successful growth investing, by contrast, requires an estimate of future earnings that is higher than what the consensus currently thinks.  Often the rewards for successful growth investing are higher, but a successful value investing approach is much more repeatable and achievable.

Buying assets below fair value, however, does not mean those assets will outperform right away.  Thus value investing requires having a firmly held view, because quite often after buying, cheap assets will continue to underperform the market.  Marks elaborates:

“If you liked it at 60, you should like it more at 50… and much more at 40 and 30.  But it’s not that easy.  No one’s comfortable with losses, and eventually any human will wonder, ‘Maybe it’s not me who’s right.  Maybe it’s the market.’…” (page 22)

Thus, successful value investing requires not only the consistent ability to identify assets available at cheap prices; it also requires the ability to ignore various signals (many of which are subconscious) flashing the message that one is wrong.  As Marks writes:

“Value investors score their biggest gains when they buy an underpriced asset, average down unfailingly and have their analysis proved out.  Thus, there are two essential ingredients for profit in a declining market: you have to have a view on intrinsic value, and you have to hold that view strongly enough to be able to hang in and buy even as price declines suggest that you’re wrong.  Oh yes, there’s a third: you have to be right.” (page 23)



Many investors make the mistake of thinking that a good company is automatically a good investment, while a bad company is automatically a bad investment.  But what really matters for the value investor is the relationship between price and value:

“For a value investor, price has to be the starting point.  It has been demonstrated time and time again that no asset is so good that it can’t become a bad investment if bought at too high a price.  And there are few assets so bad that they can’t be a good investment when bought cheaply enough.” (page 24)

In the 1960’s, there was a group of stocks called the Nifty Fifty – companies that were viewed as being so good that all one had to do was buy at any price and then hold for the long term.  But it turned out not to be true for many stocks in the basket.  Moreover, the early 1970’s led to huge declines:

“Within a few years, those price/earnings ratios of 80 or 90 had fallen to 8 or 9, meaning investors in America’s best companies had lost 90 percent of their money.  People may have bought into great companies, but they paid the wrong price.” (page 25)

Marks explains the policy at his firm Oaktree: ‘Well bought is half sold.’  “By this we mean we don’t spend a lot of time thinking about what price we’re going to be able to sell a holding for, or to whom, or though what mechanism.  If you’ve bought it cheap, eventually those questions will answer themselves.  If your estimate of intrinsic value is correct, over time an asset’s price should converge with its value.” (page 25-26)

Marks, similar to Warren Buffett and Charlie Munger, holds that psychology plays a central role in value investing:

“Whereas the key to ascertaining value is skilled financial analysis, the key to understanding the price/value relationship – and the outlook for it – lies largely in insight into other investor’s minds.  Investor psychology can cause a security to be priced just about anywhere in the short run, regardless of its fundamentals.” (page 27)

“The safest and most potentially profitable thing is to buy something when no one likes it.  Given time, its popularity, and thus its price, can only go one way: up.”

A successful value investor must build systems or rules for self-protection because all investors – all humans – suffer from psychological biases, which often operate subconsciously.  (See Daniel Kahneman’s Thinking, Fast and Slow, or see Charlie Munger’s Poor Charlie’s Almanack, for excellent discussions of various psychological biases.)

“Of all the possible routes to investment profit, buying cheap is clearly the most reliable.  Even that, however, isn’t sure to work.  You can be wrong about the current value.  Or events can come along that reduce value.  Or deterioration in attitudes or markets can make something sell even further below its value.  Or the convergence of price and intrinsic value can take more time than you have…” (page 30)

“Trying to buy below value isn’t infallible, but it’s the best chance we have.”



As Buffett frequently observes, the future is always uncertain.  Prices far below probable intrinsic value usually only exist when the future is highly uncertain.  When there is not much uncertainty, asset prices will be much higher than otherwise.  So high uncertainty about the future is the friend of the value investor.

On the other hand, in general, assets that promise higher returns always entail higher risk.  If a potentially higher-returning asset was obviously as low risk as a U.S. Treasury, then investors would rush to buy the higher-returning asset, thereby pushing up its price to the point where it would promise returns on par with a U.S. Treasury.

A successful value investor has to determine whether the potential return on an ostensibly cheap asset is worth the risk.  High risk is not necessarily bad as long as it is properly controlled and as long as the potential return is high enough.  But if the risk is too high, then it’s not the type of repeatable bet that can produce long-term success for a value investor.  Repeatedly taking too much risk – by sizing positions too large relative to risk-reward – virtually guarantees long-term failure.

Consider the Kelly criterion.  If the probability of success and the returns from a potential investment can be quantified, then the Kelly criterion tells one exactly how much to bet in order to maximize the long-term compound returns from a long series of such bets.  Betting any other amount than what the Kelly criterion says will inevitably lead to less than the maximum potential returns.  Most importantly, betting more than what the Kelly criterion says guarantees zero or negative long-term returns.  Repeatedly overbetting guarantees long-term failure.  (For more about the Kelly criterion, see:  http://boolefund.com/concentrated-investing/)

This is why Howard Marks, Warren Buffett, Charlie Munger, and other great value investors often point out that minimizing big mistakes is more important for long-term investing success than hitting home runs.  Buffett and Munger apply the same logic to life itself:  avoiding big mistakes is more important than trying to hit home runs.  Buffett:  “You have to do very few things right in life so long as you don’t do too many things wrong.”

Again, Marks points out, while riskier investments promise higher returns, those higher returns are not guaranteed, otherwise riskier investments wouldn’t be riskier!  The probability distribution of potential returns is wider for riskier investments, typically including some large potential losses.  A certain percentage of future outcomes will be zero or negative for riskier investments.

Marks agrees with Buffett and Munger that the best definition of risk is the potential to experience loss.

Of course, as John Templeton, Ray Dalio, and other great investors observe, even the best investors are typically only right two-thirds of the time, while they are wrong one-third of the time.  Thus, following a successful long-term value investing framework where one consistently and carefully pays cheap prices for assets still entails being wrong roughly one-third of the time.  Being wrong often means that the lower probability future negative scenarios do in fact occur a certain percentage of the time.  Back luck does happen a certain percentage of the time.  (Mistakes in analysis or psychology also happen.)

It’s important to bet big when the odds are heavily in one’s favor.  But one should be psychologically prepared to be wrong roughly one-third of the time, whether due to bad luck or to mistakes.  The overall portfolio should be able to withstand at least a 33% error rate.

More Notes on Deep Value

Investors are systematically too pessimistic about companies that have been doing poorly, and systematically too optimistic about companies that have been doing well.  This is why a deep value (contrarian value) approach, if applied systematically, is very likely to produce market-beating returns over a long enough period of time.

Marks explains:

“Dull, ignored, possibly tarnished and beaten-down securities – often bargains exactly because they haven’t been performing well – are often ones value investors favor for high returns…. Much of the time, the greatest risk in these low-luster bargains lies in the possibility of underperforming in heated bull markets.  That’s something the risk-conscious value investor is willing to live with.” (page 38)

Measuring Risk-Adjusted Returns

Marks mentions the Sharpe ratio – or excess return compared to the standard deviation of the return.  While not perfect, the Sharpe ratio is a solid measure of risk-adjusted return for many public market securities.

It’s important to point out again that risk can no more be objectively measured after an investment than it can be objectively measured before the investment.  Marks:

“The point is that even after an investment has been closed out, it’s impossible to tell how much risk it entailed.  Certainly the fact that an investment worked doesn’t mean it wasn’t risky, and vice versa.  With regard to a successful investment, where do you look to learn whether the favorable outcome was inescapable or just one of a hundred possibilities (many of them unpleasant)?  And ditto for a loser: how do we ascertain whether it was a reasonable but ill-fated venture, or just a wild stab that deserved to be punished?

Did the investor do a good job of assessing the risk entailed?  That’s another good questions that’s hard to answer.  Need a model?  Think of the weatherman.  He says there’s a 70 percent chance of rain tomorrow.  It rains; was he right or wrong?  Or it doesn’t rain; was he right or wrong?  It’s impossible to assess the accuracy of probability estimates other than 0 and 100 except over a very large number of trials.”

Marks believes (as do Buffett, Munger, and other top value investors) that there is some merit to the expected value framework whereby one attempts to identify possible future scenarios and the probabilities of their occurrence:

“If we have a sense for the future, we’ll be able to say which outcome is most likely, what other outcomes also have a good chance of occurring, how broad the range of possible outcomes is and thus what the ‘expected result’ is.  The expected result is calculated by weighing each outcome by its probability of occurring; it’s a figure that says a lot – but not everything – about the likely future.” (page 41)

Again, though, having a reasonable estimate of the future probability distribution is not enough.  One must also make sure that one’s portfolio can withstand a run of bad luck; and one must recognize when one has experienced a run of good luck.  Marks quotes his friend Bruce Newberg (with whom he has played cards and dice): “There’s a big difference between probability and outcome.  Probable things fail to happen – and improbable things happen – all the time.”  This is one of the most important lessons to know about investing, asserts Marks.

Marks defines investment performance: “… investment performance is what happens when a set of developments – geopolitical, macro-economic, company-level, technical and psychological – collide with an extant portfolio.  Many futures are possible, to paraphrase Dimson, but only one future occurs.  The future you get may be beneficial to your portfolio or harmful, and that may be attributable to your foresight, prudence or luck.  The performance of your portfolio under the one scenario that unfolds says nothing about how it would have fared under the many ‘alternative histories’ that were possible.”

“A portfolio can be set up to withstand 99 percent of all scenarios but succumb because it’s the remaining 1 percent that materializes.  Based on the outcome, it may seem to have been risky, whereas the investor might have been quite cautious.

Another portfolio may be structured so that it will do very well in half the scenarios and very poorly in the other half.  But if the desired environment materializes and it prospers, onlookers can conclude that it was a low-risk portfolio.

The success of a third portfolio can be entirely contingent on one oddball development, but if it occurs, wild aggression can be mistaken for conservatism and foresight.” (pages 43-44)

“Risk can be judged only by sophisticated, experienced second-level thinkers.”

The past seems very definite: for every evolving set of possible scenarios, only one scenario happened at each point along the way.  But that does not at all mean that the scenarios that actually occurred were the only scenarios that could have occurred.

Furthermore, most people assume that the future will be like the past, especially the more recent past.  As Ray Dalio says, the biggest mistake most investors make is to assume that the recent past will continue into the future.

Marks also reminds us that the “worst-case” assumed by most investors is typically not negative enough.  Marks relates a funny story his father told about a gambler who bet everything on a race with only one horse in it.  How could he lose?  “Halfway around the track, the horse jumped over the fence and ran away.  Invariably things can get worse than people expect.”  Taking more risk usually leads to higher returns, but not always.  “And when risk bearing doesn’t work, it really doesn’t work, and people are reminded what risk’s all about.”



The main source of risk, argues Marks, is high prices.  When stock prices move higher, for instance, most investors feel more optimistic and less concerned about downside risk.  But value investors have the opposite point of view: risk is typically very low when stock prices are very low, while risk tends to increase significantly when stock prices have increased significantly.

Most investors are not value investors:  “So a prime element in risk creation is a belief that risk is low, perhaps even gone altogether.  That belief drives up prices and leads to the embrace of risky actions despite the lowness of prospective returns.” (page 48)

Marks emphasizes that recognizing risk – which comes primarily from high prices – has nothing to do with predicting the future, which cannot be done with any sort of consistency when it comes to the overall stock market or the economy.

Marks also highlights, again, how the psychology of eager buyers – who are unworried about risk – is precisely what creates greater levels of risk as they drive prices higher: “Thus, the market is not a static arena in which investors operate.  It is responsive, shaped by investors’ own behavior.  Their increasing confidence creates more that they should worry about, just as their rising fear and risk aversion combine to widen risk premiums at the same time as they reduce risk.  I call this the ‘perversity of risk.’” (page 55)

In a nutshell:

“When everyone believes something is risky, their unwillingness to buy usually reduces its price to the point where it’s not risky at all.  Broadly negative opinion can make it the least risky thing, since all optimism has been driven out of its price.

And, of course, as demonstrated by the experience of Nifty Fifty investors, when everyone believes something embodies no risk they usually bid it up to the point where it’s enormously risky.  No risk is feared, and thus no reward for risk bearing – no ‘risk premium’ – is demanded or provided.  That can make the thing that’s most esteemed the riskiest.” (page 55-56)

“This paradox exists because most investors think quality, as opposed to price, is the determinant of whether something’s risky.  But high quality assets can be risky, and low quality assets can be safe.  It’s just a matter of the price paid for them…”



“Outstanding investors, in my opinion, are distinguished at least as much for their ability to control risk as they are for generating return.”

Great investors generate high returns with moderate risk, or moderate returns with low risk.  If they generate high returns with “high risk,” but they do so consistently for many years, then perhaps the high risk “either wasn’t really high or was exceptionally well-managed.”  Mark says that great investors such as Buffett or Peter Lynch tend to have very few losing years over a relatively long period of time.

It’s important, notes Marks, to see that risk leads to loss only when lower probability negative scenarios occur:  “… loss is what happens when risk meets adversity.  Risk is the potential for loss if things go wrong.  As long as things go well, loss does not arise.  Risk gives rise to loss only when negative events occur in the environment.”

“We must remember that when the environment is salutary, that is only one of the environments that could have materialized that day (or that year).  (This is Nassim Nicholas Taleb’s idea of alternative histories…)  The fact that the environment wasn’t negative does not mean that it couldn’t have been.  Thus, the fact that the environment wasn’t negative doesn’t mean risk control wasn’t desirable, even though – as things turned out – it wasn’t needed at that time.” (page 58)

The absence of losses does not mean that there was no risk.  Only a skilled investor can look at a portfolio during good times and tell how much risk has been taken.  “Bottom line: risk control is invisible in good times but still essential, since good times can so easily turn into bad times.”

Marks says that an investment manager adds value by generating higher than market returns for a given level of risk.  Achieving the same return as the market, but with less risk, is adding value.  Achieving better than market returns without undue risk is also adding value.

Many value investors, such as Marks and Buffett, somewhat underperform during up markets, but far outperform during down markets.  The net result over a long period of time is market-beating performance with very little incremental risk.  But it does take some time in order to see the value-added.  “Controlling the risk in your portfolio is a very important and worthwhile pursuit.  The fruits, however, come only in the form of losses that don’t happen.  Such what-if calculations are difficult in placid times.” (page 61)

On the other hand, the intelligent acceptance of recognized risk for profit underlies some of the wisest, most profitable investments – even though (or perhaps due to the fact that) most investors dismiss them as dangerous speculations.” (page 61)

Marks’ firm Oaktree invests in high yield bonds.  High yield bonds can be good investments over time if the prices are low enough:

“I’ve said for years that risky assets can make for good investments if they’re cheap enough.  The essential element is knowing when that’s the case.  That’s it: the intelligent bearing of risk for profit, the best test for which is a record of repeated success over a long period of time.” (page 62)

Risk bearing per se is neither wise nor unwise, says Marks.  Investing in the more aggressive niches with risk properly controlled is ideal.  But controlling risk always entails being prepared for bad scenarios.

“Extreme volatility and loss surface only infrequently.  And as time passes without that happening, it appears more and more likely that it’ll never happen – that assumptions regarding risk were too conservative.  Thus, it becomes tempting to relax rules and increase leverage.  And often this is done just before the risk finally rears its head…” (pages 62-63)

Marks quotes Nassim Taleb:

“Reality is far more vicious than Russian roulette.  First, it delivers the fatal bullet rather infrequently, like a revolver that would have hundreds, even thousands of chambers instead of six.  After a few dozen tries, one forgets about the existence of the bullet, under a numbing false sense of security… Second, unlike a well-defined precise game like Russian roulette, where the risks are visible to anyone capable of multiplying and dividing by six, one does not observe the barrel of reality… One is thus capable of unwittingly playing Russian roulette – and calling it by some alternative ‘low risk’ name.” (page 63)

A good example, which Marks does mention, is large financial institutions in 2004-2007.  Virtually no one thought that home prices could decline on a nationwide scale, since they had never done so before.

Of course, it’s also possible to be too conservative.  “You can’t run a business on the  basis of worst-case assumptions.  You wouldn’t be able to do anything.  And anyway, a ‘worst-case assumption’ is really a misnomer; there’s no such thing, short of a total loss.  Now, we know the quants shouldn’t have assumed there couldn’t be a nationwide decline in home prices.  But once you grant that such a decline can happen… what should you prepare for?  Two percent?  Ten?  Fifty?”

Marks continues:

“If every portfolio was required to be able to withstand declines on the scale we’ve witnessed this year [2008], it’s possible no leverage would ever be used.  Is that a reasonable reaction?”

“Even if we realize that unusual, unlikely things can happen, in order to act we make reasoned decisions and knowingly accept that risk when well paid to do so.  Once in a while, a ‘black swan’ will materialize.  But if in the future we always said, ‘We can’t do such-and-such, because the outcome could be worse than we’ve ever seen before,’ we’d be frozen in inaction.” (page 64-65)

“… It’s by bearing risk when we’re well paid to do so – and especially by taking risks toward which others are averse in the extreme – that we strive to add value for our clients.” (page 65)



  • Rule number one: most things will prove to be cyclical.
  • Rule number two: some of the greatest opportunities for gain and loss come when other people forget rule number one.

Marks explains:  “… processes in fields like history and economics involve people, and when people are involved, the results are variable and cyclical.  The main reason for this, I think, is that people are emotional and inconsistent, not steady and clinical.

Objective factors do play a large part in cycles, of course – factors such as quantitative relationships, world events, environmental changes, technological developments and corporate decisions.  But it’s the application of psychology to these things that causes investors to overreact or underreact, and thus determines the amplitude of the cyclical fluctuations.” (page 68)

“Economies will wax and wane as consumers spend more or less, responding emotionally to economic factors or exogenous events, geopolitical or naturally occurring.  Companies will anticipate a rosy future during the up cycle and thus overexpand facilities and inventories; these will become burdensome when the economy turns down.  Providers of capital will be too generous when the economy’s doing well, abetting overexpansion with cheap money, and then they’ll pull the reins too tight when things cease to look as good.”

Investors will overvalue companies when they’re doing well and undervalue them when things get difficult.” (page 71)



Marks holds that there are two risks in investing:  “the risk of losing money and the risk of missing opportunity.” (page 75)

Most investors consistently do the wrong things at the wrong time:  when prices are high, most investors rush to buy; when prices are low, most investors rush to sell.  Thus, the value investor can profit over time by following Warren Buffett’s advice:

“Be fearful when others are greedy.  Be greedy when others are fearful.”


“Stocks are cheapest when everything looks grim.  The depressing outlook keeps them there, and only a few astute and daring bargain hunters are willing to take new positions.”



Marks writes as follows:

“Many people possess the intellect needed to analyze data, but far fewer are able to look more deeply into things and withstand the powerful influence of psychology.  To say this another way, many people will reach similar cognitive conclusions from their analysis, but what they do with those conclusions varies all over the lot because psychology influences them differently.  The biggest investing errors come not from factors that are informational or analytical, but from those that are psychological.  Investor psychology includes many separate elements, which we will look at in this chapter, but the key thing to remember is that they consistently lead to incorrect decisions.  Much of this falls under the heading of ‘human nature.’” (pages 80-81)

Cognitive Biases

As humans, we all have psychological tendencies or cognitive biases that were mostly helpful to us during much of our evolutionary history, but that often lead us to make bad judgments in many areas of modern life.  I’ve written about many of the chief cognitive biases here:  http://boolefund.com/why-simple-quant-models-beat-experts-in-a-wide-variety-of-areas/

Marks writes about the following psychological tendencies:

  • Greed
  • Fear
  • Self-deception
  • Conformity to the crowd
  • Envy
  • Ego or overconfidence
  • Capitulation

How might these psychological tendencies have been useful in our evolutionary history?  

When food was often scarce, being greedy by hoarding food (whether at the individual or community level) made sense.  When a movement in the grass occasionally meant the presence of a dangerous predator, immediate fear (this fear is triggered by the amygdala even before the conscious mind is aware of it) was essential for survival.  When hunting for food was dangerous, often with low odds of success, self-deception – accompanied by various naturally occurring chemicals – helped hunters to persevere over long periods of time, regardless of high danger and often regardless of injury.  (Chemical reactions could often cause an injured hunter not to feel the pain much.)  If everyone in one’s hunting group, or in one’s community, was running away as fast as possible, following the crowd was usually the most rational response.  If a starving hunter saw another person with a huge pile of food, envy would trigger a strong desire to possess such a large pile of food, whether by trying to take it or by going on a hunting expedition with a heightened level of determination.  When hunting a dangerous prey, with low odds of success, ego or overconfidence would cause the hunter to be convinced that he would succeed.  From the point of view of the community, having self-deceiving and overconfident hunters was a net benefit because the hunters would persevere despite often low odds of success, and despite inevitable injuries and deaths among individual hunters.

How do these psychological tendencies cause people to make errors in modern activities such as investing?

Greed causes people to follow the crowd by paying high prices for stocks in the hope that there will be even higher prices in the future.  Fear causes people to sell or to avoid ugly stocks – stocks trading at low multiples because the businesses in question are facing major difficulties.

As humans, we have an amazingly strong tendency towards self-deception:

  • The first principle is that you must not fool yourself, and you are the easiest person to fool. – Richard Feynman
  • Nothing is easier than self-deceit. For what each man wishes, that he also believes to be true. – Demosthenes, as quoted by Charlie Munger

There have been many times in history when self-deception was probably crucial for the survival of a given individual or community.  I’ve mentioned hunters pursuing dangerous prey.  A much more recent example might be Winston Churchill, who was firmly convinced – even when virtually all the evidence was against it – that England would defeat Germany in World War II.  Churchill’s absolute belief helped sustain England long enough for both good luck and aid to arrive:  the Germans ended up overextended in Russia, and huge numbers of American troops (along with mass amounts of equipment) arrived in England.

Like other psychological tendencies, self-deception not only was important in evolutionary history, but it still often plays a constructive role.  Yet when it comes to investing, self-deception is clearly harmful, especially as the time horizon is extended so that luck evens out.

Conformity to the crowd is another psychological tendency that many (if not most) investors seem to display.  Marks notes the famous experiment by Solomon Asch.  The subject is shown lines of obviously different lengths.  But in the same room with the subject are shills, who unbeknownst to the subject have already been instructed to say that two lines of obviously different lengths actually have the same length.  So the subject of the experiment has to decide between the obvious evidence of his eyes – the two lines are clearly different lengths – and the opinion of the crowd.  A significant number (36.8 percent) ignored their own eyes and went with the crowd, saying that the two lines had equal length, despite the obvious fact that they didn’t.

(The experiment involved a control group in which there were no shills.  Almost every subject – over 99 percent – gave the correct answer under these circumstances.)

Greed, conformity, and envy together operate powerfully on the brain of many investors:

“Time and time again, the combination of pressure to conform and the desire to get rich causes people to drop their independence and skepticism, overcome their innate risk aversion and believe things that don’t make sense.” (page 84)

A good example from history is the tulip mania in Holland, during which otherwise rational people ended up paying exorbitant sums for colorful tulip bulbs.  The South Sea Bubble is another example, during which even the extremely intelligent Isaac Newton, after selling out early for a solid profit, could not resist buying in again as prices seemed headed for the stratosphere.  Newton and many others lost huge sums when prices inevitably returned to earth.

Envy has a very powerful and often negative effect on most human brains.  And as Charlie Munger always points out, envy is particularly stupid because it’s a sin that, unlike many other sins, is not any fun at all.  There are many people who could easily learn to be very happy – grateful for blessings, grateful for the wonders of life itself, etc. – who become miserable because they fixate on other people who have more of something, or who are doing better in some way.  Envy is fundamentally irrational and stupid, but it is powerful enough to consume many people.  Buffett: “It’s not greed that drives the world, but envy.”  Envy and jealousy have for a very long time caused the downfall of human beings.  This certainly holds true in investing.

Ego is another powerful psychological tendency humans have.  As with the other potential pitfalls, many of the best investors – from Warren Buffett to Ray Dalio – are fundamentally humble.  Overconfidence (closely related to ego) is a very strong bias that humans have, and if it is not overcome by learning humility and objectivity, it will kill any investor eventually.  Marks writes:

“In contrast, thoughtful investors can toil in obscurity, achieving solid gains in the good years and losing less than others in the bad years.  They avoid sharing in the riskiest behavior because they’re so aware of how much they don’t know and because they have their egos in check.  This, in my opinion, is the greatest formula for long-term wealth creation – but it doesn’t provide much ego gratification in the short run.  It’s just not that glamorous to follow a path that emphasizes humility, prudence, and risk control.  Of course, investing shouldn’t be about glamour, but often it is.” (page 85)

Capitulation is a final phenomenon that Marks emphasizes.  In general, people become overly negative about a stock that is deeply out of favor because the business in question is going through hard times.  Moreover, when overly negative investors are filled with fear and when they see everyone selling in a panic, they themselves often sell near the very bottom.  Often these investors know analytically that the stock is cheap, but their emotions (fear of loss, conformity to the crowd, etc.) are too strong, so they disbelieve their own sound logic.  The rational, contrarian, long-term value investor does just the opposite:  he or she buys near the point of maximum pessimism (to use John Templeton’s phrase).

Similarly, most investors become overly optimistic when a stock is near its all-time highs.  They see many other investors who have done well with the sky-high stock, and so they tend to buy at a price that is near the all-time highs.  Again, many of these investors – like Isaac Newton – know analytically that buying a stock when it is near its all-time highs is often not a good idea.  But greed, envy, self-deception, crowd conformity, etc. (fear of missing out, dream of a sure thing), overwhelm their own sound logic.  By contrast, the rational, long-term value investor does the opposite:  he or she sells near the point of maximum optimism.

Marks gives a marvelous example from the tech bubble of 1998-2000:

“From the perspective of psychology, what was happening with IPOs is particularly fascinating.  It went something like this: The guy next to you in the office tells you about an IPO he’s buying.  You ask what the company does.  He says he doesn’t know, but his broker told him its going to double on the day of issue.  So you say that’s ridiculous.  A week later he tells you it didn’t double… it tripled.  And he still doesn’t know what it does.  After a few more of these, it gets hard to resist.  You know it doesn’t make sense, but you want protection against continuing to feel like an idiot.  So, in a prime example of capitulation, you put in for a few hundred shares of the next IPO… and the bonfire grows still higher on the buying from new converts like you.” (page 87)



To buy when others are despondently selling and to sell when others are euphorically buying takes the greatest courage, but provides the greatest profit.” – Sir John Templeton

Superior value investors buy when others are selling, and sell when others are buying.  Value investing is simple in concept, but it is very difficult in practice.

Of course, it’s not enough just to be contrarian.  Your facts and your reasoning also have to be right:

You’re neither right nor wrong because the crowd disagrees with you.  You’re right because your data and reasoning are right – and that’s the only thing that makes you right.  And if your facts and reasoning are right, you don’t have to worry about anybody else.” – Warren Buffett

Or, as Seth Klarman puts it:  “Value investing is at its core the marriage of a contrarian streak with a calculator.”

Only by being right about the facts and the reasoning can a long-term value investor hold (or add to) a position when everyone else continues to sell.  Getting the facts and reasoning right still involves being wrong roughly one-third of the time, often due to bad luck but also sometimes due to mistakes in analysis or psychology.  But getting the facts and reasoning right leads to ‘being right’ roughly two-third of the time.

‘Being right’ usually means a robust process correctly followed – both analytically and psychologically – and the absence of bad luck.  But sometimes good luck plays a role.  Either way, a robust process correctly followed should produce positive results (on both an absolute and relative basis) over most rolling five-year periods, and over nearly all rolling ten-year periods.

It’s never easy to consistently follow a careful, contrarian value investing approach.  Marks quotes David Swensen:

“Investment success requires sticking with positions made uncomfortable by their variance with popular opinion… Only with the confidence created by a strong decision-making process can investors sell speculative excess and buy despair-driven value.

… Establishing and maintaining an unconventional investment profile requires acceptance of uncomfortably idiosyncratic portfolios, which frequently appear downright imprudent in the eyes of conventional wisdom.”

Marks puts it in his own words:

“The most profitable investment actions are by definition contrarian:  you’re buying when everyone else is selling (and the price is thus low) or you’re selling when everyone else is buying (and the price is high).  These actions are lonely and… uncomfortable.”

Marks writes about the paradoxical nature of investing:

The thing I find most interesting about investing is how paradoxical it is: how often the things that seem most obvious – on which everyone agrees – turn out not to be true.” (page 95)

The best bargains are typically only available when pessimism and uncertainty are high.  Many investors say, ‘We’re not going to try to catch a falling knife; it’s too dangerous… We’re going to wait until the dust settles and the uncertainty is resolved.’  But waiting until uncertainty gets resolved usually means missing the best bargains, as Marks says:

The one thing I’m sure of is that by the time the knife has stopped falling, the dust has settled and the uncertainty has been resolved, there’ll be no great bargains left.  When buying something has become comfortable again, its price will no longer be so low that it’s a great bargain.  Thus, a hugely profitable investment that doesn’t begin with discomfort is usually an oxymoron.

It’s our job as contrarians to catch falling knives, hopefully with care and skill.  That’s why the concept of intrinsic value is so important.  If we hold a view of value that enables us to buy when everyone else is selling – and if our view turns out to be right – that’s the route to the greatest rewards earned with the least risk.” (page 99)



It cannot be too often repeated:

“A high-quality asset can constitute a good or bad buy, and a low-quality asset can constitute a good or bad buy.  The tendency to mistake objective merit for investment opportunity, and the failure to distinguish between good assets and good buys, gets most investors into trouble.” (page 102)

What is the process by which some assets become cheap relative to intrinsic value?  Marks explains:

  • Unlike assets that become the subject of manias, potential bargains usually display some objective defect. An asset class may have weaknesses, a company may be a laggard in its industry, a balance sheet may be over-levered, or a security may afford its holders inadequate structural protection.
  • Since the efficient-market process of setting fair prices requires the involvement of people who are analytical and objective, bargains usually are based on irrationality or incomplete understanding. Thus, bargains are often created when investors either fail to consider an asset fairly, or fail to look beneath the surface to understand it thoroughly, or fail to overcome some non-value-based tradition, bias or stricture.
  • Unlike market darlings, the orphan asset is ignored or scorned. To the extent it’s mentioned at all by the media and at cocktail parties, it’s in unflattering terms.
  • Usually its price has been falling, making the first-level thinker as, ‘Who would want to own that?’ (It bears repeating that most investors extrapolate past performance, expecting the continuation of trends rather than the far-more-dependable regression to the mean.  First-level thinkers tend to view price weakness as worrisome, not as a sign that the asset has gotten cheaper.)
  • As a result, a bargain asset tends to be one that’s highly unpopular. Capital stays away from it or flees, and no one can think of a reason to own it.


Where is the best place to look for underpriced assets?  Marks observes that a good place to start is among things that are:

  • little known and not fully understood;
  • fundamentally questionable on the surface;
  • controversial, unseemly or scary;
  • deemed inappropriate for ‘respectable’ portfolios;
  • unappreciated, unpopular and unloved;
  • trailing a record of poor returns; and
  • recently the subject of disinvestment, not accumulation.

To boil it all down to just one sentence, I’d say the necessary condition for the existence of bargains is that perception has to be considerably worse than reality.  That means the best opportunities are usually found among things most others won’t do.  After all, if everyone feels good about something and is glad to join in, it won’t be bargain-priced.”  (page 105)

Marks started a fund for high yield bonds – junk bonds – in 1978.  One rating agency described high yield bonds as “generally lacking the characteristics of a desirable investment.”  Marks points out the obvious: “if nobody owns something, demand for it (and thus the price) can only go up and…. by going from taboo to even just tolerated, it can perform quite well.”

In 1987, Marks formed a fund to invest in distressed debt:

Who would invest in companies that already had demonstrated their lack of financial viability and the weakness of their management?  How could anyone invest responsibly in companies in free fall?  Of course, given the way investors behave, whatever asset is considered worst at a given point in time has a good likelihood of being the cheapest.  Investment bargains needn’t have anything to do with high quality.  In fact, things tend to be cheaper if low quality has scared people away.” (page 106)



Marks makes the same point that Warren Buffett and Charlie Munger often make: Most of the time, by far the best thing to do is absolutely nothing.  Finding one good idea a year is enough to get outstanding returns over time.  Writes Marks:

So here’s a tip: You’ll do better if you wait for investments to come to you rather than go chasing after them.  You tend to get better buys if you select from the list of things sellers are motivated to sell rather than start with a fixed notion as to what you want to own.  An opportunist buys things because they’re offered at bargain prices.  There’s nothing special about buying when prices aren’t low.” (page 107)

Marks took five courses in Japanese studies as an undergraduate business major in order to fulfill his requirement for a minor.  He learned the Japanese value of mujo:

“… mujo means cycles will rise and fall, things will come and go, and our environment will change in ways beyond our control.  Thus we must recognize, accept, cope and respond.  Isn’t that the essence of investing?

… What’s past is past and can’t be undone.  It has led to the circumstances we now face.  All we can do is recognize our circumstances for what they are and make the best decisions we can, given the givens.”

Marks quotes Buffett, who notes that there are no called strikes in investing:

“Investing is the greatest business in the world because you never have to swing.  You stand at the plate; the pitcher throws you General Motors at 47!  U.S. steel at 39!  And nobody calls a strike on you.  There’s no penalty except opportunity.  All day you wait for the pitch you like; then, when the fielders are asleep, you step up and hit it.”

It’s dumb to invest when the opportunities are not there.  But when the overall market is high, there are still a few ways to do well as a long-term value investor.  If one is able to ignore short-term volatility and focus on the next five to ten years, then one can invest in undervalued stocks.

If one’s assets under management are small enough, then there can be certain parts of the market where one can still find excellent bargains.  An example would be micro-cap stocks, since very few professional investors look there.  (This is the focus of the Boole Microcap Fund.)

Another example of potentially cheap (albeit volatile) stocks in an otherwise expensive stock market might be offshore oil drillers.  Oil early this year approached $26 per barrel, and many offshore drillers were selling at 5-10% of tangible book value.  These days, oil fields deplete faster than ever, due to improvements in technology, while demand continues to increase virtually every year.  Demand for offshore oil drilling is likely to be crucial in finding and developing new sources of oil.  Assuming that over the next five to ten years, the market clearing price of oil is approximately $60-70, some drillers were/are likely very cheap.  (Also, seeing oil back in the $80-100 range in the next five to ten years wouldn’t be surprising, and may even be likely.)

Of course, there could be a bear market and/or recession this year, next year, or in 2018 – no one knows when exactly.  Thus, one must be focused on the next five to ten years in order to invest in offshore oil drillers, because as cheap as they were/are, they could still get cut in half or worse during a bear market and/or recession.  Last point:  Before the next ten years are up, electric vehicles are likely to start making a difference in the demand for oil.  At some point in the future (7 years? 10? 20?), electric and hybrid vehicles will start causing the overall demand for oil to be much lower than otherwise.  Eventually other green technologies will also start to have a noticeable impact.  Thus, offshore oil drillers may be quite cheap from a five-year point of view, but eventually the world economy will reach a plateau in the demand for oil.  (Even then, oil demand is likely to remain near the plateau for decades unless there is a huge technological breakthrough, which is always possible in fields such as artificial intelligence or nuclear energy.)

(Daniel Yergin of CERA has predicted an undulating plateau – with global daily demand reaching 115 million barrels, up from today’s 94 million.  Yergin has predicted that the undulating plateau will begin around 2030 and last about twenty years.  Other experts have predicted peak production as beginning much earlier.)



“We have two classes of forecasters: Those who don’t know – and those who don’t know they don’t know.” – John Kenneth Galbraith

Marks, like Buffett, Munger, and most other top value investors, thinks that financial forecasting simply cannot be done with any sort of consistency.  But Marks has two caveats:

  • The more we concentrate on smaller-picture things, the more it’s possible to gain a knowledge advantage. With hard work and skill, we can consistently know more than the next person about individual companies and securities, but that’s much less likely with regard to markets and economies.  Thus, I suggest people try to ‘know the knowable.’
  • An exception comes in the form of my suggestion, on which I elaborate in the next chapter, that investors should make an effort to figure out where they stand at a moment in time in terms of cycles and pendulums. That won’t render the future twists and turns knowable, but it can help one prepare for likely developments.


Marks has tracked (in a limited way) many macro predictions, including U.S. interest rates, the U.S. stock market, and the yen/dollar exchange rate.  He found quite clearly that most forecasts were not correct.

I can elaborate on two examples that I spent much time on (when I should have stayed focused on finding individual companies available at cheap prices):

  • the U.S. stock market
  • the yen/dollar exchange

A secular bear market for U.S. stocks began (arguably) in the year 2000, when the 10-year Graham-Shiller P/E – also called the CAPE (cyclically adjusted P/E) – was over 30, its highest level in U.S. history.  The long-term average CAPE is around 16.  Based on over one hundred years of history, the pattern for U.S. stocks in a secular bear market would be relatively flat or lower until the CAPE approached 10.  However, ever since Greenspan started running the Fed in the 1980’s, the Fed has usually had a policy of stimulating the economy and stocks by lowering rates or keeping rates as low as possible.  This has caused U.S. stocks to be much higher than otherwise.  For instance, with rates today staying near zero, U.S. stocks could easily be twice as high as or three times as high as “normal” indefinitely, assuming the Fed decides to keep rates low for many more years.  As Buffett has noted, near-zero rates for many decades would eventually mean price/earnings ratios on stocks of 100.

In any case, in the year 2012 to 2013, some of the smartest market historians (including Russell Napier, author of Anatomy of the Bear) started predicting that the S&P 500 Index would fall towards a CAPE of 10 or lower, which is how every previous U.S. secular bear market concluded.  It didn’t happen in 2012, or in 2013, or in 2014, or in 2015.  Moreover, it may not happen in 2016 or even 2017.  Eventually the U.S. stock market will experience another major bear market.  But by the time that happens, it may start from a level over 2300 in the next year or two, and it may not decline below 1500, which is actually above the level from which the smartest forecasters (such as Russell Napier) said the decline would begin.

Note: Jeremy Grantham, who is as long-term bearish as anyone, has been predicting 2250 or 2300 on the S&P 500 Index for the past couple of years – see the GMO Q1 2016 Letter, as well as several previous letters here: https://www.gmo.com/

Robert Shiller, the Nobel Prize-winning economist who perfected the CAPE (Shiller P/E), said in 1996 that U.S. stocks were high.  But if an investor had gone to cash in 1996, they wouldn’t have had any chance of being ahead of the stock market until 2008 to 2009, more than 10 years later during the biggest financial crisis since the Great Depression.

Shiller has recently explained the CAPE with more clarity: http://www.businessinsider.com/robert-shiller-on-stocks-2013-1

When the CAPE is high, as it is today, the long-term investor should still have a large position in U.S. stocks.  But the long-term investor should expect fairly low ten-year returns, a few percent per annum, and they also should some investments outside of U.S. stocks.  Shiller also has observed that certain sectors in the U.S. economy can be cheap (low CAPE).  Many oil-related stocks, for example, are very probably quite cheap today (mid 2016) relative to their long-term normalized earnings power.

The main point here, though, is that forecasting the next bear market or the next recession with any precision is generally impossible.  Another example would be the Economic Cycle Research Institute (https://www.businesscycle.com/), which predicted a U.S. recession around 2011-2012 based on its previously quite successful set of leading economic indicators.  But they were wrong, and they later admitted that the Fed printing so much money not only may have kept the U.S. barely out of recession, but also may have led to distortions in the economic data, making ECRI’s set of leading economic indicators no longer as reliable.

As for the yen/dollar exchange, the story begins in a familiar way:  some of the smartest macro folks around predicted (in 2010 and later) that shorting the yen vs. the U.S. dollar would be the “trade of the decade,” and that the yen/dollar exchange would exceed 200.  In 2007, the yen/dollar was over 120.  By 2011-2012, the yen/dollar had gone to around 76.  From there until December 2014 and much of 2015, the yen/dollar again exceeded 120.  However, recently the BOJ decided not to continue trying to weaken their currency by printing huge amounts of money.  This decision has led the yen/dollar to decline from over 120 in late January 2016 to about 106 recently.

The “trade of the decade argument” was the following:  the debt-to-GDP in Japan has reached stratospheric levels (over 400-500%, including over 250% for government debt-to-GDP), government deficits have continued to widen, and the Japanese population is actually shrinking.  Since long-term GDP growth is a function of population growth plus innovation, it should become mathematically impossible for the Japanese government to pay back its debt without a significant devaluation of their currency.  If the BOJ could devalue the yen by 67% – which would imply a yen/dollar exchange rate of well over 200 – then Japan could repay the government debt in seriously devalued currency.  In this scenario – a yen devaluation of 67% – Japan effectively would only have to repay 33% of the government debt.  Currency devaluation – inflating away the debts – is what most major economies throughout history have done.

The bottom line as regards the yen is the following:  Either Japan must devalue the Yen by 67% – implying a yen/dollar exchange rate of well over 200 – or Japan will inevitably reach the point where it is quite simply impossible for it to repay a large portion of the government debt.  That’s the argument.  There could be other solutions, however.  The human economy is likely to be much larger in the future, and there may be some way to help the Japanese government with its debts.  After all, the situation wouldn’t seem so insurmountable if Japan could grow its population.  But this might happen in some indirect way if the human economy becomes more open in the future, perhaps involving the creation of a new universal currency.

In any case, for the past five to ten years, and even longer, it has been argued that either the yen/dollar would eventually exceed 200 (thus inflating away as much as 67% of the debt), or the Japanese government would inevitably default on JGB’s (Japanese government bonds).  In either case, the yen should collapse relative to the U.S. dollar, meaning a yen/dollar of well over 200.  This has been described as “the trade of the decade,” but it may not happen this decade or even next decade.

In the end, one could have spent decades trying to short the Yen or trying to short JGB’s, without much to show for it.  Or one could have spent those decades doing value investing:  finding and buying cheap stocks, year in and year out.  Decades later, value investing would almost certainly have produced a far better result, and with a relatively low level of risk.

The same logic applies to market timing, or trying to profit on the basis of predicting bull markets, bear markets, recessions, etc.  For the huge majority of investors, they would get much better profits, at relatively low risk, by following a value investing approach (whether by investing in a value fund, or by applying the value approach directly to stocks).

In Sum

In sum, financial forecasting cannot be done with any sort of consistency.  Every year, there are many people making financial forecasts, and so purely as a matter of chance, a few will be correct in a given year.  But the ones correct this year are almost never the ones correct the next time around, because what they’re trying to predict can’t be predicted with any consistency.  Marks writes thus:

“I am not going to try to prove my contention that the future is unknowable.  You can’t prove a negative, and that certainly includes this one.  However, I have yet to meet anyone who consistently knows what lies ahead macro-wise…”

“One way to get to be right sometimes is to always be bullish or always be bearish; if you hold a fixed view long enough, you may be right sooner or later.  And if you’re always an outlier, you’re likely to eventually be applauded for an extremely unconventional forecast that correctly foresaw what no one else did.  But that doesn’t mean your forecasts are regularly of any value…

It’s possible to be right about the macro-future once in a while, but not on a regular basis.  It doesn’t do any good to possess a survey of sixty-four forecasts that includes a few that are accurate; you have to know which ones they are.  And if the accurate forecasts each six months are made by different economists, it’s hard to believe there’s much value in the collective forecasts.”

Marks gives one more example: How many predicted the crisis of 2007-2008?  Of those who did predict it – there was bound to be some from pure chance alone – how many of those then predicted the recovery starting in 2009 and continuing until today (mid-2016)?  The answer is “very few.”  The reason, observes Marks, is that those who got 2007-2008 right “did so at least in part because of a tendency toward negative views.”  They probably were negative well before 2007-2008, and more importantly, they probably stayed negative afterwards, during which the U.S. stock market increased (from the low) more than 200% as the U.S. economy expanded from 2009 to today (mid-2016).  And the U.S. economy could expand until 2018, while the S&P 500 Index could exceed 2300 or 2400!  If that happened before the next U.S. bear market, many of the smartest market historians and analysts would have been at least 5-6 years early, not to mention missing a doubling of the U.S. stock market (from roughly 1200 to roughly 2400).


Marks has a description for investors who believe in the value of forecasts.  They belong to the “I know” school, and it’s easy to identify them:

  • They think knowledge of the future direction of economies, interest rates, markets and widely followed mainstream stocks is essential for investment success.
  • They’re confident it can be achieved.
  • They know they can do it.
  • They’re aware that lots of other people are trying to do it too, but they figure either (a) everyone can be successful at the same time, or (b) only a few can be, but they’re among them.
  • They’re comfortable investing based on their opinions regarding the future.
  • They’re also glad to share their views with others, even though correct forecasts should be of such great value that no one would give them away gratis.
  • They rarely look back to rigorously assess their record as forecasters. (page 121)

Marks contrasts the confident “I know” folks with the guarded “I don’t know” folks.  The latter believe you can’t predict the macro-future, and thus the proper goal for investing is to do the best possible job analyzing individual securities.  If you belong to the “I don’t know” school, eventually everyone will stop asking you where you think the market’s going.  “You’ll never get to enjoy that one-in-a-thousand moment when your forecast comes true and the Wall Street Journal runs your picture.  On the other hand, you’ll be spared all those times when forecasts miss the mark, as well as the losses that can result from investing based on overrated knowledge of the future.”

Marks continues by noting that no one likes investing on the assumption that the future is unknowable.  But if the future IS largely unknowable, then it’s far better as an investor to acknowledge that fact than to pretend otherwise.

Furthermore, says Marks, the biggest problems for investors tend to happen when investors forget the difference between probability and outcome (i.e., the limits of foreknowledge):

  • when they believe the shape of the probability distribution is knowable with certainty (and that they know it),
  • when they assume the most likely outcome is the one that will happen,
  • when they assume the expected result accurately represents the actual result, or
  • perhaps most important, when they ignore the possibility of improbable outcomes.

Marks sums it up:

“Overestimating what you’re capable of knowing or doing can be extremely dangerous – in brain surgery, transocean racing or investing.  Acknowledging the boundaries of what you can know – and working within those limits rather than venturing beyond – can give you a great advantage.” (page 123)



Marks believes that market cycles – inevitable ups and downs – cannot be predicted as to extent and (especially) as to timing, but have a profound influence on us as investors.  The only thing we can predict is that market cycles are inevitable.

Marks holds that as investors, we can have a rough idea of market cycles.  We can’t predict what will happen exactly or when.  But we can at least develop valuable insight into various future events.

“So look around, and ask yourself: Are investors optimistic or pessimistic?  Do the media talking heads say the markets should be piled into or avoided?  Are novel investment schemes readily accepted or dismissed out of hand?  Are securities offerings and fund openings being treated as opportunities to get rich or possible pitfalls?  Has the credit cycle rendered capital readily available or impossible to obtain?  Are price/earnings ratios high or low in the context of history, and are yield spreads tight or generous?  All of these things are important, and yet none of them entails forecasting.  We can make excellent investment decisions on the basis of present observations, with no need to make guesses about the future.” (page 126)

Marks likens the process of assessing the current cycle with “taking the temperature” of the market.  Again, one can never precisely time market turning points, but one can at least become aware of when markets are becoming overheated, or when they’ve become unusually cheap.

It may be more difficult today to take the market’s temperature because of the policy of near-zero (or negative) interest rates in many of the world’s major economies.  This obviously distorts all asset prices.  As Buffett remarked recently, if U.S. rates were going to stay near zero for many decades into the future, U.S. stocks would eventually be much higher than they are today.  Zero rates indefinitely would easily mean price/earnings ratios of 100 (or even 200).

Stanley Druckenmiller, one of the most successful macro investors, has consistently said that the stock market is driven in large part not by earnings, but by central bank liquidity.

In any case, timing the next major bear market is virtually impossible, as acknowledged by the majority of great investors such as Howard Marks, Warren Buffett, Charlie Munger, Seth Klarman, Bill Ackman, and others.  The next major bear market could start this year (2016), next year (2017), or even in 2018.

And remember that many of the smartest already predicted that it would happen in 2012.  Then they predicted 2013, then 2014, then 2015.  This highlights the main point Marks, Buffett, and Munger have made:  If some people predict a major bear market every year, eventually they will be right.  But that’s not particularly helpful for investors.

What Marks, Buffett, and Munger stress is to focus on finding cheap stocks.  Pay cheap enough prices so that, on average, one can make a profit over the next five years or ten years.  At some point – no one knows precisely when – the U.S. stock market is likely to drop roughly 30-50%.  One must be psychologically prepared for this.  And one’s portfolio must also be prepared for this.

If one is able to buy enough cheap stocks, while maintaining a focus on the next five years or ten years, and if one is psychologically prepared for a big drop at some point, which always happens periodically, then one will be in good position.

Note:  Gold stocks and gold have often been negatively correlated with the U.S. stock market.  Thus, David Einhorn and Stan Druckenmiller may be right to have fairly large exposures to gold.

Many oil-related stocks recently dropped by as much as 90 percent or more, largely because the oil price dropped to $26.  With the oil price beginning to recover significantly (albeit it not in a straight line) on its way back to a market clearing price of $60-70 in the next few years or so, some oil-related stocks appear to offer good five-year returns.

Other stocks that are probably cheap compared to net asset value or earnings power could also round out a portfolio prepared for a major (or minor) bear market.

Note:  Cheap stocks (whether oil-related or otherwise) typically have lower correlation than usual with the broader stock market.  Even if the broader market declines, some cheap stocks may do much better on both a relative and absolute basis.

Finally, some percentage in cash is an excellent position to have in the event of a major (or minor) bear market.  The tricky part, again, is what percentage to have in cash and when.  Many excellent value investors have had 50% or more in cash since 2012 or 2013.  Even if the bear market happens this year (2016), the performance-drag of a large cash position initiated in 2012 may prove challenging.  And if the bear market does not happen until 2300 on the S&P 500 Index, sometime in 2017 or even 2018, then the challenges for such a large and early cash position will be even greater.

For these reasons, many great value investors – including Marks, Buffett, Munger, and many others – simply never try to time the market.  Many of these value investors essentially stay fully invested in the cheapest stocks they can find.  Over a very long period of time, many studies have shown that hedges, short positions, and cash lower the volatility of the portfolio, but also lower the long-term returns.  Given how many smart people have been hedging since 2012, the past four years and counting have provided yet another clear example of why market timing is impossible to do with any consistency.

Henry Singleton, described by both Buffett and Munger as being the best capital allocator (among CEO’s) in U.S. history, compounded business value at Teledyne at incredible rates for decades by buying stocks (including Teledyne) when they were cheap.  Singleton’s amazing track record included the 1970’s, when the broader U.S. stock market went virtually nowhere.  Singleton was a genius (100 points away from being a chess grandmaster).  On the subject of market timing, Singleton has said:

  • “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.”



Luck – chance or randomness – influences investment outcomes.  Marks considers Nassim Taleb’s Fooled by Randomness to be essential reading for investors.  Writes Marks: “Randomness (or luck) plays a huge part in life’s results, and outcomes that hinge on random events should be viewed as different from those that do not.”

Marks quotes Taleb:

“If we have heard of [history’s great generals and inventors], it is simply because they took considerable risks, along with thousands of others, and happened to win.  They were intelligent, courageous, noble (at times), had the highest possible obtainable culture in their day – but so did thousands of others who live in the musty footnotes of history.”

A central concept from Taleb is that of “alternative histories.”  What actually has happened in history is merely a small subset of all the things that could have happened, at least as far as we know.  As long as there is a component of indeterminacy in human behavior (not to mention the rest of reality), one must usually assume that many “alternative histories” were possible.  From the practical point of view of investing, given a future that is currently unknowable in many respects, one must develop a reasonable set of scenarios along with estimated probabilities for each scenario.  And, when judging the quality of past decisions, one must think carefully about various possible (“alternative”) histories, of which what actually happened appears to be a small subset.

“Thus, the fact that a stratagem or action worked – under the circumstances that unfolded – doesn’t necessarily prove that the decision behind it was wise.”


Marks says he agrees with all of Taleb’s important points:

  • Investors are right (and wrong) all the time for the ‘wrong reason.’ Someone buys a stock because he or she expects a certain development; it doesn’t occur; the market takes the stock up anyway; the investor looks good (and invariably accepts credit).
  • The correctness of a decision can’t be judged from the outcome. Neverthelss, that’s how people assess it.  A good decision is one that’s optimal at the time it’s made, when the future is by definition unknown.  Thus, correct decisions are often unsuccessful, and vice versa.
  • Randomness alone can produce just about any outcome in the short run. In portfolios that are allowed to reflect them fully, market movements can easily swamp the skillfulness of the manager (or lack thereof).  But certainly market movements cannot be credited to the manager (unless he or she is the rare market timer who’s capable of getting it right repeatedly).
  • For these reasons, investors often receive credit they don’t deserve. One good coup can be enough to build a reputation, but clearly a coup can arise out of randomness alone.  Few of these “geniuses” are right more than once or twice in a row.
  • Thus, it’s essential to have a large number of observations – lots of years of data – before judging a given manager’s ability.

Over the long run, the rational investor learns, refines, and sticks with a robust investment process that reliably produces good results.  In the short run, when a good process sometimes leads to bad outcomes (often due to bad luck but sometimes due to a mistake), one must simply be stoic and patient.

Marks continues:

“The actions of the ‘I know’ school are based on a view of a single future that is knowable and conquerable.  My ‘I don’t know’ school thinks of future events in terms of a probability distribution.  That’s a big difference.  In the latter case, we may have an idea which one outcome is most likely to occur, but we also know there are many other possibilities, and those other outcomes may have a collective likelihood much higher than the one we consider most likely.” (page 139)

Marks concludes:

  • “We should spend our time trying to find value among the knowable – industries, companies and securities – rather than base our decisions on what we expect from the less-knowable macro world of economies and broad market performance.
  • Given that we don’t know exactly which future will obtain, we have to get value on our side by having a strongly held, analytically derived opinion of it and buying for less when opportunities to do so present themselves.
  • We have to practice defensive investing, since many of the outcomes are likely to go against us. It’s more important to ensure survival under negative outcomes than it is to guarantee maximum returns under favorable ones.
  • To improve our chances of success, we have to emphasize acting contrary to the herd when it’s at extremes, being aggressive when the market is low and cautious when it’s high.
  • Given the highly indeterminate nature of outcomes, we must view strategies and their results – both good and bad – with suspicion until proved over a large number of trials.”



Unlike professional tennis, where a successful outcome depends on which player hits the most winners, successful investing generally depends on minimizing mistakes more than it does on finding winners.

“… investing is full of bad bounces and unanticipated developments, and the dimensions of the court and the height of the net change all the time.  The workings of economies and markets are highly imprecise and variable, and the thinking and behavior of the other players constantly alter the environment.  Even if you do everything right, other investors can ignore your favorite stock; management can squander the company’s opportunities; government can change the rules; or nature can serve up a catastrophe.” (page 142)

Marks argues that successful investing is a balance between offense and defense, and that this balance often differs for each individual investor.  What’s important is to stick with an investment process that works over the long term:

“… Few people (if any) have the ability to switch tactics to match market conditions on a timely basis.  So investors should commit to an approach – hopefully one that will serve them through a variety of scenarios.  They can be aggressive, hoping they’ll make a lot on the winners and not give it back on the losers.  They can emphasize defense, hoping to keep up in good times and excel by losing less than others in bad times.  Or they can balance offense and defense, largely giving up on tactical timing but aiming to win through superior security selection in both up and down markets.” (page 144)

“And by the way, there’s no right choice between offense and defense.  Lots of possible routes can bring you to success, and your decision should be a function of your personality and leanings, the extent of your belief in your ability, and the peculiarities of the markets you work in and the clients you work for.”

Marks argues that defense can be viewed as aiming for higher returns, but through the avoidance of mistakes and through consistency, rather than through home runs and occasional flashes of brilliancy.

Avoiding losses first involves buying assets at cheap prices (well below intrinsic value).  Another element to avoiding losses is to ensure that one’s portfolio can survive a bear market.  If the five-year or ten-year returns appear to be high enough, an investor still may choose to play more offense than defense, even when he or she knows that a bear market is likely within five years or less.  But one must be fully prepared – psychologically and in one’s portfolio – for many already very cheap stocks to get cut in half or worse during a bear market.

Again, some investors can accept higher volatility in exchange for higher long-term returns.  One must know oneself.  One must know one’s clients.  One must really think through all the possible scenarios, because things can get much worse than one can imagine during bear markets.  And bear markets are inevitable.

There is always a trade-off between potential return and potential downside.  Choosing to aim for higher long-term returns means accepting higher downside volatility over shorter periods of time.

But it’s important to keep in mind that many investors fail not due to lack of home runs, but due to having too many strikeouts.  Overbetting is thus a common cause of failure for long-term investors.  We know from the Kelly criterion that overbetting guarantees zero or negative long-term returns.  Therefore, it’s wise for most investors to aim for consistency – a high batting average based on many singles and doubles – rather than to aim for the maximum number of home runs.

Put differently, it is easier for most investors to minimize losses than it is to hit a lot of home runs.  Thus, most investors are much more likely to achieve long-term success by minimizing losses and mistakes, than by hitting a lot of home runs.

“Investing defensively can cause you to miss out on things that are hot and get hotter, and it can leave you with your bat on your shoulder in trip after trip to the plate.  You may hit fewer home runs than another investor… but you’re also likely to have fewer strikeouts and fewer inning-ending double plays.

Defensive investing sounds very erudite, but I can simplify it: Invest scared!  Worry about the possibility of loss.  Worry that there’s something you don’t know.  Worry that you can make high-quality decisions but still be hit by bad luck or surprise events.  Investing scared will prevent hubris; will keep your guard up and your mental adrenaline flowing; will make you insist on adequate margin of safety; and will increase the chances that your portfolio is prepared for things going wrong.  And if nothing does go wrong, surely the winners will take care of themselves.” (pages 151-152)



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

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

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

The 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.