The Black Swan


(Image:  Zen Buddha Silence by Marilyn Barbone.)

November 3, 2019

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

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

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

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

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

Here’s the outline:

    • Prologue

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

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

PART TWO: WE JUST CAN’T PREDICT

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

PART THREE: THOSE GRAY SWANS OF EXTREMISTAN

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

PART FOUR: THE END

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

 

PROLOGUE

Taleb writes:

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

Taleb defines a black swan as having three attributes:

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

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

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

Taleb continues:

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

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

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

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

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

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

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

Taleb introduces the terms Platonicity and the Platonic fold:

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

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

 

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

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

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

Taleb adds:

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

(Photo by Pp1)

 

CHAPTER 1: THE APPRENTICESHIP OF AN EMPIRICAL SKEPTIC

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

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

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

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

Then a Black Swan hit:

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

Taleb makes a general point about history:

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

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

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

Photo by Anton Samsonov

Taleb writes about the danger of oversimplification:

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

 

CHAPTER 2: YEVGENIA’S BLACK SWAN

Taleb:

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

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

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

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

Yevgenia’s book is a Black Swan.

 

 

CHAPTER 3: THE SPECULATOR AND THE PROSTITUTE

Taleb introduces Mediocristan and Extremistan:

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

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

(Photo by Flavijus)

Taleb comments on knowledge and Extremistan:

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

Taleb gives many examples:

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

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

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

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

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

 

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

Photo of turkey by Chris Galbraith

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

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

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

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

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

Roasted turkey.  Photo by Alexander Raths.

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

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

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

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

Taleb writes:

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

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

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

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

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

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

Taleb introduces the themes for the next five chapters:

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

 

CHAPTER 5: CONFIRMATION SHMONFIRMATION!

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

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

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

Taleb continues:

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

Taleb comments:

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

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

Taleb explains:

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

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

Taleb next writes:

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

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

Taleb makes the following point:

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

Taleb defines naive empiricism:

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

Taleb makes an important point here:

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

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

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

Taleb adds:

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

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

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

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

Illustration by intheskies

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

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

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

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

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

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

 

CHAPTER 6: THE NARRATIVE FALLACY

Taleb introduces the narrative fallacy:

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

Taleb continues:

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

Taleb clarifies:

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

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

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

Photo by Narokzaad

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

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

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

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

Taleb gives another example.  Read the following:

A BIRD IN THE

THE HAND IS WORTH

TWO IN THE BUSH

Notice anything unusual?  Try reading it again.  Taleb:

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

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

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

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

Dopamine molecule. Illustration by Liliya623.

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

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

Taleb again:

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

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

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

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

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

Taleb continues:

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

Which is more likely?

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

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

Next Taleb defines two kinds of Black Swan:

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

 

CHAPTER 7: LIVING IN THE ANTECHAMBER OF HOPE

Taleb explains:

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

Taleb adds:

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

Illustration by Mariusz Prusaczyk

Taleb describes a strategy of betting on the Black Swan:

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

 

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

Taleb:

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

Taleb tells the story of the drowned worshippers:

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

This is the problem of silent evidence.  Taleb again:

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

Taleb continues:

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

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

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

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

(Photo by Volodymyr Pyndyk)

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

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

Taleb offers another example:

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

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

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

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

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

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

 

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

Taleb introduces Fat Tony (from Brooklyn):

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

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

Next Taleb introduces non-Brooklyn John:

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

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

(Photo by Christian Delbert)

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

Taleb explains:

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

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

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

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

(Photo by Franky44)

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

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

Taleb adds:

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

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

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

Taleb draws a conclusion about the casino:

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

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

Taleb wraps up Part One of his book:

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

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

 

PART TWO: WE JUST CAN’T PREDICT

Taleb:

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

 

CHAPTER 10: THE SCANDAL OF PREDICTION

Taleb highlights the story of the Sydney Opera House:

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

Taleb then asks:

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

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

Illustration by Airdone.

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

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

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

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

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

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

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

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

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

Taleb continues:

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

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

Taleb adds:

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

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

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

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

Taleb comments:

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

Taleb asserts:

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

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

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

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

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

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

Taleb writes:

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

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

Taleb says:

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

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

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

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

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

Taleb continues:

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

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

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

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

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

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

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

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

 

CHAPTER 11: HOW TO LOOK FOR BIRD POOP

Taleb notes that most discoveries are the product of serendipity.

Photo by Marek Uliasz

Taleb writes:

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

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

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

Furthermore, observes Taleb:

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

Taleb adds:

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

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

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

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

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

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

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

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

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

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

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

First Kahneman and Tversky used a positive framing:

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

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

Next a negative framing:

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

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

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

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

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

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

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

 

CHAPTER 12: EPISTEMOCRACY, A DREAM

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

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

Photo by Jacek Dudzinski

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

So what would an epistemocracy look like?

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

Taleb adds:

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

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

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

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

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

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

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

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

 

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

Taleb writes about being a fool in the right places:

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

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

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

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

(Illustration by Ileezhun)

Taleb recommends trial and error:

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

Taleb then points out:

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

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

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

Taleb offers five tricks:

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

Taleb concludes:

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

Taleb explains:

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

 

PART THREE: THOSE GRAY SWANS OF EXTREMISTAN

The final four items related to the Black Swan:

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

 

CHAPTER 14: FROM MEDIOCRISTAN TO EXTREMISTAN, AND BACK

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

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

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

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

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

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

Photo by Pat Lalli

Taleb:

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

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

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

 

CHAPTER 15: THE BELL CURVE, THAT GREAT INTELLECTUAL FRAUD

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

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

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

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

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

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

(Power law graph, via Wikimedia Commons)

Taleb:

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

Taleb writes:

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

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

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

Taleb summarizes:

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

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

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

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

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

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

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

Taleb adds:

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

 

CHAPTER 16: THE AESTHETICS OF RANDOMNESS

Taleb:

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

Taleb explains:

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

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

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

Let’s consider again Mediocristan.  Taleb:

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

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

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

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

Taleb argues that fractals can make Black Swans gray:

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

Taleb continues:

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

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

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

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

Taleb observes:

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

 

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

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

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

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

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

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

Taleb comments:

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

None of that happened.

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

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

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

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

 

CHAPTER 18: THE UNCERTAINTY OF THE PHONY

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

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

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

 

PART FOUR: THE END

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

Taleb concludes:

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

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

Taleb continues:

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

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

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

Taleb’s final points:

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

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

 

BOOLE MICROCAP FUND

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

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

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

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

 

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

My e-mail: jb@boolefund.com

 

 

 

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

Fooled by Randomness

(Image:  Zen Buddha Silence by Marilyn Barbone.)

October 20, 2019

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

Here’s the outline:

    • Prologue

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

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

Part II: Monkeys on Typewriters—Survivorship and Other Biases

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

Part III: Wax in my Ears—Living With Randomitis

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

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

 

PROLOGUE

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

GENERAL

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

MARKET PERFORMANCE

Lucky idiot Skilled investor
Survivorship bias Market outperformance

FINANCE

Volatility Return (or drift)
Stochastic variable Deterministic variable

PHYSICS AND ENGINEERING

Noise Signal

LITERARY CRITICISM

None Symbol

PHILOSOPHY OF SCIENCE

Epistemic probability Physical probability
Induction Deduction
Synthetic proposition Analytic proposition

 

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

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

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

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

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

(Photo by Neil Lockhart)

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

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

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

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

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

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

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

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

Taleb writes:

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

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

(A lucky seven.  Photo by Eagleflying)

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

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

Photo by Antoniodiaz

 

TWO: A BIZARRE ACCOUNTING METHOD

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

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

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

Photo by Banjong Khanyai

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

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

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

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

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

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

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

 

THREE: A MATHEMATICAL MEDITATION ON HISTORY

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

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

Photo by Emily2k

Taleb says:

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

Taleb continues:

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

Note from a value investing point of view

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

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

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

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

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

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

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

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

 

FOUR: RANDOMNESS, NONSENSE, AND THE SCIENTIFIC INTELLECTUAL

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

 

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

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

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

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

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

Taleb continues:

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

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

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

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

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

Taleb adds:

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

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

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

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

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

 

SIX: SKEWNESS AND ASYMMETRY

Taleb presents the following Table:

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

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

Taleb comments that even professional investors misunderstand this bet:

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

(Coin toss.  Photo by Christian Delbert)

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

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

Rare Events

Taleb explains his trading strategy:

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

Illustration by lqoncept

Taleb gives an example where his strategy paid off:

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

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

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

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

Taleb notes the following:

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

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

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

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

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

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

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

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

 

SEVEN: THE PROBLEM OF INDUCTION

Taleb quotes the Scottish philosopher David Hume:

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

(Black swan.  Photo by Damithri)

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

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

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

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

Taleb explains the concept of an open society:

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

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

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

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

 

PART II: MONKEYS ON TYPEWRITERS—SURVIVORSHIP AND OTHER BIASES

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

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

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

 

EIGHT: TOO MANY MILLIONAIRES NEXT DOOR

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

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

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

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

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

 

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

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

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

Photo by Alhovik

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

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

Taleb makes two counterintuitive points:

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

Taleb concludes:

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

The mysterious letter

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

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

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

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

Taleb continues:

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

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

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

 

TEN: LOSER TAKES ALL—ONE THE NONLINEARITIES OF LIFE

Taleb writes:

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

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

(Photo by Maocheng)

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

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

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

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

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

 

ELEVEN: RANDOMNESS AND OUR BRAIN—WE ARE PROBABILITY BLIND

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

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

(Diagram by Oleg Alexandrov, via Wikimedia Commons)

Taleb:

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

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

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

Linda is 31 years old, single, outspoken, and very bright.  She majored in philosophy.  As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

Which is more probable?

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

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

(By svjo, via Wikimedia Commons)

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

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

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

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

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

Number of afflicted persons  /  Number of true and false positives

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

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

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

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

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

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

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

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

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

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

 

PART III: WAX IN MY EARS—LIVING WITH RANDOMITIS

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

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

Taleb admits that he is dominated by his emotions:

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

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

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

 

TWELVE: GAMBLERS’ TICKS AND PIGEONS IN A BOX

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

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

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

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

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

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

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

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

 

THIRTEEN: CARNEADES COMES TO ROME—ON PROBABILITY AND SKEPTICISM

Taleb writes:

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

Taleb holds that Cicero engaged in probabilistic reasoning:

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

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

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

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

 

FOURTEEN: BACCHUS ABANDONS ANTONY

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

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

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

Taleb concludes with some advice (stoicism):

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

 

BOOLE MICROCAP FUND

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

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

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

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

 

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

My e-mail: jb@boolefund.com

 

 

 

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

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

(Image:  Zen Buddha Silence by Marilyn Barbone.)

October 13, 2019

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

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

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

Mohnish sums up the Dhandho approach as:

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

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

10-BAGGERS TO 100-BAGGERS

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

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

Here’s the outline for this blog post:

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

 

PATEL MOTEL DHANDHO

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

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

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

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

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

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

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

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

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

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

 

MANILAL DHANDHO

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

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

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

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

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

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

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

 

VIRGIN DHANDHO

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

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

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

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

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

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

 

MITTAL DHANDHO

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

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

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

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

 

THE DHANDHO FRAMEWORK

Mohnish lays out the Dhando framework, including:

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

Let’s look at each point.

 

DHANDHO 101: INVEST IN EXISTING BUSINESSES

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

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

 

DHANDHO 102: INVEST IN SIMPLE BUSINESSES

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

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

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

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

 

DHANDHO 201: INVEST IN DISTRESSED BUSINESSES IN DISTRESSED INDUSTRIES

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

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

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

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

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

 

DHANDHO 202: INVEST IN BUSINESSES WITH DURABLE MOATS

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

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

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

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

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

Mohnish quotes Charlie Munger:

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

Mohnish adds:

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

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

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

 

DHANDHO 301: FEW BETS, BIG BETS, INFREQUENT BETS

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

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

The Kelly criterion can be written as follows:

  • F = p – [q/o]

where

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

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

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

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

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

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

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

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

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

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

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

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

(These percentages are rounded for simplicity.)

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

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

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

Mohnish quotes Charlie Munger again:

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

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

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

We might invest up to 40% of our net worth in a single security under conditions coupling an extremely high probability that our facts and reasoning are correct with a very low probability that anything could change the underlying value of the investment.

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

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

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

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

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

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

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

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

Mohnish concludes:

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

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

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

 

DHANDHO 302: FIXATE ON ARBITRAGE

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

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

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

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

 

DHANDHO 401: MARGIN OF SAFETY—ALWAYS!

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Frontline

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

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

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

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

Mohnish continues:

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

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

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

Mohnish notes the net asset value of Frontline:

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

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

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

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

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

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

Mohnish gives a final piece of advice:

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

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

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

 

DHANDHO 403: INVEST IN THE COPYCATS RATHER THAN THE INNOVATORS

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

Mohnish writes:

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

 

A SHORT CHECKLIST

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

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

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

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

A few more points:

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

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

 

BE GENEROUS

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

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

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

 

BOOLE MICROCAP FUND

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

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

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

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

 

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

My e-mail: jb@boolefund.com

 

 

 

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

Seeking Wisdom

(Image:  Zen Buddha Silence by Marilyn Barbone)

October 6, 2019

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

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

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

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

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

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

Here’s the outline:

PART ONE:  WHAT INFLUENCES OUR THINKING

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

PART TWO:  THE PSYCHOLOGY OF MISJUDGMENTS

  • Misjudgments explained by psychology
  • Psychological reasons for mistakes

PART THREE:  THE PHYSICS AND MATHEMATICS OF MISJUDGMENTS

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

PART FOUR:  GUIDELINES TO BETTER THINKING

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

(Photo by Nick Webb)

 

Part One:  What Influences Our Thinking

OUR ANATOMY SETS THE LIMITS FOR OUR BEHAVIOR

Bevelin quotes Nobel Laureate Dr. Gerald Edelman:

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

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

(Illustration by ustas)

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

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

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

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

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

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

 

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

Bevelin writes:

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

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

(Illustration by goce risteski)

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

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

 

ADAPTIVE BEHAVIOR FOR SURVIVAL AND REPRODUCTION

Bevelin:

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

Illustration by kalpis

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

 

Part Two:  The Psychology of Misjudgments

Bevelin quotes the Greek philosopher and orator, Dio Chrysostom:

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

MISJUDGMENTS EXPLAINED BY PSYCHOLOGY

Illustration by intheskies

Bevelin lists 28 reasons for misjudgments and mistakes:

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

 

PSYCHOLOGICAL REASONS FOR MISTAKES

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

1. Mere Association

Bevelin:

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

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

2. Incentives (Reward and Punishment)

Incentives are extremely important.   Charlie Munger:

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

Munger again:

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

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

3. Self-interest and Incentives

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

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

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

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

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

4. Self-serving Tendencies and Overoptimism 

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

Bevelin again:

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

5. Self-deception and Denial

Munger likes to quote Demosthenes:

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

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

Richard Feynman:

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

6. Consistency

Bevelin:

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

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

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

Bevelin quotes Warren Buffett:

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

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

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

7. Deprival Syndrome

Bevelin:

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

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

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

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

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

8. Status Quo and Do-Nothing Syndrome

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

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

9. Impatience

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

10. Envy and Jealousy

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

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

11. Contrast Comparison

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

Bevelin writes:

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

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

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

Bevelin observes:

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

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

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

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

12. Anchoring

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

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

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

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

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

13. Vividness and Recency

Bevelin explains:

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

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

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

14. Omission and Abstract Blindness

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

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

15. Reciprocation

Munger:

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

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

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

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

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

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

Guilt is also a net positive, asserts Munger:

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

16. Liking and Disliking

Munger:

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

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

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

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

Warren Buffett:

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

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

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

Munger explains:

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

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

17. Social Proof

Munger comments:

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

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

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

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

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

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

Munger points out that Social Proof can sometimes be constructive:

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

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

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

18. Authority

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

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

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

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

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

Bevelin quotes the British novelist and scientist Charles Percy Snow:

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

19. The Narrative Fallacy (Sensemaking)

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

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

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

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

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

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

20. Reason-respecting

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

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

21. Believe First and Doubt Later

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

Bevelin continues:

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

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

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

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

22. Memory Limitations

Bevelin:

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

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

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

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

23. Do-something Syndrome

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

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

24. Say-something Syndrome

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

25. Emotions

Bevelin writes:

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

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

26. Stress

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

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

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

27. Pain and Chemicals

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

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

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

28. Multiple Tendencies

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

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

 

Part Three:  The Physics and Mathematics of Misjudgments

SYSTEMS THINKING

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

 

SCALE AND LIMITS

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

 

CAUSES

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

 

NUMBERS AND THEIR MEANING

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

 

PROBABILITIES AND NUMBER OF POSSIBLE OUTCOMES

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

 

SCENARIOS

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

 

COINCIDENCES AND MIRACLES

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

 

RELIABILITY OF CASE EVIDENCE

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

 

MISREPRESENTATIVE EVIDENCE

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

 

Part Four:  Guidelines to Better Thinking

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

Bevelin lists 12 tools that he discusses:

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

 

MODELS OF REALITY

Bevelin:

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

Bevelin continues:

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

What models are most reliable?  Bevelin quotes Munger:

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

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

Bevelin adds:

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

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

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

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

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

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

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

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

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

 

MEANING

Bevelin writes:

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

    • Meaning of words:  What do the words mean?  What do they imply?  Do they mean anything?  Can we translate words, ideas, or statements into an ordinary situation that tells us something?  An expression is always relative.  We have to judge and measure it against something.
    • Meaning of an event:  What effect is produced?  What is really happening using ordinary words?  What is it doing?  What is accomplished?  Under what conditions does it happen?  What else does it mean?
    • Causes:  What is happening here and why?  Is this working?  Why or why not?  Why did that happen?  Why does it work here but not there?  How can it happen?  What are the mechanisms behind?  What makes it happen?
    • Implications:  What is the consequence of this observation, event, or experience?  What does that imply?
    • Purpose:  Why should we do that?  Why do I want this to happen?
    • Reason:  Why is this better than that?
    • Usefulness:  What is the applicability of this?  Does it mean anything in relation to what I want to achieve?

Turning to the field of investing, how do we decide how much to pay for a business?  Buying stock is buying a fractional share of a business.  Bevelin quotes Warren Buffett:

What you’re trying to do is to look at all the cash a business will produce between now and judgment day and discount it back to the present using an appropriate discount rate and buy a lot cheaper than that.  Whether the money comes from a bank, an Internet company, a brick company… the money all spends the same.  Why pay more for a telecom business than a brick business?  Money doesn’t know where it comes from.  There’s no sense in paying more for a glamorous business if you’re getting the same amount of money, but paying more for it.  It’s the same money that you can get from a brick company at a lower cost.  The question is what are the economic characteristics of the bank, the Internet company, or the brick company.  That’s going to tell you how much cash they generate over long periods in the future.

 

SIMPLIFICATION

Bevelin quotes Munger:

We have a passion for keeping things simple.

Bevelin then quotes Buffett:

We haven’t succeeded because we have some great, complicated systems or magic formulas we apply or anything of the sort.  What we have is just simplicity itself.

Munger again:

If something is too hard, we move on to something else.  What could be more simple than that?

Munger:

There are things that we stay away from.  We’re like the man who said he had three baskets on his desk: in, out, and too tough.  We have such baskets—mental baskets—in our office.  An awful lot of stuff goes in the ‘too tough’ basket.

Buffett on how he and Charlie Munger do it:

Easy does it.  After 25 years of buying and supervising a great variety of businesses, Charlie and I have not learned how to solve difficult business problems.  What we have learned is to avoid them.  To the extent we have been successful, it is because we concentrated on identifying one-foot hurdles that we could step over rather than because we acquired any ability to clear seven-footers.  The finding may seem unfair, but in both business and investments it is usually far more profitable to simply stick with the easy and obvious than it is to resolve the difficult.

It’s essential that management maintain focus.  Buffett:

A… serious problem occurs when the management of a great company gets sidetracked and neglects its wonderful base business while purchasing other businesses that are so-so or worse… (Would you believe that a few decades back they wee growing shrimp at Coke and exploring for oil at Gillette?)  Loss of focus is what most worries Charlie and me when we contemplate investing in businesses that in general look outstanding.  All too often, we’ve seen value stagnate in the presence of hubris or of boredom that caused the attention of managers to wander.

For an investor considering an investment, it’s crucial to identify what is knowable and what is important.  Buffett:

There are two questions  you ask yourself as you look at the decision you’ll make.  A) is it knowable?  B) is it important?  If it is not knowable, as you know there are all kinds of things that are important but not knowable, we forget about those.  And if it’s unimportant, whether it’s knowable or not, it won’t make any difference.  We don’t care.

 

RULES AND FILTERS

Bevelin writes:

When we know what we want, we need criteria to evaluate alternatives.  Ask: What are the most critical (and knowable) factors that will cause what I want to achieve or avoid?  Criteria must be based on evidence and be reasonably predictive… Try to use as few criteria as necessary to make your judgment.  Then rank them in order of their importance and use them as filters.  Set decision thresholds in a way that minimizes the likelihood of false alarms and misses (in investing, choosing a bad investment or missing a good investment).  Consider the consequences of being wrong.

Bear in mind that in many fields, a relatively simple statistical prediction rule based on a few key variables will perform better than experts over time.  See: http://boolefund.com/simple-quant-models-beat-experts-in-a-wide-variety-of-areas/

Bevelin gives as an example the following: a man is rushed to the hospital while having a heart attack.  Is it high-risk or low-risk?  If the patient’s minimum systolic blood pressure over the initial 24-hour period is less than 91, then it’s high-risk.  If not, then the next question is age.  If the patient is over 62.5 years old, then if he displays sinus tachycardia, he is high-risk.  It turns out that this simple model—developed by Statistics Professor Leo Breiman and colleagues at the University of California, Berkeley—works better than more complex models and also than experts.

In making an investment decision, Buffett has said that he uses a 4-step filter:

    • Can I understand it?
    • Does it look like it has some kind of sustainable competitive advantage?
    • Is the management composed of able and honest people?
    • Is the price right?

If a potential investment passes all four filters, then Buffett writes a check.  By “understanding,” Buffett means having a “reasonable probability” of assessing whether the business will be in 10 years.

 

GOALS

Bevelin puts forth:

Always ask:  What end result do I want?  What causes that?  What factors have a major impact on the outcome?  What single factor has the most impact?  Do I have the variable(s) needed for the goal to be achieved?  What is the best way to achieve my goal?  Have I considered what other effects my actions will have that will influence the final outcome?

When we solve problems and know what we want to achieve, we need to prioritize or focus on the right problems.  What should we do first?  Ask:  How serious are the problems?  Are they fixable?  What is the most important problem?  Are the assumptions behind them correct?  Did we consider the interconnectedness of the problems?  The long-term consequences?

 

ALTERNATIVES

Bevelin writes:

Choices have costs.  Even understanding has an opportunity cost.  If we understand one thing well, we may understand other things better.  The cost of using a limited resource like time, effort, and money for a specific purpose, can be measured as the value or opportunity lost by not using it in its best available alternative use…

Bevelin considers a business:

Should TransCorp take the time, money, and talent to build a market presence in Montana?  The real cost of doing that is the value of the time, money, and talent used in its best alternative use.  Maybe increasing their presence in a state where they already have a market share is creating more value.  Sometimes it is more profitable to marginally increase a cost where a company already has an infrastructure.  Where to they marginally get the most leverage on resources spent?  Always ask: What is the change of value of taking a specific action?  Where is it best to invest resources from a value point of view?

 

CONSEQUENCES

Bevelin writes:

Whenever we install a policy, take an action, or evaluate statements, we must trace the consequences.  When doing so, we must remember four key things:

  • Pay attention to the whole system.  Direct and indirect effects,
  • Consequences have implications or more consequences, some which may be unwanted.  We can’t estimate all possible consequences but there is at least one unwanted consequence we should look out for,
  • Consider the effects of feedback, time, scale, repetition, critical thresholds and limits,
  • Different alternatives have different consequences in terms of costs and benefits.  Estimate the net effects over time and how desirable these are compared to what we want to achieve.

We should heed Buffett’s advice:  Whenever someone makes an assertion in economics, always ask, “And then what?”  Very often, particularly in economics, it’s the consequences of the consequences that matter.

 

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  http://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC

Walter Schloss: Cigar-Butt Specialist

September 29, 2019

Walter Schloss generated one of the best investment track records of all time—close to 21% (gross) annually over 47 years—by investing exclusively in cigar butts (deep value stocks).  Cigar-butt investing usually means buying stock at a discount to book value, i.e., a P/B < 1 (price-to-book ratio below 1).

The highest returning cigar butt strategy comes from Ben Graham, the father of value investing.  It’s called the net-net strategy whereby you take current assets minus all liabilities, and then invest at 2/3 of that level or less.

  • The main trouble with net nets today is that many of them are tiny microcap stocks—below $50 million in market cap—that are too small even for most microcap funds.
  • Also, many net nets exist in markets outside the United States.  Some of these markets have had problems periodically related to the rule of law.

Schloss used net nets in the early part of his career (1955 to 1960).  When net nets became too scarce (1960), Schloss started buying stocks at half of book value.  When those became too scarce, he went to buying stocks at two-thirds of book value.  Eventually he had to adjust again and buy stocks at book value.  Though his cigar-butt method evolved, Schloss was always using a low P/B to find cheap stocks.

(Photo by Sky Sirasitwattana)

One extraordinary aspect to Schloss’s track record is that he invested in roughly 1,000 stocks over the course of his career.  (At any given time, his portfolio had about 100 stocks.)  Warren Buffett commented:

Following a strategy that involved no real risk—defined as permanent loss of capital—Walter produced results over his 47 partnership years that dramatically surpassed those of the S&P 500.  It’s particularly noteworthy that he built this record by investing in about 1,000 securities, mostly of a lackluster type.  A few big winners did not account for his success.  It’s safe to say that had millions of investment managers made trades by a) drawing stock names from a hat; b) purchasing these stocks in comparable amounts when Walter made a purchase; and then c) selling when Walter sold his pick, the luckiest of them would not have come close to equaling his record. There is simply no possibility that what Walter achieved over 47 years was due to chance.

Schloss was aware that a concentrated portfolio—e.g., 10 to 20 stocks—could generate better long-term returns.  However, this requires unusual insight on a repeated basis, which Schloss humbly admitted he didn’t have.

Most investors are best off investing in low-cost index funds or in quantitative value funds.  For investors who truly enjoy looking for undervalued stocks, Schloss offered this advice:

It is important to know what you like and what you are good at and not worry that someone else can do it better.  If you are honest, hardworking, reasonably intelligent and have good common sense, you can do well in the investment field as long as you are not too greedy and don’t get too emotional when things go against you.

I found a few articles I hadn’t seen before on The Walter Schloss Archive, a great resource page created by Elevation Capital: https://www.walterschloss.com/

Here’s the outline for this blog post:

  • Stock is Part Ownership;  Keep It Simple
  • Have Patience;  Don’t Sell on Bad News
  • Have Courage
  • Buy Assets Not Earnings
  • Buy Based on Cheapness Now, Not Cheapness Later
  • Boeing:  Asset Play
  • Less Downside Means More Upside
  • Multiple Ways to Win
  • History;  Honesty;  Insider Ownership
  • You Must Be Willing to Make Mistakes
  • Don’t Try to Time the Market
  • When to Sell
  • The First 10 Years Are Probably the Worst
  • Stay Informed About Current Events
  • Control Your Emotions;  Be Careful of Leverage
  • Ride Coattails;  Diversify

 

STOCK IS PART OWNERSHIP;  KEEP IT SIMPLE

A share of stock represents part ownership of a business and is not just a piece of paper or a blip on the computer screen.

Try to establish the value of the company.  Use book value as a starting point.  There are many businesses, both public and private, for which book value is a reasonable estimate of intrinsic value.  Intrinsic value is what a company is worth—i.e., what a private buyer would pay for it.  Book value—assets minus liabilities—is also called “net worth.”

Follow Buffett’s advice: keep it simple and don’t use higher mathematics.

(Illustration by Ileezhun)

Some kinds of stocks are easier to analyze than others.  As Buffett has said, usually you don’t get paid for degree of difficulty in investing.  Therefore, stay focused on businesses that you can fully understand.

  • There are thousands of microcap companies that are completed neglected by most professional investors.  Many of these small businesses are simple and easy to understand.

 

HAVE PATIENCE;  DON’T SELL ON BAD NEWS

Hold for 3 to 5 years.  Schloss:

Have patience.  Stocks don’t go up immediately.

Schloss again:

Things usually take longer to work out but they work out better than you expect.

(Illustration by Marek)

Don’t sell on bad news unless intrinsic value has dropped materially.  When the stock drops significantly, buy more as long as the investment thesis is intact.

Schloss’s average holding period was 4 years.  It was less than 4 years in good markets when stocks went up more than usual.  It was greater than 4 years in bad markets when stocks stayed flat or went down more than usual.

 

HAVE COURAGE

Have the courage of your convictions once you have made a decision.

(Courage concept by Travelling-light)

Investors shun companies with depressed earnings and cash flows.  It’s painful to own stocks that are widely hated.  It can also be frightening.  As John Mihaljevic explains in The Manual of Ideas (Wiley, 2013):

Playing into the psychological discomfort of Graham-style equities is the tendency of such investments to exhibit strong asset value but inferior earnings or cash flows.  In a stressed situation, investors may doubt their investment theses to such an extent that they disregard the objectively appraised asset values.  After all—the reasoning of a scared investor might go—what is an asset really worth if it produces no cash flow?

A related worry is that if a company is burning through its cash, it will gradually destroy net asset value.  Ben Graham:

If the profits had been increasing steadily it is obvious that the shares would not sell at so low a price.  The objection to buying these issues lies in the probability, or at least the possibility, that earnings will decline or losses continue, and that the resources will be dissipated and the intrinsic value ultimately become less than the price paid.

It’s true that an individual cigar butt (deep value stock) is more likely to underperform than an average stock.  But because the potential upside for a typical cigar butt is greater than the potential downside, a basket of cigar butts (portfolio of at least 30) does better than the market over time and also has less downside during bad states of the world—such as bear markets and recessions.

Schloss discussed an example: Cleveland Cliffs, an iron ore producer.  Buffett owned the stock at $18 but then sold at about that level.  The steel industry went into decline.  The largest shareholder sold out because he thought the industry wouldn’t recover.

Schloss bought a lot of stock at $6.  Nobody wanted it.  There was talk of bankruptcy.  Schloss noted that if he had lived in Cleveland, he probably wouldn’t have been able to buy the stock because all the bad news would have been too close.

Soon thereafter, the company sold some assets and bought back some stock.  After the stock increased a great deal from the lows, then it started getting attention from analysts.

In sum, often when an industry is doing terribly, that’s the best time to find cheap stocks.  Investors avoid stocks when they’re having problems, which is why they get so cheap.  Investors overreact to negative news.

 

BUY ASSETS NOT EARNINGS

(Illustration by Teguh Jati Prasetyo)

Schloss:

Try to buy assets at a discount [rather] than to buy earnings.  Earnings can change dramatically in a short time.  Usually assets change slowly.  One has to know much more about a company if one buys earnings.

Not only can earnings change dramatically; earnings can easily be manipulated—often legally.  Schloss:

Ben made the point in one of his articles that if U.S. Steel wrote down their plants to a dollar, they would show very large earnings because they would not have to depreciate them anymore.

 

BUY BASED ON CHEAPNESS NOW, NOT CHEAPNESS LATER

Buy things based on cheapness now.  Don’t buy based on cheapness relative to future earnings, which are hard to predict.

Graham developed two ways of estimating intrinsic value that don’t depend on predicting the future:

  • Net asset value
  • Current and past earnings

Professor Bruce Greenwald, in Value Investing (Wiley, 2004), has expanded on these two approaches.

  • As Greenwald explains, book value is a good estimate of intrinsic value if book value is close to the replacement cost of the assets.  The true economic value of the assets is the cost of reproducing them at current prices.
  • Another way to determine intrinsic value is to figure out earnings power—also called normalized earnings—or how much the company should earn on average over the business cycle.  Earnings power typically corresponds to a market level return on the reproduction value of the assets.  In this case, your intrinsic value estimate based on normalized earnings should equal your intrinsic value estimate based on the reproduction value of the assets.

In some cases, earnings power may exceed a market level return on the reproduction value of the assets.  This means that the ROIC (return on invested capital) exceeds the cost of capital.  It can be exceedingly difficult, however, to determine by how much and for how long earnings power will exceed a market level return.  Often it’s a question of how long some competitive advantage can be maintained.  How long can a high ROIC be sustained?

As Buffett remarked:

The key to investing is not assessing how much an industry is going to affect society, or how much it will grow, but rather determining the competitive advantage of any given company and, above all, the durability of that advantage.  The products or services that have wide, sustainable moats around them are the ones that deliver rewards to investors.

A moat is a sustainable competitive advantage.  Schloss readily admits he can’t determine which competitive advantages are sustainable.  That requires unusual insight.  Buffett can do it, but very few investors can.

As far as franchises or good businesses—companies worth more than adjusted book value—Schloss says he likes these companies, but rarely considers buying them unless the stock is close to book value.  As a result, Schloss usually buys mediocre and bad businesses at book value or below.  Schloss buys “difficult businesses” at clearly cheap prices.

Buying a high-growing company on the expectation that growth will continue can be quite dangerous.  First, growth only creates value if the ROIC exceeds the cost of capital.  Second, expectations for the typical growth stock are so high that even a small slowdown can cause the stock to drop noticeably.  Schloss:

If observers are expecting the earnings to grow from $1.00 to $1.50 to $2.00 and then $2.50, an earnings disappointment can knock a $40 stock down to $20.  You can lose half your money just because the earnings fell out of bed.

If you buy a debt-free stock with a $15 book selling at $10, it can go down to $8.  It’s not great, but it’s not terrible either.  On the other hand, if things turn around, that stock can sell at $25 if it develops its earnings.

Basically, we like protection on the downside.  A $10 stock with a $15 book can offer pretty good protection.  By using book value as a parameter, we can protect ourselves on the downside and not get hurt too badly.

Also, I think the person who buys earnings has got to follow it all the darn time.  They’re constantly driven by earnings, they’re driven by timing.  I’m amazed.

 

BOEING:  ASSET PLAY

(Boeing 377 Stratocruiser, San Diego Air & Space Museum Archives, via Wikimedia Commons)

Cigar butts—deep value stocks—are characterized by two things:

  • Poor past performance;
  • Low expectations for future performance, i.e., low multiples (low P/B, low P/E, etc.)

Schloss has pointed out that Graham would often compare two companies.  Here’s an example:

One was a very popular company with a book value of $10 selling at $45.  The second was exactly the reverse—it had a book value of $40 and was selling for $25.

In fact, it was exactly the same company, Boeing, in two very different periods of time.  In 1939, Boeing was selling at $45 with a book of $10 and earning very little.  But the outlook was great.  In 1947, after World War II, investors saw no future for Boeing, thinking no one was going to buy all these airplanes.

If you’d bought Boeing in 1939 at $45, you would have done rather badly.  But if you’d bought Boeing in 1947 when the outlook was bad, you would have done very well.

Because a cigar butt is defined by poor recent performance and low expectations, there can be a great deal of upside if performance improves.  For instance, if a stock is at a P/E (price-to-earnings ratio) of 5 and if earnings are 33% of normal, then if earnings return to normal and if the P/E moves to 15, you’ll make 900% on your investment.  If the initial purchase is below true book value—based on the replacement cost of the assets—then you have downside protection in case earnings don’t recover.

 

LESS DOWNSIDE MEANS MORE UPSIDE

If you buy stocks that are protected on the downside, the upside takes care of itself.

The main way to get protection on the downside is by paying a low price relative to book value.  If in addition to quantitative cheapness you focus on companies with low debt, that adds additional downside protection.

If the stock is well below probable intrinsic value, then you should buy more on the way down.  The lower the price relative to intrinsic value, the less downside and the more upside.  As risk decreases, potential return increases.  This is the opposite of what modern finance theory teaches.  According to theory, your expected return only increases if your risk also increases.

In The Superinvestors of Graham-and-Doddsville, Warren Buffett discusses the relationship between risk and reward.  Sometimes risk and reward are positively correlated.  Buffett gives the example of Russian roulette.  Suppose a gun contains one cartridge and someone offers to pay you $1 million if you pull the trigger once and survive.  Say you decline the bet as too risky, but then the person offers to pay you $5 million if you pull the trigger twice and survive.  Clearly that would be a positive correlation between risk and reward.  Buffett continues:

The exact opposite is true with value investing.  If you buy a dollar bill for 60 cents, it’s riskier than if you buy a dollar bill for 40 cents, but the expectation of reward is greater in the latter case.  The greater the potential for reward in the value portfolio, the less risk there is.

One quick example:  The Washington Post Company in 1973 was selling for $80 million in the market.  At the time, that day, you could have sold the assets to any one of ten buyers for not less than $400 million, probably appreciably more.  The company owned the Post, Newsweek, plus several television stations in major markets.  Those same properties are worth $2 billion now, so the person who would have paid $400 million would not have been crazy.

Now, if the stock had declined even further to a price that made the valuation $40 million instead of $80 million, its beta would have been greater.  And to people that think beta measures risk, the cheaper price would have made it look riskier.  This is truly Alice in Wonderland.  I have never been able to figure out why it’s riskier to buy $400 million worth of properties for $40 million than $80 million.

Link: https://bit.ly/2jBezdv

Most brokers don’t recommend buying more on the way down because most people (including brokers’ clients) don’t like to buy when the price keeps falling.  In other words, most investors focus on price instead of intrinsic value.

 

MULTIPLE WAYS TO WIN

A stock trading at a low price relative to book value—a low P/B stock—is usually distressed and is experiencing problems.  But there are several ways for a cigar-butt investor to win, as Schloss explains:

The thing about buying depressed stocks is that you really have three strings to your bow:  1) Earnings will improve and the stocks will go up;  2) somebody will come in and buy control of the company;  or 3) the company will start buying its own stock and ask for tenders.

Schloss again:

But lots of times when you buy a cheap stock for one reason, that reason doesn’t pan out but another reason does—because it’s cheap.

 

HISTORY;  HONESTY;  INSIDER OWNERSHIP

Look at the history of the company.  Value line is helpful for looking at history 10-15 years back.  Also, read the annual reports.  Learn about the ownership, what the company has done, when business they’re in, and what’s happened with dividends, sales, earnings, etc.

It’s usually better not to talk with management because it’s easy to be blinded by their charisma or sales skill:

When we buy into a company that has problems, we find it difficult talking to management as they tend to be optimistic.

That said, try to ensure that management is honest.  Honesty is more important than brilliance, says Schloss:

…we try to get in with people we feel are honest.  That doesn’t mean they’re necessarily smart—they may be dumb.

But in a choice between a smart guy with a bad reputation or a dumb guy, I think I’d go with the dumb guy who’s honest.

Finally, insider ownership is important.  Management should own a fair amount of stock, which helps to align their incentives with the interests of the stockholders.

Speaking of insider ownership, Walter and Edwin Schloss had a good chunk of their own money invested in the fund they managed.  You should prefer investment managers who, like the Schlosses, eat their own cooking.

 

YOU MUST BE WILLING TO MAKE MISTAKES

(Illustration by Lkeskinen0)

You have to be willing to make mistakes if you want to succeed as an investor.  Even the best value investors tend to be right about 60% of the time and wrong 40% of the time.  That’s the nature of the game.

You can’t do well unless you accept that you’ll make plenty of mistakes.  The key, again, is to try to limit your downside by buying well below probable intrinsic value.  The lower the price you pay (relative to estimated intrinsic value), the less you can lose when you’re wrong and the more you can make when you’re right.

 

DON’T TRY TO TIME THE MARKET

No one can predict the stock market.  Ben Graham observed:

If I have noticed anything over these sixty years on Wall Street, it is that people do not succeed in forecasting what’s going to happen to the stock market.

(Illustration by Maxim Popov)

Or as value investor Seth Klarman has put it:

In reality, no one knows what the market will do; trying to predict it is a waste of time, and investing based upon that prediction is a speculative undertaking.

Perhaps the best quote comes from Henry Singleton, a business genius (100 points from being a chess grandmaster) who was easily one of the best capital allocators in American business history:

I don’t believe all this nonsense about market timing.  Just buy very good value and when the market is ready that value will be recognized.

Singleton built Teledyne using extraordinary capital allocation skills over the course of more than three decades, from 1960 to the early 1990’s.  Fourteen of these years—1968 to 1982—were a secular bear market during which stocks were relatively flat and also experienced a few large downward moves (especially 1973-1974).  But this long flat period punctuated by bear markets didn’t slow down or change Singleton’s approach.  Because he consistently bought very good value, on the whole his acquisitions grew significantly in worth over time regardless of whether the broader market was down, flat, or up.

Of course, it’s true that if you buy an undervalued stock and then there’s a bear market, it may take longer for your investment to work.  However, bear markets create many bargains.  As long as you maintain a focus on the next 3 to 5 years, bear markets are wonderful times to buy cheap stocks (including more of what you already own).

In 1955, Buffett was advised by his two heroes, his father and Ben Graham, not to start a career in investing because the market was too high.  Similarly, Graham told Schloss in 1955 that it wasn’t a good time to start.

Both Buffett and Schloss ignored the advice.  In hindsight, both Buffett and Schloss made great decisions.  Of course, Singleton would have made the same decision as Buffett and Schloss.  Even if the market is high, there are invariably individual stocks hidden somewhere that are cheap.

Schloss always remained fully invested because he knew that virtually no one can time the market except by luck.

 

WHEN TO SELL

Don’t be in too much of a hurry to sell… Before selling try to reevaluate the company again and see where the stock sells in relation to its book value.

Selling is hard.  Schloss readily admits that many stocks he sold later increased a great deal.  But he doesn’t dwell on that.

The basic criterion for selling is whether the stock price is close to estimated intrinsic value.  For a cigar butt investor like Schloss, if he paid a price that was half book, then if the stock price approaches book value, it’s probably time to start selling.  (Unless it’s a rare stock that is clearly worth more than book value, assuming the investor was able to buy it low in the first place.)

If stock A is cheaper than stock B, some value investors will sell A and buy B.  Schloss doesn’t do that.  It often takes four years for one of Schloss’s investments to work.  If he already has been waiting for 1-3 years with stock A, he is not inclined to switch out of it because he might have to wait another 1-3 years before stock B starts to move.  Also, it’s very difficult to compare the relative cheapness of stocks in different industries.

Instead, Schloss makes an independent buy or sell decision for every stock.  If B is cheap, Schloss simply buys B without selling anything else.  If A is no longer cheap, Schloss sells A without buying anything else.

 

THE FIRST 10 YEARS ARE PROBABLY THE WORST

John Templeton’s worst ten years as an investor were his first ten years.  The same was true for Schloss, who commented that it takes about ten years to get the hang of value investing.

 

STAY INFORMED ABOUT CURRENT EVENTS

(Photo by Juan Moyano)

Walter Schloss and his son Edwin sometimes would spend a whole day discussing current events, social trends, etc.  Edwin Schloss said:

If you’re not in touch with what’s going on or you don’t see what’s going on around you, you can miss out on a lot of investment opportunities. So we try to be aware of everything around us—like John Templeton says in his book about being open to new ideas and new experiences.

 

CONTROL YOUR EMOTIONS;  BE CAREFUL OF LEVERAGE

Try not to let your emotions affect your judgment.  Fear and greed are probably the worst emotions to have in connection with the purchase and sale of stocks.

Quantitative investing is a good way to control emotion.  This is what Graham suggested and practiced.  Graham just looked at the numbers to make sure they were below some threshold—like 2/3 of current assets minus all liabilities (the net-net method).  Graham typically was not interested in what the business did.

On the topic of discipline and controlling your emotions, Schloss told a great story about when Warren Buffett was playing golf with some buddies:

One of them proposed, “Warren, if you shoot a hole-in-one on this 18-hole course, we’ll give you $10,000 bucks.  If you don’t shoot a hole-in-one, you owe us $10.”

Warren thought about it and said, “I’m not taking the bet.”

The others said, “Why don’t you?  The most you can lose is $10. You can make $10,000.”

Warren replied, If you’re not disciplined in the little things, you won’t be disciplined in the big things.”

Be careful of leverage.  It can go against you.  Schloss acknowledges that sometimes he has gotten too greedy by buying highly leveraged stocks because they seemed really cheap.  Companies with high leverage can occasionally become especially cheap compared to book value.  But often the risk of bankruptcy is too high.

Still, as conservative value investor Seth Klarman has remarked, there’s room in the portfolio occasionally for a super cheap, highly indebted company.  If the probability of success is high enough and if the upside is great enough, it may not be a difficult decision.  Often the upside can be 10x or 20x your investment, which implies a positive expected return even when the odds of success are 10%.

 

RIDE COATTAILS;  DIVERSIFY

Sometimes you can get good ideas from other investors you know or respect.  Even Buffett did this.  Buffett called it “coattail riding.”

Schloss, like Graham and Buffett, recommends a diversified approach if you’re doing cigar butt (deep value) investing.  Have at least 15-20 stocks in your portfolio.  A few investors can do better by being more concentrated.  But most investors will do better over time by using a quantitative, diversified approach.

Schloss tended to have about 100 stocks in his portfolio:

…And my argument was, and I made it to Warren, we can’t project the earnings of these companies, they’re secondary companies, but somewhere along the line some of them will work out.  Now I can’t tell you which ones, so I buy a hundred of them.  Of course, it doesn’t mean you own the same amount of each stock.  If we like a stock we put more money in it.  Positions we are less sure about we put less in… We then buy the stock on the way down and try to sell it on the way up.

Even though Schloss was quite diversified, he still took larger positions in the stocks he liked best and smaller positions in the stocks about which he was less sure.

Schloss emphasized that it’s important to know what you know and what you don’t know.  Warren Buffett and Charlie Munger call this a circle of competence.  Even if a value investor is far from being the smartest, there are hundreds of microcap companies that are easy to understand with enough work.

(Image by Wilma64)

The main trouble in investing is overconfidence: having more confidence than is warranted by the evidence.  Overconfidence is arguably the most widespread cognitive bias suffered by humans, as Nobel Laureate Daniel Kahneman details in Thinking, Fast and Slow.  By humbly defining your circle of competence, you can limit the impact of overconfidence.  Part of this humility comes from making mistakes.

The best choice for most investors is either an index fund or a quantitative value fund.  It’s the best bet for getting solid long-term returns, while minimizing or removing entirely the negative influence of overconfidence.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  http://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

The Go-Go Years

(Image:  Zen Buddha Silence by Marilyn Barbone.)

September 22, 2019

John Brooks is one of the best business writers of all time.  Business Adventures may be his best book, but Once in GolcondaThe Go-Go Years, and The Games Players are also worth reading.

I wrote about Business Adventures here: http://boolefund.com/business-adventures/

Today’s blog post deals with The Go-Go Years.

Here’s a brief outline:

    • Climax: The Day Henry Ross Perot Lost $450 million
    • Fair Exchange: The Year the Amex Delisted the Old Guard Romans
    • The Last Gatsby: Recessional for Edward M. Gilbert
    • Palmy Days and Low Rumblings: Early Warnings Along Wall Street
    • Northern Exposure: Early Warnings Along Bay Street
    • The Birth of Go-Go: The Rise of a Proper Chinese Bostonian
    • The Conglomerateurs: Corporate Chutzpah and Creative Accounting
    • The Enormous Back Room: Drugs, Fails, and Chaos Among the Clerks
    • Go-Go at High Noon: The View from Trinity Church
    • Confrontation: Steinberg/Leasco vs. Renchard/Chemical Bank

 

CLIMAX

Henry Ross Perot from Dallas, Texas, was one of the top six richest people in America on April 22, 1970.  That day, he suffered a stock market loss of $450 million, still leaving him with a billion dollars worth of stock.  Brooks writes that Perot lost more than the assets of any charitable foundation in the country outside of the top five.  Brooks adds:

It was also quite possibly more in actual purchasing power than any man had ever lost in a single day since the Industrial Revolution brought large private accumulations of money into being.

On May 8, 1970, schools were closed in protest of Vietnam.  One of the antiwar demonstrations by students took place at Wall Street.  This particular demonstration was noticeably nonviolent.  Unfortunately, right before noon, a group of construction workers—carrying construction tools and wearing heavy boots—attacked the student demonstrators.  Fifty (out of a thousand) students needed first-aid treatment, and twenty-three of those were hospitalized.

Brooks explains that workers on Wall Street sided with the students:

Perhaps out of common humanity, or perhaps out of class feeling, the bulls and bears felt more kinship with the doves than with the hawks.  At Exchange Place, Robert A. Bernhard, a partner in the aristocratic firm of Lehman Brothers, was himself assaulted and severely cut in the head by a construction worker’s heavy pliers, after he had tried to protect a youth who was being beaten.  A few blocks north, a young Wall Street lawyer was knocked down, kicked, and beaten when he protested against hardhats who were yelling, ‘Kill the Commie bastards!’

However, many on Wall Street took no part in the struggle.  Brooks continues:

…there is an all too symbolic aspect to professional Wall Street’s role that day as a bystander, sympathizing, unmistakeably, with the underdogs, the unarmed, the peace-lovers, but keeping its hands clean—watching with fascination and horror from its windows…

Brooks asks:

Did it make sense any more to live—and live at the top of the heap—by playing games with paper while children screamed under the window?

Although Perot understood that Wall Street—where a company could be taken public—was the source of his wealth, he nonetheless still believed in the West—and the “frontier”—not the East.  Brooks:

He believed that all things were possible in America for the man of enterprise and that the natural habitat of the man of enterprise was the “frontier.”

Perot graduated from the Naval Academy in 1953, where he was class president.  After four years of active Navy duty, he went to work as a salesman on commission for IBM.

Perot was earning so much money at IBM that the company cut his commissions by 80 percent and they gave him a quota for the year, past which he wouldn’t earn anything.  In 1962, Perot hit his quota on January 19, which made him essentially unemployed for the rest of the year.

Perot’s solution was to start his own company—Electronic Data Systems Corp., designers, installers, and operators of computer systems.  The new company struggled for some time.  Finally in 1965, federal Medicare legislation was passed.  E.D.S. soon had subcontracts to administer Medicare or Medicaid in eleven states.

All told, by 1968 E.D.S. had twenty-three contracts for computer systems, 323 full-time employees, about $10 million in assets, annual net profits of over $1.5 million, and a growth curve so fantastic as to make investment bankers’ mouths water.

By early 1970, having beaten every city slicker he encountered, Perot was worth $1.5 billion.  (This was a few years after E.D.S. went public.)

Perot proceeded to become what Brooks calls a “moral billionaire.”  He pledged to give away nearly all of his fortune to improve people’s lives.  Early on, when he started making charitable donations, he refused to take a tax write-off because he felt he owed tax money to a country that had given him such great opportunities.

Regarding the one-day stock market loss of $450 million, Brooks says:

The way Perot received the news of his monumental setback on April 22 was casual to the point of comedy.

Perot thought, correctly, that the $1.5 billion he had made over eight years wasn’t entirely real because it couldn’t be turned easily into cash.  Moreover, he had plenty of money, including a billion dollars in E.D.S. stock post-crash.  The bottom line, notes Brooks, was that Perot viewed the one-day swing as a non-event.

Brooks writes that E.D.S. was experiencing outstanding financial results at the time.  So the stock swoon wasn’t related to company fundamentals.  Many other stocks had fallen far more on a percentage basis than E.D.S. would fall on April 22, 1970.

At any rate, since the vast majority of stocks had already fallen, whereas E.D.S. stock hadn’t fallen at all, it seemed to make sense that E.D.S. stock would finally experience some downward volatility.  (Brooks notes that University Computing, a stock in E.D.S’s industry, was 80 percent below its peak before E.D.S. even started falling.)  Furthermore, it appeared that there was a bear raid on E.D.S. stock—the stock was vulnerable precisely because it was near its all-time highs, whereas so many other stocks were far lower than their all-time highs.

Brooks concludes:

Nor is it without symbolic importance that the larger market calamity of which E.D.S. was a part resembled in so many respects what had happened forty years before—what wise men had said, for more than a generation, over and over again as if by way of incantation, could never happen again.  It had happened again, as history will; but (as history will) it had happened differently.

 

FAIR EXCHANGE

Brooks tells the stories of two swindlers, Lowell McAfee Birrell and Alexander Guterma.  Brooks writes:

Birrell, like Richard Whitney before him, was apparently a scoundrel as much by choice as by necessity.  The son of a small-town Presbyterian minister, a graduate of Syracuse University and Michigan law school, a handsome, brilliant, and charming man who began his career with the aristocratic Wall Street law firm of Cadwalader, Wickersham and Taft and soon belonged to the Union League and Metropolitan Clubs, Birrell, if he had not been Birrell, might easily have become the modern-day equivalent of a Morgan partner—above the battle and beyond reproach.

Birrell issued himself tons of unauthorized stock in corporations he controlled, and then illegally sold the shares.  The S.E.C. was after Birrell in 1957.  To escape prosecution, Birrell fled to Brazil.

Brooks again:

Guterma was in the mold of the traditional international cheat of spy stories—an elusive man of uncertain national origin whose speech accent sometimes suggested Old Russia, sometimes the Lower East Side of New York, sometimes the American Deep South.

Guterma made his first fortune in the Phillipines during World War II.  He ran a gambling casino that catered to occupying Japanese serviceman.

In 1950, Guterma married an American woman and moved to the United States.  Brooks:

During the succeeding decade he controlled, and systematically looted, more than a dozen substantial American companies…

In September 1959, Guterma was indicted for fraud, stock manipulation, violation of federal banking laws, and failure to register as the agent of a foreign government.

Brooks mentioned Birrell and Guterma as background to a story in 1961 that involved Gerard A. (Jerry) Re and his son, Gerard F. Re.  The Re’s formed the Amex’s largest firm of stock specialists.  (At that time, specialists maintained orderly markets in various stocks.)  One problem was that specialists often have inside information about specific stocks from which they could profit.  Brooks comments:

Pushed in one direction by prudent self-interest, in the other by sense of duty or fear of punishment, a specialist at such times faces a dilemma more appropriate to a hero in Corneille or Racine than to a simple businessman brought up on classic Adam Smith and the comfortable theory of the socially beneficent marketplace.

The S.E.C. finally took notice.  Brooks:

Over a period of at least six years, the S.E.C. charged, the father and son had abused their fiduciary duties in just about every conceivable way, repeating a personal profit of something like $3 million.  They had made special deals with unethical company heads—Lowell Birrell in particular—to distribute unregistered stock to the public in violation of the law.  In order to manipulate the prices of those stocks for their private benefit and that of the executives they were in league with, they had bribed the press, given false tips by word of mouth, paid kickbacks to brokers, generated false public interest by arranging for fictitious trades to be recorded on the tape—the whole, infamous old panoply of sharp stock-jobbing practices.

Ralph S. Saul, the S.E.C.’s young assistant director of the Division of Trading and Exchanges, led the investigation against the Res.  After only two hours of oral arguments, the S.E.C. permanently banned the Res from the securities business.

It turned out that the president of the Amex, Edward T. McCormick, was on the S.E.C.’s list of Re associates.  This implied that the Amex, or at least its chief, knew what was going on all along.

McCormick, who held a master’s degree from the University of California and a PhD from Duke, had started working for the S.E.C. in 1934.  In 1951, he left his post as S.E.C. commissioner to become head of the Amex.  Brooks notes that this sort of talent drain had been the bane of the S.E.C. from its beginnings.  Brooks says:

The scholar and bureaucrat had turned out to be a born salesman.  But with the Amex’s growth, it began to appear toward the end of the decade, a certain laxness of administration had crept in.  Restless at his desk, Ted McCormick was always out selling up-and-coming companies on listing their shares on the Amex, and while he was in Florida or at the Stork Club drumming up trade, sloppy practices were flourishing back at Trinity Place.

Many didn’t notice the Res’ misdeeds.  And it seemed that those who knew didn’t care.  However, a father-and-son-in-law team, David S. Jackson and Andrew Segal, were greatly disturbed.

Jackson had seen McCormick change over the years:

…Jackson had watched McCormick gradually changing from a quiet, reflective man into a wheeler-dealer who loved to be invited by big businessmen to White Sulphur Springs for golf, and the change worried him.  “Ted,” he would say, when they were at dinner at one or the other’s house, “why don’t you read any more?”

“I haven’t got time,” McCormick would reply.

“But you’ll lose your perspective,” Jackson would protest, shaking his head.

Jackson and Segal eventually concluded that McCormick was not fit to be the president of Amex.  Jackson met with McCormick to tell him he should resign.  McCormick reacted violently, picking up a stack of papers and slamming them on to his desk, and then punching a wall of his office.

McCormick told Jackson that he had never done anything dishonest.

“No, I don’t think you have,” Jackson said, his voice shaking.  “But you’ve been indiscreet.”

Roughly a dozen members of Amex, mostly under forty and nicknamed the Young Turks, sided with Jackson and Segal in calling for McCormick’s resignation.  However, they were greatly outnumbered and they were harrassed and threatened.

One Young Turk, for example, was pointedly reminded of a questionable stock transaction in which he had been involved some years earlier, and of how easily the matter could be called to the S.E.C.’s attention; to another it was suggested that certain evidence at hand, if revealed, could make a shambles of his pending suit for divorce; and so on.

Soon Jackson and Segal were practically alone.  Then something strange happened.  The  S.E.C. chose to question Jackson about an incident in which one of Jackson’s assistants, years earlier, had done a bad job of specializing.  The S.E.C. was led by its top investigators, Ralph Saul, David Silver, and Edward Jaegerman.  They questioned Jackson for hours with what seemed to be hostility, scorn, and sarcasm.

Jackson went home and started writing a letter to send to various public officials to complain about his poor treatment by the S.E.C.  Jackson read the letter aloud over the phone to Ralph Saul, who was horrified.  Saul apologized, asked Jackson not to send the letter, and said that amends would be made.

The S.E.C. sent a team to watch the Jackson and Segal operation, trade by trade.  The S.E.C. concluded that Jackson and Segal were honest and asked them to become allies in the reform of the Amex.  Jackson and Segal agreed.

McCormick eventually was forced to resign.

 

THE LAST GATSBY

Brooks tells the story of Edward M. Gilbert:

From the first, he was a bright but lazy student with a particular aptitude for mathematics, a talented and fanatical athlete, and something of a spoiled darling…

Matriculating at Cornell in the early stages of World War II, he made a name for himself in tennis and boxing, won the chess championship of his dormitory, and earned a reputation as a prankster, but went on neglecting his studies.

Gilbert enlisted in the Army Air Force and worked for Army newspapers.  He demonstrated a talent for acquiring foreign languages.

After the war, Gilbert joined his father’s company.

During this period of his business apprenticeship he embarked on a series of personal ventures that were uniformly unsuccessful.  He backed a prizefighter who turned out to be a dud.  He was co-producer of a Broadway play, How Long Till Summer? that starred the black folksinger Josh White’s son… [but the play] got disastrous notices and closed [after a week.]  Gilbert also dabbled in the stock market without any notable success.

Edward Gilbert’s father, Harry Gilbert, became a multi-millionaire when his company, Empire Millwork, sold stock to the public.  Brooks:

He was ever ready to use his money to indulge his son, and over the years he would do so again and again… Never a corporate rainmaker, Harry Gilbert, humanly enough, yearned to appear vital, enterprising, and interesting to his friends and colleagues.  The son’s deals and the electric office atmosphere they created were made possible by the father’s money.  Doubtless the father on occasion did not even understand the intricate transactions his son was forever proposing—debentures and takeovers and the like.  But to admit it would be to lose face… And so, again and again, he put up the money.  Harry Gilbert bought commercial glamour from his son.

Brooks explains that, in 1948, Eddie Gilbert began dreaming of enlarging Empire Millwork through mergers.  In 1951, he asked his father for a directorship.  But Harry Gilbert turned him down.  So Eddie quit and entered the hardwood-floor business on his own.

There were two versions of what happened next.  In one version, Eddie Gilbert was successful and Empire bought him out in 1962.  In the other version, Eddie tried and failed to corner the hardwood-floor market, and Harry bought him out to bury the big mistake.  Brooks writes:

At any rate, in 1955 Eddie returned to Empire with new power and freedom to act.

Eddie wanted to buy E. L. Bruce and Company, the country’s leading hardwood-floor company.

With net sales of around $25 million a year, Bruce was considerably larger than Empire, but it was a staid firm, conservatively managed and in languid family control, of the sort that is the classic prey for an ambitious raider.  In 1955, Eddie Gilbert persuaded his father to commit much of his own and the company’s resources in an attempt to take over Bruce.

Now Eddie came into his own at last.  He began to make important friends in Wall Street—brokers impressed with his dash and daring, and delighted to have the considerable commissions he generated.  Some of his friends came from the highest and most rarified levels of finance.

Brooks continues:

In his early thirties, a short, compact man with pale blue eyes and a sort of ferret face under thinning hair, Gilbert had a direct personal charm that compensated for his vanity and extreme competitiveness.  Sometimes his newfound friends patronized him behind his back, laughing at his social pretensions and his love of ostentation, but they continued going to his parties and, above all, following his market tips.  Some accused him of being a habitual liar; they forgave him because he seemed genuinely to believe his lies, especially those about himself and his past.  He was a compulsive gambler—but, endearingly, a very bad one; on lucky streaks he would double bets until he lost all his winnings, or draw to inside straights for huge sums at poker, or go for broke on losing streaks; yet at all times he seemed to take large losses in the best of humor.

Eddie urged his new friends as well as his family to buy Bruce stock, which was selling around $25 a share.

All that spring, the Gilberts and their relatives and Eddie’s friends accumulated the stock, until in June it had reached the seventies and was bouncing up and down from day to day and hour to hour in an alarming way.  What was in the process of developing in Bruce stock was the classically dangerous, sometimes disastrous situation called a corner.

Bruce family management had realized that a raid was developing, so they were buying as much stock as they could.  At the same time, speculators began shorting the stock on the belief that the stock price would fall.  Shorting involved borrowing shares and selling them, and later buying them back.  The short sellers would profit if they bought it back at a price lower than where they sold it.  However, they would lose money if they bought it back at a price higher than where they sold it.

The problem for short sellers was that Eddie’s friends and family, and Bruce family management, ended up owning all available shares of stock.  Brooks:

The short sellers were squeezed; if called upon to deliver the stock they had borrowed and then sold, they could not do so, and those who owned it were in a position to force them to buy back what they owed at a highly inflated price.

Short sellers bought what little stock was available, sending the price up to 188.

Eddie Gilbert, coming out of the fray in the fall of 1958, seemed to have arrived at last—apparently paper-rich from his huge holdings of high-priced Bruce stock, rich in the esteem of his society backers, nationally famous from the publicity attendant on the corner he had brought about.

Gilbert was self-indulgent with his new wealth, for instance, keeping a regular Monday box at the Metropolitan Opera.  Brooks:

He acquired a huge Fifth Avenue apartment and, when and as he could, filled it with French antiques, a fortune in generally almost-first-rate paintings, and a staff of six.  Sometimes he lived in a mansion at Palm Beach, epitome of Real Society in faded turn-of-the-century photographs.  He took an immense villa at Cap Martin on the French Riviera, where he mingled when he could with Maria Callas and Aristotle Onassis and their like, and gave huge outdoor parties with an orchestra playing beside an Olympic-size swimming pool.

Brooks explains that Gilbert was not genuinely rich:

His paper profits were built on borrowing, and he was always mortgaged right up to the hilt; to be thus mortgaged, and to remain so, was all but an article of faith with him… He was habitually so pressed for cash that on each January first he would draw his entire $50,000 empire salary for the coming year in a lump sum in advance.  By the summer of 1960 he was in bad financial trouble.  Empire National stock was down, Gilbert’s brokers were calling for additional margin, and Gilbert was already in debt all over New York.  He owed large sums to dozens of art dealers… But he hung on gamely; when friends advised him at least to liquidate the art collection, he refused.  To sell it, he explained, would be to lose face.

However, Gilbert was saved when Bruce stock increased sharply.  This led Gilbert to want to have Bruce acquire Celotex Corporation, a large manufacturer of building-insulation materials.  Gilbert acquired as much Celotex stock as he could.  He put his friends and family into Celotex.  Even his old enemies the Bruce family authorized Gilbert’s use of $1.4 million of the company’s money to buy Celotex shares.

But then Gilbert’s fortunes reversed again.  The stock market started to go sour.  Moreover, Gilbert’s marriage was on the rocks.  Gilbert moved to Las Vegas in order to stay there the 6-week period required for a Nevada divorce.

Gilbert kept his residence in Las Vegas as much of a secret as he could.  The few people from Bruce who were allowed to know where Gilbert was were sworn to secrecy.

While in Vegas, Gilbert would be up at dawn, since the markets opened at 7:00 A.M. Nevada time.  In the afternoons, Gilbert went to the casinos to gamble.  He later admitted that his gambling losses were heavy.

Meanwhile, the stock market continued to decline.  Eddie Gilbert was in trouble.  Most of Eddie’s friends—who held Celotex on margin—were also in trouble.

Gilbert himself had all but exhausted his borrowing power.  His debts to brokers, to friends, to Swiss bankers, to New York loan sharks on the fringes of the underworld, all loomed over him, and the market betrayed him daily by dropping even more.

Brooks writes:

The third week of May became for Gilbert a nightmare of thwarted pleas by telephone—pleas to lenders for new loans, pleas to brokers to be patient and not sell him out, pleas with friends to stick with him just a little longer.  But it was all in vain, and in desperation that same week Gilbert took the old, familiar, bad-gambler’s last bad gamble—to avoid the certainty of bankruptcy he risked the possibility of criminal charges.  Gilbert ordered an official of Bruce to make out checks drawn on the Bruce treasury to a couple of companies called Rhodes Enterprises and Empire Hardwood Flooring, which were actually dummies for Gilbert himself, and he used the proceeds to shore up his personal margin calls.  The checks amounted to not quite $2 million; the act amounted to grand larceny.

Gilbert hoped that the prices of Bruce and Celotex would rise, allowing him to repay Bruce for the improper loan.  But Gilbert had a premonition that the stock prices of Bruce and Celotex were about to tumble more.  Gilbert later told The New York Times:

“I suddenly knew that I couldn’t get through this without getting hurt and getting innocent people hurt.”

Gilbert was right, as the prices of Bruce and Celotex collapsed on what turned out to be Blue Monday, the Stock Exchange’s second worse day of the century thus far.  Bruce fell to 23, down 9 3/8, while Celotex fell to 25, down 6.  In total, Gilbert lost $5 million on Blue Monday.  Furthermore, many of Gilbert’s friends who’d followed his advice also had huge losses.

Gilbert realized that if he could find a block buyer for his Celotex shares, that might allow him to repay loans, especially the improper loan from Bruce.  But Gilbert was unable to find such a buyer.  So Eddie did the last thing he felt he could—he fled to Brazil, which had no effective extradition treaty with the United States.

Suddenly Gilbert returned to the United States, despite federal and state charges against him that carried penalties adding up to 194 years in prison.  Gilbert’s father had hired Arnold Bauman, a New York criminal lawyer, who had told Gilbert that he could return to the U.S. if he promised to implicate other wrongdoers.  Gilbert never fulfilled these promises, however, and he ended up spending a bit over two years in prison.

Before going to prison, Gilbert was free on bail for four and a half years.  During that time, with more money form his father, Gilbert started and ran a new business, the Northerlin Company, flooring brokers.  He was successful for a time, allowing him to begin repaying loans.  But again he was too aggressive, and he had to sell the Northerlin Company for a tax loss.

 

PALMY DAYS AND LOW RUMBLINGS

Brooks explains how William Lucius Cary came to be appointed as chairman of the S.E.C.:

A strong Report on Regulatory Agencies to the President Elect, commissioned by the President-elect himself and written late in 1960 by James M. Landis, who had been an S.E.C. chairman in New Deal days, showed that Kennedy was bent on bringing the S.E.C. back to life, and it set the stage for the Cary regime.  Landis called for more funds as well as greater regulatory zeal, and Kennedy and Congress implemented the Landis conclusions with practical backing; between 1960 and 1964, the S.E.C.’s annual appropriation increased from $9.5 million to almost $14 million and its payroll from fewer than one thousand persons to almost fifteen hundred.  But the change was not only quantitative.  Cary concentrated on recruiting talented and enthusiastic lawyers, devoting perhaps a third of his time to the task.  His base supply naturally enough consisted of his former students and their friends; the atmosphere… soon changed from one of bureaucratic somnolence to one of academic liberal activism.

Brooks gives background on Cary:

Cary in 1962 was a lawyer of fifty-one with the gentlemanly manner and the pixyish countenance of a New England professor.  A late-starting family man, he had two children who were still tots; his wife, Katherine, was a great-great-granddaughter of America’s first world-famous novelist, James Fenimore Cooper.  His reputation among his colleagues of the bar was, as one of them put it, for “sweetness of temperament combined with fundamental toughness of fibre.”…He had grown up in and around Columbus, the son of a lawyer and president of a small utility company; he had graduated from Yale and then from Yale Law, practiced law a couple of years in Cleveland, then done a long stretch in federal government—first as a young S.E.C. assistant counsel, later as an assistant attorney general in the tax division of the Justice Department, then as an Office of Strategic Services cloak-and-dagger functionary in wartime Roumania and Yugoslavia.  In 1947 he had entered academic life, teaching law thereafter, first at Northwestern and later at Columbia.  He was in the latter post, taking one day a week off to go downtown to the “real world” of Wall Street and practice law with the firm of Patterson, Belknap and Webb, when John F. Kennedy appointed him S.E.C. chairman soon after assuming the Presidency in January 1961.

Brooks then states:

Two actions during his first year in office gave the financial district an inkling of Cary’s mettle and the S.E.C.’s new mood.

The first case was In the Matter of Cady, Roberts and Co., which related to events that occurred two years before.  A young broker of Cady, Roberts and Co., Robert M. Gintel, had received information that Curtiss-Wright Corporation was about to seriously cut its quarterly dividend.  Gintel immediately sold 7,000 shares for his firm’s customers.  This violated Rule 10B-5 of the S.E.C. against trading based on privileged information.  Gintel was suspended from trading for twenty days.  It seemed like a light sentence.

But so firmly entrenched was the Wall Street tradition of taking unfair advantage of the larger investing public, and so lax the S.E.C.’s administration of that particular part of the law between 1942 and 1961, that not a single stockbroker had ever been prosecuted for improper use of privileged information during those two decades.

The second action led by Cary involved a two-year Special Study of the securities markets.  The study was released in three parts.

Specifically, the first installment said that insider-trading rules should be tightened; standards of character and competence for stockbrokers should be raised; further curbs should be put on the new-issues market; and S.E.C. surveillance should be extended to the thousands of small-company stocks traded over the counter that had previously been free of federal regulation…

The second part of the study… concentrated on stock-exchange operations, recommending that brokers’ commissions on trades be lowered, that the freedom of action of specialists be drastically curtailed, and that floor traders—those exchange members who play the market with their own money on the floor itself, deriving from their membership the unique advantages over nonmembers of being at the scene of action and of paying no commissions to brokers—be legislated right out of existence through the interdiction of their activities.

Brooks continues:

The third and final part… was probably the harshest of the three—and in view of political realities the most quixotic.  Turning its attention to the wildly growing mutual-fund business, the S.E.C. now recommended outlawing of the kind of contract, called “front-end load,” under which mutual-fund buyers agreed (and still agree) to pay large sales commissions off the top of their investment.  It also accused the New York Stock Exchange of leaning toward “tenderness rather than severity” in disciplining those of its members who have broken its rules.

Brooks comments:

All in all, the Special Study was a blueprint for a fair and orderly securities market, certainly the most comprehensive such blueprint ever drawn up, and if all of its recommendations had been promptly put into effect, what follows in this chronicle’s later chapters would be a different tale.  But, of course, they were not.

Brooks explains:

The law that was finally passed—the Securities Acts Amendments of 1964—had two main sections, one extending S.E.C. jurisdiction to include some twenty-five hundred over-the-counter stocks (about as many as were traded on the New York and American exchanges combined), and the other giving the government authority to set standards and qualifications for securities firms and their employees.

As far as it went, it was a good law, a landmark law, a signal achievement for Cary and his egghead crew.  But it fell far short of what the Special Study had asked for.  Not a word, for example, about mutual-fund abuses; no new restrictions on the activities of specialists; and nothing to alter the Stock Exchange’s habit of “tenderness” toward its erring members.  Those items had been edited out in the course of the political compromises that had made passage of the bill possible.

The “bitterest pill of all,” writes Brooks, was that the floor traders continued to be allowed to trade for their own accounts using privileged or inside information.  The Special Study had asked that such trading be outlawed.  But there were very strong objections from the Stock Exchange and then from business in general.  Their arguments referred to the freedom of the marketplace and also the welfare of the investing public.  The Stock Exchange commissioned the management firm of Cresap, McCormick and Paget to study the issue and determine if floor trading served the public or not.

Brooks observes:

Built into this situation was one of those moral absurdities that are so dismayingly common in American business life.  The Stock Exchange, largely run by floor traders and their allies, had a vested interest in finding that floor traders serve a socially useful purpose.  Cresap, McCormick and Paget, being on the Exchange payroll, had a vested interest in pleasing the Exchange…

Cresap, McCormick and Paget labored mightily.  One may imagine the Exchange’s gratification when the report, finished at last, concluded that abolition of floor trading would decrease liquidity and thereby introduce a dangerous new volatility into Stock Exchange trading, doing “irreparable farm” to the free and fair operation of the auction market.  But perhaps the Exchange’s gratification was less than complete.  The magisterial authority of the report was somewhat sullied when James Dowd, head of the Cresap team that had compiled it, stated publicly that his actual finding had been that floor trading was far from an unmixed blessing for the public, and accused the Stock Exchange of having tampered with the report before publishing it… Cary wanted to hold S.E.C. hearings on the matter, but was voted down by his fellow commissioners.

At all events, the report as finally published did not seem to be a triumph of logical thought.

Brooks concludes:

Thus frustrated, Cary’s S.E.C. came to achieve through administration much of what it had failed to achieve through legislation.

In August 1964, the S.E.C. issued strict new rules requiring Stock Exchange members to pass an exam before being permitted to be floor traders.  As well, each floor trader had to submit daily a detailed report of his or her transactions.

Shortly after imposition of the new rules, the number of floor traders on the Stock Exchange dropped from three hundred to thirty.  As an important factor in the market, floor trading was finished.  Cary had won through indirection.

 

NORTHERN EXPOSURE

There were hardly any blacks or women on Wall Street in the 1960’s.  Brooks:

Emancipated, highly competent and successful women in other fields—the arts, publishing, real estate, retail trade—still found it consistent with their self-esteem to affect a coy bewilderment when conversation turned to the stock market or the intricacies of finance.

Brooks continues:

Liberal Democrats, many of them Jewish, were about as common as conservative Republicans in the positions of power; now, one of them, Howard Stein of Dreyfus Corporation, would be the chief fund-raiser for Eugene McCarthy’s 1968 presidential campaign…

Many of the men putting together the stock market’s new darlings, the conglomerates, were liberals—and, of course, it didn’t hurt a Wall Street analyst or salesman to be on close and sympathetic terms with such men.  There were even former Communists high in the financial game.

Between 1930 and 1951, very few young people went to work on Wall Street.  Brooks writes:

Indeed, by 1969, half of Wall Street’s salesmen and analysts would be persons who had come into the business since 1962, and consequently had never seen a bad market break.  Probably the prototypical portfolio hotshot of 1968 entered Wall Street precisely in 1965… Portfolio management had the appeal of sports—that one cleanly wins or loses, the results are measurable in numbers; if one’s portfolio was up 30 or 50 percent for a given year one was a certified winner, so recognized and so compensated regardless of whether he was popular with his colleagues or had come from the right ancestry or the right side of the tracks.

Brooks describes:

It was open season now on Anglo-Saxon Protestants even when they stayed plausibly close to the straight and narrow.  Their sins, or alleged sins, which had once been so sedulously covered up by press and even government, were now good politics for their opponents.  They had become useful as scapegoats—as was perhaps shown in the poignant personal tragedy of Thomas S. Lamont.  Son of Thomas W. Lamont, the Morgan partner who may well have been the most powerful man in the nation in the nineteen twenties, “Tommy” Lamont was an amiable, easygoing man.  He was a high officer of the Morgan Guaranty Trust Company and a director of Texas Gulf Sulphur Company, and on the morning—April 16, 1964—when Texas Gulf publicly announced its great Timmins ore strike, he notified one of his banking colleagues of the good news at a moment when, although he had reason to believe that it was public knowledge, by the S.E.C.’s lights in fact it was not.  The colleague acted quickly and forcefully on Lamont’s tip, on behalf of some of the bank’s clients; then, almost two  hours later, when news of the mine was unquestionably public, Lamont bought Texas Gulf stock for himself and his family.

Lamont had known for several days earlier, and had done nothing.  And when he informed his colleague about the Timmins ore strike, he believed that the information was already public knowledge.  According to the S.E.C., however, the insider trading rule also required one to wait “a reasonable amount of time,” so that the news could be digested.  Brooks:

In so doing, it lumped [Lamont] with flagrant violators, some Texas Gulf geologists and executives who had bought stock on the strength of their knowledge of Timmins days and months earlier, and who made up the bulk of the S.E.C.’s landmark insider case of 1966.

Could it be, then, that the S.E.C. knew well enough that it had a weak case against Lamont, and dragged him into the suit purely for the publicity value of his name?  The outlandishness of the charge against him, and the frequency with which his name appeared in newspaper headlines about the case, suggest such a conclusion.

In the end, all charges against Lamont were dropped, while virtually no charges against the other defendants were dropped.  Unfortunately, before this happened, Lamont’s health had declined and he had passed away.

Brooks continues:

The Texas Gulf ore strike at Timmins in early 1964 had dramatically shown Canada to United States investors as the new Golconda.  Here was a great, undeveloped land with rich veins of dear metals lying almost untouched under its often-frozen soil; with stocks in companies that might soon be worth millions selling for nickels or dimes on Bay Street, the Wall Street of Toronto; and with no inconvenient Securities and Exchange Commission on hand to monitor the impulsiveness of promoters or cool the enthusiasm of investors.  American money flowed to Bay Street in a torrent in 1964 and early in 1965, sending trading volume there to record heights and severely overtaxing the facilities of the Toronto Stock Exchange.  Copies of The Northern Miner, authoritative gossip sheet of the Canadian mining industry, vanished from south-of-the-border newsstands within minutes of their arrival; some Wall Street brokers, unwilling to wait for their copies, had correspondents in Toronto telephone them the Miner‘s juicier items the moment it was off the press.  And why not?  Small fortunes were being made almost every week by quick-acting U.S. investors on new Canadian ore strikes, or even on rumors of strikes.  It was as if the vanished western frontier, with its infinite possibilities both spiritual and material had magically reappeared, with a new orientation ninety degrees to the right of the old one.

The Canadian economy in general was growing fast along with the exploitation of the nation’s mineral resources, and among the Canadian firms that had attracted the favorable attention of U.S. investors, long before 1964, was Atlantic Acceptance Corporation, Ltd., a credit firm, specializing in real-estate and automobile loans, headed by one Campbell Powell Morgan, a former accountant with International Silver Company of Canada, with an affable manner, a vast fund of ambition, and, it would appear later, a marked weakness for shady promoters and a fatal tendency toward compulsive gambling.

In 1955, two years after founding Atlantic, Morgan sought to raise money from Wall Street at a time when some on Wall Street thought they could make profits in Toronto.  Morgan knew Alan T. Christie, another Canadian.  Christie was a partner in “the small but rising Wall Street concern of Lambert and Company.”

At Christie’s recommendation, Lambert and Company in 1954 put $300,000 into Atlantic Acceptance, thereby becoming Atlantic’s principal U.S. investor and chief booster in Wall Street and other points south.

Brooks again:

The years passed and Atlantic seemed to do well, its annual profits steadily mounting along with its volume of loans.  Naturally, it constantly needed new money to finance its continuing expansion.  Lambert and Company undertook to find the money in the coffers of U.S. investing institutions; and Jean Lambert, backed by Christie, had just the air of European elegance and respectability, spiced with a dash of mystery, to make him perfectly adapted for the task of impressing the authorities of such institutions.

Christie first contacted Harvey E. Mole, Jr., head of the U.S. Steel and Carnegie Pension Fund.  Brooks:

Christie made the pitch for Steel to invest in Atlantic.  Mole, born in France but out of Lawrenceville and Princeton, was no ramrod-stiff traditional trustee type; rather, he fancied himself, not without reason, as a money manager with a component of dash and daring.  Atlantic Acceptance was just the kind of relatively far-out, yet apparently intrinsically sound, investment that appealed to Mole’s Continental sporting blood.  The Steel fund took a bundle of Atlantic securities, including subordinate notes, convertible preferred stock, and common stock, amounting to nearly $3 million.

The following year, Lambert and Company convinced the Ford Foundation to invest in Atlantic Acceptance.  Brooks comments:

After that, it was easy.  With the kings of U.S. institutional investing taken into camp, the courtiers could be induced to surrender virtually without a fight.  Now Lambert and Company could say to the fund managers, “If this is good enough for U.S. Steel and the Ford Foundation, how can you lose?”  “We were all sheep,” one of them would admit, sheepishly, years later.  Before the promotion was finished, the list of U.S. investors in Atlantic had become a kind of Burke’s Peerage of American investing institutions: the Morgan Guaranty and First National City Banks; the Chesapeake and Ohio Railway; the General Council of Congregational Churches; Pennsylvania and Princeton Universities (perhaps not coincidentally, the man in charge of Princeton’s investment program was Harvey Mole); and Kuhn, Loeb and Company, which, to the delight of Lambert, gave the enterprise its valuable imprimatur by taking over as agent for the sale of Atlantic securities in the United States.  Perhaps the final turn of the screw, as the matter appears in hindsight, is the fact that the list of Atlantic investors eventually included Moody’s Investors Service, whose function is to produce statistics and reports designed specifically to help people avoid investment pitfalls of the sort of which Atlantic would turn out to be an absolutely classic case.

In the early 1960’s, Atlantic’s sales were increasing nearly 100 percent per year.  It seemed that the company was exceeding what anyone could have expected.  Of course, in the loan business, you can increase volumes significantly by making bad loans that are unlikely to be repaid.  Brooks:

In fact, that was precisely what Atlantic was doing, intentionally and systematically.

However, having such investors as the Steel fund, the Ford Foundation, etc., made it easy to dismiss critics.

Late in 1964, Atlantic, hungry for capital as always, sold more stock; and early in 1965, Kuhn, Loeb helped place $8.5 million more in Atlantic long-term debt with U.S. institutional investors.  By this time, Lambert and Company’s stake in Atlantic amounted to $7.5 million.  The firm’s commitment was a do-or-die matter; it would stand or fall with Atlantic.  Moreover, it is now clear that by this time Morgan and his associates were engaged in conducting a systematic fraud on a pattern not wholly dissimilar to that of Ponzi or Ivar Kreuger.  Atlantic would use the new capital flowing from Wall Street to make new loans that its major officers knew to be unsound; the unsoundness would be deliberately camouflaged in the company’s reports, in order to mislead investors; the spurious growth represented by the ever-increasing loans would lure in new investment money, with which further unsound loans would be made; and so on and on.  Morgan had taken to intervening personally each year in the work of his firm’s accountants—some of whom were willing enough to commit fraud at their client’s request—to ensure that a satisfactory rise in profits was shown through overstatement of assets and understatement of allowances for bad debts.  For 1964, it would come out later, Atlantic’s announced $1.4 million profit, under proper accounting procedure, should have been reported as a loss of $16.6 million.

Brooks continues:

The game, like all such games, could not go on forever.  By early 1965, suspicion of Atlantic’s operations was in the wind.  In April, the New York Hanseatic Corporation, a $12-million investor in Atlantic paper, asked the Toronto-Dominion Bank for a credit check on Atlantic.  The response—which in retrospect appears dumbfounding—was favorable.  In fact, if the bank had been able to penetrate the mystifications of Powell’s accountants, it would have discovered that Atlantic was by that time actually insolvent.  For several years, at the instigation of some of the various international schemers for whom Morgan had a fatal affinity, the firm had been increasingly involved in a desperate and doomed plunge in a shaky venture far from home: between 1963 and early 1965 it had committed more than $11 million to the Lucayan Beach, a hotel with a gambling casino attached, on balmy, distant Grand Bahama Island.  A Royal Commission would later describe the investment as “the last throw of the dice to retrieve all the losses created by years of imprudence and impropriety.”  But the Lucayan Beach venture, managed incompetently and fraudulently, did not flourish, and the losses were not to be retrieved.

On June 15, Atlantic went into default.  It needed $25 million to cure the situation.  “Of course, it neither had not could raise such a sum.”  Brooks comments:

The Old Establishment of U.S. investing had fallen for its own fading mystique.  Believing, with tribal faith that can only be called touching, that no member of the club could make a serious mistake, the members had followed each other blindly into the crudest of traps, and had paid the price for their folly.

Brooks concludes: “…the Atlantic episode neatly divides Wall Street’s drama of the decade, ending the first act, and beginning the second and climactic one.”

 

THE BIRTH OF GO-GO

Brooks defines the term “go-go” as a method of investing:

The method was characterized by rapid in-and-out trading of huge blocks of stock, with an eye to large profits taken very quickly, and the term was used specifically to apply to the operation of certain mutual funds, none of which had previously operated in anything like such a free, fast, or lively manner.

The mood and the method seem to have started, of all places, in Boston, the home of the Yankee trustee.  The handling of other people’s money in the United States began in Boston, the nation’s financial center until after the Civil War.  Trusteeship is by its nature conservative—its primary purpose being to conserve capital—and so indeed was the type of man it attracted in Boston.  Exquisitely memorialized in the novels of John P. Marquand, for a century the Boston trustee was the very height of unassailable probity and sobriety: his white hair neatly but not too neatly combed; his blue Yankee eyes untwinkling, at least during business hours; the lines in his cheeks running from his nose to the corners of his mouth forming a reassuringly geometric isoceles triangle; his lips touching liquor only at precisely set times each day, and then in precise therapeutic dosage; his grooming impeccable (his wildest sartorial extravagance a small, neat bow tie) with a single notable exception—that he wore the same battered gray hat through his entire adult life, which, so life-preserving was his curriculum, seldom ended before he was eighty-five or ninety.

Brooks writes about the Boston-born “prudent man rule.”  In 1830, Justice Samuel Putnam of the Supreme Judicial Court of Massachusetts wrote in a famous opinion:

All that can be required of a Trustee to invest is that he conduct himself faithfully and exercise a sound discretion.  He is to observe how men of prudence, discretion, and intelligence manage their own affairs, not in regard to speculation, but in regard to permanent disposition of their funds, considering the probable income, as well as the probable safety of the capital to be invested.

Brooks then writes that Boston, in 1924, was the location of “another epoch-making innovation in American money management, the founding of the first two mutual funds, Massachusetts Investors Trust and State Street Investing Company.”  Later, after World War II, “the go-go cult quietly originated hard by Beacon Hill under the unlikely sponsorship of a Boston Yankee named Edward Crosby Johnson II.”  Brooks describes Johnson:

Although never a trustee by profession, Johnson was almost the Boston-trustee type personified.

Brooks adds:

The market bug first bit him in 1924 when he read a serialization in the old Saturday Evening Post of Edwin Lefevre’s “Reminiscences of a Stock Market Operator,” the story of the career of the famous speculator Jesse Livermore.  “I’ll never forget the thrill,” he told a friend almost a half century later.  “Everything was there, or else implied.  Here was the picture of a world in which it was every man for himself, no favors asked or given.  You were what you were, not because you were a friend of somebody, but for yourself.  And Livermore—what a man, always betting his whole wad!  A sure system for losing, of course, but the point was how much he loved it.  Operating in the market, he was like Drake sitting on the poop of his vessel in a cannonade.  Glorious!”

Eventually Johnson was asked to take over Fidelity Fund, a mutual fund with only $3 million under management.  Brooks comments:

Edward Crosby Johnson II, for all of his trustee-like ways, clearly had a speculative background and temperament; after all, his stock-market idol was one of the master speculators.

What this meant in practice was that Fidelity was willing to trade in and out of stocks, often fairly rapidly, rather than buy and hold.

Then in 1952, Johnson met Gerald Tsai, Jr.  Johnson, liking the young man’s looks, first hired Tsai as a junior stock analyst.  Tsai was born in Shanghai in 1928 to Westernized Chinese parents.  Tsai’s father had been educated at the University of Michigan, and was Shanghai district manager for the Ford Motor Company.  Tsai himself got a BA and MA in economics from Boston University.  Brooks:

“I liked the market,” he would explain years later.  “I felt that being a foreigner I didn’t have a competitive disadvantage there, when I might somewhere else.  If you buy GM at forty and it goes to fifty, whether you are an Oriental, a Korean, or a Buddhist doesn’t make any difference.”

Tsai’s reasons for liking the market were similar to Johnson’s reasons: “you were what you were not because you were a friend of somebody, but for yourself.”  Brooks continues:

At Fidelity, Tsai was not long in making his mark.  Always impeccably groomed, his moon face as impassive as a Buddha, he showed himself to be a shrewd and decisive picker of stocks for shot-term appreciation…

Tsai explained later that Johnson gave you your head—a chance to work on your own rather than as part of a committee—but he simultaneously gave you your rope, saying “God ahead and hang yourself with it.”  Tsai also quoted Johnson as saying, “Do it by yourself.  Two men can’t play a violin.”

Soon Tsai asked Johnson if he could launch a speculative growth fund.  Johnson said yes in the space of an hour, saying, “Go ahead.  Here’s your rope.”

Tsai’s rope was called Fidelity Capital Fund, and it was the company’s first frankly speculative public growth fund.  Right from the start, he operated it in a way that was at the time considered almost out-and-out gambling.  He concentrated Fidelity Capital’s money in a few stock that were then thought to be outrageously speculative and unseasoned for a mutual fund (Polaroid, Xerox, and Litton Industries among them).

Brooks notes:

As once “Jesse Livermore is buying it!” had been the signal for a general stampede into any stock, so now it was “Gerry Tsai is buying it!”  Like Livermore’s, his prophecies by force of his reputation came to be to a certain extent self-fulfilling.

Brooks also writes about the invention of the “hedge fund,” so named because, unlike a mutual fund, a hedge fund could operate on margin and make short sales.  Brooks describes the man who invented the hedge fund:

He was Alfred Winslow Jones, no sideburned gunslinger but a rather shy, scholarly journalist trained in sociology and devoted to good works.  Born in Australia at the turn of the century to American parents posted there by General Electric, he graduated from Harvard in 1923, got a Ph.D. in sociology from Columbia, served in the foreign service in Berlin during the thirties, and became a writer for Time-Life in the forties.

In 1949, Winslow got the idea for a hedge fund.  He raised $100,000 in investment capital, $40,000 of it his.  The first hedge fund did well, even in the bad market of 1962 because of its capacity to sell short.  Its clients were mainly writers, teachers, scholars, social workers.  (One client was Sam Stayman, the bridge expert.)  Winslow’s fund showed a five-year gain of 325 percent and a ten-year gain of more than twice that amount.  The fund took 20 percent of profits as an annual fee.

Brooks adds:

Alfred Jones, in his own middle sixties, had made so much money out of A. W. Jones and Company’s annual 20 percents that he could well afford to indulge his predilections.  Spending less and less time at his office on Broad Street, he devoted himself more and more to a personal dream of ending all poverty.  Considering material deprivation in the land of affluence to be a national disgrace, he set up a personal foundation devoted to mobilizing available social skills against it… Jones could afford to go the way of the aristocrat, treating money-making as something too simple to be taken very seriously, and putting his most profound efforts into work not in the cause of profit but in that of humanity.

In late 1965, Tsai left Fidelity and formed his own mutual fund, the Manhattan Fund.  Tsai set out to raise $25 million, but he ended up raising $247 million.  The only problem for Tsai was that the bull market had peaked.  Investors expected Tsai to make 50 percent a year, but he could only do so if the bull market continued.  Brooks comments:

But if Tsai no longer seemed to know when to cash in the investments he made for others, he knew when to cash in his own.  In August, 1968, he sold Tsai Management and Research to C.N.A. Financial Corporation, an insurance holding company, in exchange for a high executive post with C.N.A. and C.N.A. stock worth in the neighborhood of $30 million.

 

THE CONGLOMERATEURS

Brooks defines the term:

Derived from the Latin word glomus, meaning wax, the word suggests a sort of apotheosis of the old Madison Avenue cliche “a big ball of wax,” and is no doubt apt enough; but right from the start, the heads of conglomerate companies objected to it.  Each of them felt that his company was a mesh of corporate and managerial genius in which diverse lines of endeavor—producing, say, ice cream, cement, and flagpoles—were subtly welded together by some abstruse metaphysical principle so refined as to be invisible to the vulgar eye.  Other diversified companies, each such genius acknowledged, were conglomerates; but not his own.

In 1968, 26 of the country’s 500 biggest companies disappeared through conglomerate merger.  Some of the largest targets were acquired by companies much smaller than themselves.  Moreover, enthusiasts were saying that eventually 200 super-conglomerates would be doing most of the national business.  There would only be a handful of non-conglomerates left.  Brooks observes:

The movement was new and yet old.  In the nineteenth century, few companies diversified their activities very widely by acquiring other companies or by any other means.  There is, on the face of it, no basic reason for believing that a man who can successfully run an ice cream business should not be able to successfully run an ice-cream-and-cement business, or even an ice-cream-cement-and-flagpole business.  On the other hand, there is no reason for believing that he should be able to do so.  In the Puritan and craft ethic that for the most part ruled nineteenth-century America, one of the cardinal precepts was that the shoemaker should stick to his last.

Brooks notes that it was during the 1950’s that “really uninhibited diversification first appeared.  Brooks:

During that decade, National Power and Light, as a result of its purchase of another company, found itself chiefly engaged in peddling soft drinks; Borg-Warner, formerly a maker of automotive parts, got into refrigerators, other consumer products; and companies like Penn-Texas and Merritt Chapman and Scott, under the leadership of corporate wild men like David Carr and Louis E. Wolfson, took to ingesting whatever companies swam within reach.  Among the first companies to be called conglomerates were Litton, which in 1958 began to augment its established electronics business with office calculators and computers and later branched out into typewriters, cash registers, packaged foods, conveyor belts, oceangoing ships, solder, teaching aids, and aircraft guidance systems, and Textron, once a placid and single-minded New England textile company, and eventually a purveyor of zippers, pens, snowmobiles, eyeglass frames, silverware, golf carts, metalwork machinery, helicopters, rocket engines, ball bearings, and gas meters.

Brooks lists the factors involved in the conglomerate explosion:

    • corporate affluence
    • a decline of the stick-to-your-last philosophy among businessmen
    • a decline of the stick-to-anything philosophy among almost everyone else
    • a rise in the influence of graduate business schools, led by Harvard, which in the 1960’s were trying to enshrine business as a profession, and often taught that managerial ability was an absolute quality, not limited by the type of business being managed
    • federal antitrust laws, which forbade most mergers between large companies in the same line of business

One additional factor was that many investors focused on just one metric: the price-to-earnings (P/E) ratio.  Brooks clarifies why this isn’t a reliable guide when investing in a conglomerate: when a company with a high P/E buys a company with a low P/E, earnings per share increases.  Brooks:

There is an apparent growth in earnings that is entirely an optical illusion.  Moreover, under accounting procedures of the late nineteen sixties, a merger could generally be recorded in either of two ways—as a purchase of one company by another, or as a simple pooling of the combined resources.  In many cases, the current earnings of the combined company came out quite differently under the two methods, and it was understandable that the company’s accountants were inclined to choose arbitrarily the method that gave the more cheerful result.  Indeed, the accountant, through this choice and others as his disposal, was often able to write for the surviving company practically any current earnings figure he chose—a situation that impelled one leading investment-advisory service to issue a derisive bulletin entitled, “Accounting as a Creative Art.”

Brooks continues:

The conglomerate game tended to become a form of pyramiding… The accountant evaluating the results of a conglomerate merger would apply his creative resources by writing an earnings figure that looked good to investors; they, reacting to the artistry, would buy the company’s stock, thereby forcing its market price up to a high multiple again; the company would then make the new merger, write new higher earnings, and so on.

 

THE ENORMOUS BACKROOM

Brooks writes:

Nineteen sixty-eight was to be the year when speculation spread like a prairie fire—when the nation, sick and disgusted with itself, seemed to try to drown its guilt in a frenetic quest for quick and easy money.  “The great garbage market,” Richard Jenrette called it—a market in which the “leaders” were neither old blue chips like General Motors and American Telephone nor newer solid starts like Polaroid and Xerox, but stock with names like Four Seasons Nursing Centers, Kentucky Fried Chicken, United Convalescent Homes, and Applied Logic.  The fad, as in 1961, was for taking short, profitable rides on hot new issues.

As trading volume increased, back-office troubles erupted.  Brooks:

The main barometric measuring-device for the seriousness of back-office trouble was the amount of what Wall Street calls “fails.”  A fail, which might more bluntly be called a default, occurs when on the normal settlement date for any stock trade–five days after the transaction–the seller’s broker for some reason does not physically deliver the actual sold stock certificates to the buyer’s broker, or the buyer’s broker for some reason fails to receive it.

The reasons for fails typically are that either the selling broker can’t find the certificates being sold, the buying broker misplaces them, or one side or the other makes a mistake in record-keeping by saying that the stock certificates have not been delivered when in fact they have been.

Lehman Brothers, in particular, was experiencing a high level of fails.

Stock discrepancies at the firm, by the end of May, ran into hundreds of millions of dollars.  Lehman reacted by eliminating a few accounts, ceasing the make markets in over-the-counter stocks, and refusing further orders for low-priced securities; it did not augment these comparatively mild measures with drastic ones—the institution of a crash program costing half a million dollars to eliminate stock record errors—until August, when the S.E.C. threatened to suspend Lehman’s registration as a broker-dealer and thus effectively put it out of business.  Lehman’s reluctance to act promptly to save its customers’ skins, and ultimately its own, was all too characteristic of Wall Street’s attitude toward its troubles in 1968.

Brooks comments:

Where were the counsels of restraint, not to say common sense, in both Washington and on Wall Street?  The answer seems to lie in the conclusion that in America, with its deeply imprinted business ethic, no inherent stabilizer, moral or practical, is sufficiently strong in and of itself to support the turning away of new business when competitors are taking it on.  As a people, we would rather face chaos making potsfull of short-term money than maintain long-term order and sanity by profiting less.

 

GO-GO AT HIGH NOON

Brooks on New York City in 1968:

Almost all of the great cultural centers of history have first been financial centers.  This generalization, for which New York City provides a classic example, is one to be used for purposes of point-proving only with the greatest caution.  To conclude from it that financial centers naturally engender culture would be to fall into the most celebrated of logical fallacies.  It is nonetheless a suggestive fact, and particularly so in the light of 1968 Wall Street, standing as it was on the toe of the same rock that supported Broadway, off-Broadway, Lincoln Center, the Metropolitan Museum, the Museum of Modern Art, and Greenwich Village.

Brooks then writes about changes on Wall Street:

Begin with the old social edifices that survived more or less intact.  In many instances they were Wall Street’s worst and most dispensable; for example, its long-held prejudices, mitigated only by tokenism, against women and blacks.

A few women—but not many—were reaching important positions.  Meanwhile, for blacks, Wall Street “had advanced the miniscule distance from the no-tokenism of 1965 to tokenism at the end of the decade.”

 

CONFRONTATION

Brooks writes:

Spring of 1969—a time that now seems in some ways part of another, and a more romantic, era—was in the business world a time of Davids and Goliaths: of threatened takeovers of venerable Pan American World Airways by upstart Resorts International, for example, and of venerable Goodrich Tire and Rubber by upstart Northwest Industries… Undoubtedly, though, the David-and-Goliath act of early 1969 that most caught the popular imagination was an attempt upon the century-and-a-half-old Chemical Bank New York Trust Company (assets a grand $9 billion) by the eight-year-old Leasco Data Processing Equipment Corporation of Great Neck, Long Island (assets a mere $400 million), a company entirely unknown to almost everyone in the larger business community without a special interest in either computer leasing, Leasco’s principal business until 1968, or in the securities market, in which its stock was a star performer.  In that takeover contest, the roles of Goliath and David were played, with exceptional spirit, by William Shryock Renchard of the Chemical and Saul Phillip Steinberg of Leasco.

Renchard was from Trenton, New Jersey, and although he attended Princeton, he probably was not expected to amount to much.  That is, he didn’t stand out at all at Princeton and his senior yearbook said, “Renchard is undecided as to his future occupation.”

By 1946, at the age of thirty-eight, Renchard was a vice president of Chemical Bank and Trust Company.  In 1955, he became executive vice president; in 1960, he was made president; and in 1966, he was made chairman of the board of what was now called Chemical Bank New York Trust Company.  By that time, the bank had $9 billion in assets and was the country’s six largest commercial bank.

Saul Phillip Steinberg was from Brooklyn and was a full generation younger than Renchard.  Steinberg was unexceptional, although he did develop an early habit of reading The Wall Street Journal.  He attended the Wharton School of Finance and Commerce at the University of Pennsylvania.

Steinberg researched I.B.M., expecting to find negative things.  Instead, he learned that I.B.M. was a brilliantly run company.  He also concluded that I.B.M.’s industrial computers would last longer than was assumed.  So he started a business whereby he purchased I.B.M. computers and then leased them out for longer terms and for lower rates than I.B.M. itself was offering.

In 1964, two years after launching his business—Ideal Leasing Company—earnings were $255,000 and revenues were $8 million.  Steinberg then decided to go public, and the company’s name was changed to Leasco Data Processing Equipment Corporation.  Public sale of Leasco stock brought in $750,000.  Leasco’s profits skyrocketed: 1967 profits were more than eight times 1966 profits.  Brooks:

As might be expected of a young company with ambition, a voracious need for cash, and a high price-to-earnings multiple, Leasco became acquisition-minded… In 1966 and 1967, Leasco increased its corporate muscle by buying several small companies in fields more or less related to computers or to leasing… These acquisitions left the company with $74 million in assets, more than eight hundred employees, larger new headquarters in Great Neck, Long Island, and a vast appetite for further growth through mergers.

Diversified companies learned that if they acquired or merged with a fire-and-casualty company, then the otherwise restricted cash reserves—”redundant capital”—of the fire-and-casualty company could be put to use.  Thus, Leasco got the idea of acquiring Reliance Insurance Company, a Philadelphia-based fire-and-casualty underwriter “with more than five thousand employees, almost $350 million in annual revenues, and a fund of more than $100 million in redundant capital.”  Brooks writes:

Truly—to change the metaphor—it was a case of the minnow swallowing the whale; Reliance was nearly ten times Leasco’s size, and Leasco, as the surviving company, found itself suddenly more than 80 percent in the insurance business and less than 20 percent in the computer-leasing business.

Brooks adds:

[Leasco] suddenly had assets of $400 million instead of $74 million, net annual income of $27 million instead of $1.4 million, and 8,500 employees doing business in fifty countries instead of 800 doing business in only one.

Brooks notes that Leasco’s stock had, over the previous five years, increased 5,410 percent making Leasco “the undisputed king of all the go-go stocks.”  Now comes the story of Leasco and Chemical Bank.

Leasco had gotten interested in acquiring a bank.  Banks often sold at low price-to-earnings ratio’s, giving Leasco leverage in a takeover.  Also, Steinberg thought “that it would be advantageous to anchor Leasco’s diversified financial services to a New York money-center bank with international connections.”  By the fall of 1968, Leasco was zeroing in on Renchard’s $9-billion Chemical Bank.

Leasco had begun buying shares in Chemical and had prepared a hypothetical tender offer involving warrants and convertible debentures when Chemical learned of Leasco’s intended takeover.  Brooks:

…Renchard was in no doubt as to Chemical’s response.  He and his bank were going to fight Leasco with all their strength.  True enough, a merger, as in the Reliance case, would result in immediate financial benefit to the stockholders of both companies.  But it seemed to Renchard and his colleagues that more than immediate stockholder profit was involved.  The century-and-a-half-old Chemical Bank a mere division of an unseasoned upstart called Leasco?

Renchard organized an eleven-man task force to come up with a strategy for fighting off any takeover attempt.  Renchard commented later:

“We were guessing that they would offer stuff with a market value of around $110 for each share of our stock, which was then selling at $72.  So we knew well enough it would be tough going persuading our stockholders not to accept.”

First, Renchard leaked the story to The New York Times.  The Times published a piece that included the following:

Can a Johnny-come-lately on the business scene move in on the Establishment and knock off one of the biggest prizes in sight?

[…]

Is Chemical in the bag?  Hardly.  William S. Renchard, chairman of the Chemical Bank, sounded like a Marine Corps colonel in presenting his battle plan…

One strategy Chemical came up with was to attack the value of Leasco stock by selling it or shorting it.  This approach was discussed at a February 6 strategy meeting, but no one afterwards was ever willing to admit it.  Brooks:

The striking and undeniable fact is, however, that on that very day, Leasco stock, which had been hovering in the stratosphere at around 140, abruptly began to fall in price on large trading volume.  By the close the following day Leasco was down almost seven points, and over the following three weeks it would drop inexorably below 100.

Chemical planned a full-scale strategy meeting:

At the Chemical strategy meeting—which was attended, this time, not only by Chemical’s in-house task force, but by invitees from other powerful Wall Street institutions sympathetic to the Chemical cause, including First Boston, Kuhn Loeb, and Hornblower Weeks—a whole array of defensive measures were taken up and thrashed out, among them the organizing of telephone teams to contact Chemical stockholders; the retaining of the leading proxy-soliciting firms solely to deny their services to Leasco; and the possibility of getting state and federal legislation introduced through the bankers’ friends in Albany and Washington in order to make a Leasco takeover of Chemical illegal.  Despite the availability of such weapons, the opinion of those present seemed to be that Leasco’s venture had an excellent chance of success.

Finally, Renchard and Steinberg met for lunch.  Brooks writes:

One may imagine the first reactions of the antagonists to each other.  One was lean, iron-gray, of distinctly military bearing; a North Shore estate owner, very conscious of the entrenched power of the nation standing behind him, very much a man of few and incisive words.  The other was round-faced, easy-smiling, a man of many words who looked preposterously younger than his already preposterous twenty-nine years, and given, as he talked, to making windmill gestures with his arms and suddenly jumping galvanically up from his chair; a South Shore estate owner…; a young man bubbling with energy and joy in living.

During the meeting, Steinberg said he wanted it to be a friendly takeover.  Renchard seemed to be open to that possibility.  At the same time, Renchard said that he was “a pretty good gutter fighter,” to which Steinberg replied that his own record as a gutter fighter “was considered to be pretty good, too.”

A second meeting was held.  This time, Renchard and Steinberg brought their chief aides.  Steinberg put more emphasis on his friendly intentions, and he conceded that he would be willing to not be the chief executive of the merged entity.  Renchard said they had lots to consider and would get back in touch shortly.

Then Chemical held another full-scale battle meeting at which they considered several possible options.  They thought about changing their company’s charter to make a Leasco takeover legally difficult if not impossible.  They floated the idea of buying a fire-and-casualty company to create an antitrust conflict with Leasco’s ownership of Reliance.  They even talked about arranging to have a giant insurance company take over Chemical.  Brooks notes:

Probably the most effective of Chemical’s various salvos was on the legislative front… Richard Simmons of the Cravath law firm, on retainer from Chemical, began devoting full time to the Leasco affair, concentrating his attention on the drafting of laws specifically designed to prevent or make difficult the takeover of banks similar to Chemical by companies that resembled Leasco, and getting these drafts introduced as bills in the State Legislature in Albany and the Congress in Washington.

Simmons’ anti-bank-takeover bill was introduced in Albany and was passed.  Moreover, a Wall Street Journal article questioned Leasco’s earnings prospects.  As well, the Department of Justice sent a letter to Leasco raising the possibility that the proposed takeover might violate antitrust laws.  In truth, the proposed takeover did not violate antitrust laws.  How the Justice Department came to send such a letter has never been explained, observes Brooks.

At this point, Steinberg decided to abandon the effort to merge with Chemical.  Brooks quotes Steinberg:

“Nobody was objective… bankers and businessmen I’d never met kept calling up out of the blue and attacking us for merely thinking about taking over a big bank.  Some of the attacks were pretty funny—responsible investment bankers taking as if we were using Mafia tactics.. Months after we’d abandoned our plans, executives of major corporations were still calling up and ranting, ‘I feel it was so wrong, what you tried to do—’  And yet they could never say why… I still don’t know exactly what it was.”

 

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  http://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

Quantitative Microcap Value

(Image:  Zen Buddha Silence by Marilyn Barbone.)

September 8, 2019

Jack Bogle and Warren Buffett correctly maintain that most investors should invest in an S&P 500 index fund.  An index fund will allow you to outpace 90-95% of all active investors—net of costs—over the course of 4-5 decades.  This is purely a function of cost.  Active investors as a group will do the same as the S&P 500, but that is before costs.  After costs, active investors will do about 2.5% worse per year than the index.

An index fund is a wise choice.  But you can do much better if you invest in a quantitative microcap value strategy—focused on undervalued microcap stocks with improving fundamentals.  If you adopt such an approach, you can outperform the S&P 500 by roughly 7% per year.  For details, see: http://boolefund.com/cheap-solid-microcaps-far-outperform-sp-500/

But this can only work if you have the ability to ignore volatility and stay focused on the very long term.

“Investing is simple but not easy.” — Warren Buffett

(Photo by USA International Trade Administration)

Assume the S&P 500 index will return 8% per year over the coming decades.  The average active approach will produce roughly 5.5% per year.  A quantitative microcap approach—cheap micro caps with improving fundamentals—will generate about 15% per year.

What would happen if you invested $50,000 for the next 30 years in one of these approaches?

Investment Strategy Beginning Value Ending Value
Active $50,000 $249,198
S&P 500 Index $50,000 $503,133
Quantitative Microcap $50,000 $3,310,589

As you can see, investing $50,000 in an index fund will produce $503,133, which is more than ten times what you started with.  Furthermore, $503,133 is more than twice $249,198, which would be the result from the average active fund.

However, if you invested $50,000 in a quantitative microcap strategy, you would end up with $3,310,589.  This is more than 66 times what you started with, and it’s more than 6.5 times greater than the result from the index fund.

You could either invest in a quantitative microcap approach or you could invest in an index fund.  You’ll do fine either way.  Or you could invest part of your portfolio in the microcap strategy and part in an index fund.

What’s the catch?

For most of us as investors, our biggest enemy is ourselves.  Let me explain.  Since 1945, there have been 27 corrections where stocks dropped 10% to 20%, and there have been 11 bear markets where stocks dropped more than 20%.  The stock market has always recovered and gone on to new highs.  However, many investors have gotten scared and sold their investments after stocks have dropped 10-20%+.

Edgar Wachenheim, in the great book Common Stocks and Common Sense, gives the following example:

The financial crisis during the fall of 2008 and the winter of 2009 is an extreme (and outlier) example of volatility.  During the six months between the end of August 2008 and end of February 2009, the [S&P] 500 Index fell by 42 percent from 1,282.83 to 735.09.  Yet by early 2011 the S&P 500 had recovered to the 1,280 level, and by August 2014 it had appreciated to the 2000 level.  An investor who purchased the S&P 500 Index on August 31, 2008, and then sold the Index six years later, lived through the worst financial crisis and recession since the Great Depression, but still earned a 56 percent profit on his investment before including dividends—and 69 percent including the dividends… During the six-year period August 2008 through August 2014, the stock market provided an average annual return of 11.1 percent—above the range of normalcy in spite of the abnormal horrors and consequences of the financial crisis and resulting deep recession.

If you can stay the course through a 25% drop and even through a 40%+ drop, and remain focused on the very long term, then you should invest primarily in stocks, whether via an index fund, a quantitative microcap value fund, or some other investment vehicle.

The best way to stay focused on the very long term is simply to ignore the stock market entirely.  All you need to know or believe is:

  • The U. S. and global economies will continue to grow, mainly due to improvements in technology.
  • After every correction or bear market—no matter how severe—the stock market has always recovered and gone on to new highs.

If you’re unable to ignore the stock market, and if you might get scared and sell during a correction or bear market—don’t worry if you’re in this category since many investors are—then you should try to invest a manageable portion of your liquid assets in stocks.  Perhaps investing 50% or 25% of your liquid assets in stocks will allow you to stay the course through the inevitable corrections and bear markets.

The best-performing investors will be those who can invest for the very long term—several decades or more—and who don’t worry about (or even pay any attention to) the inevitable corrections and bear markets along the way.  In fact, Fidelity did a study of its many retail accounts.  It found that the best-performing accounts were owned by investors who literally forgot that they had an account!

  • Note: If you were to buy and hold twenty large-cap stocks chosen at random, your long-term performance would be very close to the S&P 500 Index.  (The Dow Jones Industrial Average is a basket of thirty large-cap stocks.)

Bottom Line

If you’re going to be investing for a few decades or more, and if you can basically ignore the stock market in the meantime, then you should invest fully in stocks.  Your best long-term investment is an index fund, a quantitative microcap value fund, or a combination of the two.

If you can largely ignore volatility, then you should consider investing primarily in a quantitative microcap value fund.  This is very likely to produce far better long-term performance than an S&P 500 index fund.

Many top investors—including Warren Buffett, perhaps the greatest investors of all time—earned the highest returns of their career when they could invest in microcap stocks.  Buffett has said that he’d still be investing in micro caps if he were managing small sums.

To learn more about Buffett getting his highest returns mainly from undervalued microcaps, here’s a link to my favorite blog post: http://boolefund.com/buffetts-best-microcap-cigar-butts/

The Boole Microcap Fund that I manage is a quantitative microcap value fund.  For details on the quantitative investment process, see: http://boolefund.com/why-invest-in-boole-microcap/

Although the S&P 500 index appears rather high—a bear market in the next year or two wouldn’t be a surprise—the positions in the Boole Fund are quite undervalued.  When looking at the next 3 to 5 years, I’ve never been more excited about the prospects of the Boole Fund relative to the S&P 500—regardless of whether the index is up, down, or flat.

(The S&P 500 may be flat for 5 years or even 10 years, but after that, as you move further into the future, eventually there’s more than a 99% chance that the index will be in positive territory.  The longer your time horizon, the less risky stocks are.)

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  http://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

The Success Equation: Untangling Skill and Luck

(Image:  Zen Buddha Silence by Marilyn Barbone.)

August 25, 2019

Michael Mauboussin wrote a great book called The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing (Harvard Business Press, 2012).

Here’s an outline for this blog post:

  • Understand were you are on the skill-luck continuum
  • Assess sample size, significance, and swans
  • Always consider a null hypothesis
  • Think carefully about feedback and rewards
  • Make use of counterfactuals
  • Develop aids to guide and improve your skill
  • Have a plan for strategic interactions
  • Make reversion to the mean work for you
  • Know your limitations

 

UNDERSTAND WHERE YOU ARE ON THE SKILL-LUCK CONTINUUM

If an activity is mostly skill or mostly luck, it’s generally easy to classify it as such.  But many activities are somewhere in-between the two extremes, and it’s often hard to say where it falls on the continuum between pure skill and pure luck.

An activity dominated by skill means that results can be predicted reasonably well.  (You do need to consider the rate at which skill changes, though.)  A useful statistic is one that is persistent—the current outcome is highly correlated with the previous outcome.

An activity dominated by luck means you need a very large sample to detect the influence of skill.  The current outcome is not correlated with the previous outcome.

Obviously the location of an activity on the continuum gives us guidance on how much reversion to the mean is needed in making a prediction.  In an activity that is mostly skill, the best estimate for the next outcome is the current outcome.  In an activity that is mostly luck, the best guess for the next outcome is close to the base rate (the long-term average), i.e., nearly a full reversion to the mean.

Our minds by nature usually fail to regress to the mean as much as we should.  That’s because System 1—the automatic, intuitive part of our brain—invents coherent stories based on causality.  This worked fine during most of our evolutionary history.  But when luck plays a significant role, there has to be substantial reversion to the mean when predicting the next outcome.

 

ASSESS SAMPLE SIZE, SIGNIFICANCE, AND SWANS

Even trained scientists have a tendency to believe that a small sample of a population is representative of the whole population.  But a small sample can deviate meaningfully from the larger population.

If an activity is mostly skill, then a small sample will be representative of the larger population from which it is drawn.  If an activity is mostly luck, then a small sample can be significantly different from the larger population.  A small sample is not reliable when an activity is mostly luck—we need a large sample in this case in order to glean information.

In business, it would be an error to create a sample of all the companies that used a risky strategy and won, without also taking into account all the companies that used the same strategy and lost.  A narrow sample of just the winners would obviously be a biased view of the strategy’s quality.

Also be careful not to confuse statistical significance with economic significance.  Mauboussin quotes Deirdre McCloskey and Stephen Ziliak: “Tell me the oomph of your coefficient; and do not confuse it with mere statistical significance.”

Lastly, it’s important to keep in mind that some business strategies can produce a long series of small gains, followed by a huge loss.  Most of the large U.S. banks pursued such a strategy from 2003-2007.  It would obviously be a big mistake to conclude that a long series of small gains is safe if in reality it is not.

Another example of ignoring black swans is Long-Term Capital Management.  The fund’s actual trades were making about 1% per year.  But LTCM argued that these trades had infintessimally small risk, and so they levered the trades at approximately 40:1.  Many banks didn’t charge LTCM anything for the loan because LTCM was so highly regarded at the time, having a couple of Nobel Prize winners, etc.  Then a black swan arrived—the Asian financial crisis in 1998.  LTCM’s trades went against them, and because of the astronomically high leverage, the fund imploded.

 

ALWAYS CONSIDER A NULL HYPOTHESIS

Always compare the outcomes to what would have been generated under the null hypothesis.  Many streaks can easily be explained by luck alone.

Mauboussin gives the example of various streaks of funds beating the market.  Andrew Mauboussin and Sam Arbesman did a study on this.  They assumed that the probability a given fund would beat the S&P 500 Index was equal to the fraction of active funds that beat the index during a given year.  For example, 52 percent of funds beat the S&P 500 in 1993, so the null model assigns the same percentage probability that any given fund would beat the market in that year.  Mauboussin and Arbesman then ran ten thousand random simulations.

They determined that, under the null model—pure luck and no skill—146.9 funds would have a 5-year market-beating streak, 53.6 funds would have a 6-year streak, 21.4 funds would have a 7-year streak, 7.6 funds would have an 8-year streak, and 3.0 funds would have a 9-year streak.  They compared these figures to the actual empirical frequencies:  206 funds had 5-year streaks, 119 had 6-year streaks, 75 had 7-year streaks, 23 had 8-year streaks, and 28 had 9-year streaks.

So there were many more streaks in the empirical data than the null model generated.  This meant that some of those streaks involved the existence of skill.

 

THINK CAREFULLY ABOUT FEEDBACK AND REWARDS

Everybody wants to improve.  The keys to improving performance include high-quality feedback and proper rewards.

Only a small percentage of people achieve expertise through deliberate practice.  Most people hit a performance plateau and are satisfied to stay there.  Of course, for many activities—like driving—that’s perfectly fine.

The deliberate practice required to develop true expertise involves a great deal of hard and tedious work.  It is not pleasant.  It requires thousands of hours of very focused effort.  And there must be a lot of timely and accurate feedback in order for someone to keep improving and eventually attain expertise.

Even if you’re not pursuing expertise, the keys to improvement are still focused practice and high-quality feedback.

In activities where skill plays a significant role, actual performance is a reasonable measure of progress.  Where luck plays a strong role, the focus must be on the process.  Over shorter periods of time—more specifically, over a relatively small number of trials—a good process can lead to bad outcomes, and a bad process can lead to good outcomes.  But over time, with a large number of trials, a good process will yield good outcomes overall.

The investment industry struggles in this area.  When a strategy does well over a short period of time, quite often it is marketed and new investors flood in.  When a strategy does poorly over a short period of time, very often investors leave.  Most of the time, these strategies mean revert, so that the funds that just did well do poorly and the funds that just did poorly do well.

Another area that’s gone off-track is rewards for executives.  Stock options have become a primary means of rewarding executives.  But the payoff from a stock option involves a huge amount of randomness.  In the decade of the 1990’s, even poor-performing companies saw their stocks increase a great deal.  In the decade of the 2000’s, many high-performing companies saw their stocks stay relatively flat.  So stock options on the whole have not distinguished between skill and luck.

A solution would involve having the stock be measured relative to an index or relative to an appropriate peer group.  Also, the payoff from options could happen over longer periods of time.

Lastly, although executives—like the CEO—are much more skillful than their junior colleagues, often executive success depends to a large extent on luck while the success of those lower down can be attributed almost entirely to skill.  For instance, the overall success of a company may only have a 0.3 correlation with the skill of the CEO.  And yet the CEO would be paid as if the company’s success was highly correlated with his or her skill.

 

MAKE USE OF COUNTERFACTUALS

Once we know what happened in history, hindsight bias naturally overcomes us and we forget how unpredictable the world looked beforehand.  We come up with reasons to explain past outcomes.  The reasons we invent typically make it seem as if the outcomes were inevitable when they may have been anything but.

Mauboussin says a good way to avoid hindsight bias is to engage in counterfactual thinking—a careful consideration of what could have happened but didn’t.

Mauboussin gives an example in Chapter 6 of the book: MusicLab.  Fourteen thousand people were randomly divided into 8 groups—each 10% of the total number of people—and one independent group—20% of the total number of people.  There were forty-eight songs from unknown bands.  In the independent group, each person could listen to each song and then decide to download it based on that alone.  In the other 8 groups, for each song, a person would see how many other people in his or her group had already downloaded the song.

You could get a reasonable estimate for the “objective quality” of a song by looking at how the independent group rated them.

But in the 8 “social influence” groups, strange things happened based purely on luck—or which songs were downloaded early on and which were not.  For instance, a song “Lockdown” was rated twenty-sixth in the independent group.  But it was the number-one hit in one of the social influence worlds and number forty in another.

In brief, to maintain an open mind about the future, it is very helpful to maintain an open mind about the past.  We have to work hard to overcome our natural tendency to view what happened as having been inevitable.  System 1 always creates a story based on causality—System 1 wants to explain simply what happened and close the case.

If we do the Rain Dance and it rains, then to the human brain, it looks like the dance caused the rain.

But when we engage System 2 (the logical, mathematical part of our brain)—which requires conscious effort—we can come to realize that the Rain Dance didn’t cause the rain.

 

DEVELOP AIDS TO GUIDE AND IMPROVE YOUR SKILL

Depending on where an activity lies on the pure luck to pure skill continuum, there are different ways to improve skill.

When luck predominates, to improve our skill we have to focus on learning the process for making good decisions.  A good process must be well grounded in three areas:

  • analytical
  • psychological
  • organizational

In picking an undervalued stock, the analytical part means finding a discrepancy between price and value.

The psychological part of a good process entails an identification of the chief cognitive biases, and techniques to mitigate the influence of these cognitive biases.  For example, we all tend to be wildly overconfident when we make predictions.  System 1 automatically makes predictions all the time.  Usually this is fine.  But when the prediction involves a probabilistic area of life—such as an economy, a stock market, or a political situation—System 1 makes errors systematically.  In these cases, it is essential to engage System 2 in careful statistical thinking.

The organizational part of a good process should align the interests of principals and agents—for instance, shareholders (principals) and executives (agents).  If the executives own a large chunk of stock, then their interests are much more aligned with shareholder interests.

Now consider the middle of the continuum between luck and skill.  In this area, a checklist can be very useful.  A doctor caring for a patient is focused on the primary problem and can easily forget about the simple steps required to minimize infections.  Following the suggestion of Peter Pronovost, many hospitals have introduced simple checklists.  Thousands of lives and hundreds of millions of dollars have been saved, as the checklists have significantly reduced infections and deaths related to infections.

A checklist can also help in a stressful situation.  The chemicals of stress disrupt the functioning of the frontal lobes—the seat of reason.  So a READ-DO checklist gets you to take the concrete, important steps even when you’re not thinking clearly.

Writes Mauboussin:

Checklists have never been shown to hurt performance in any field, and they have helped results in a great many instances.

Finally, anyone serious about improving their performance should write down—if possible—the basis for every decision and then measure honestly how each decision turned out.  This careful measurement is the foundation for continual improvement.

The last category involves activities that are mostly skill.  The key to improvement is deliberate practice and good feedback.  A good coach can be a great help.

Even experts benefit from a good coach.  Feedback is the single most powerful way to improve skill.  Being open to honest feedback is difficult because it means being willing to admit where we need to change.

Mauboussin concludes:

One simple and inexpensive technique for getting feedback is to keep a journal that tracks your decisions.  Whenever you make a decision, write down what you decided, how you came to that decision, and what you expect to happen.  Then, when the results of that decision are clear, write them down and compare them with what you thought would happen.  The journal won’t lie.  You’ll see when you’re wrong.  Change your behavior accordingly.

 

HAVE A PLAN FOR STRATEGIC INTERACTIONS

The weaker side won more conflicts in the twentieth century than in the nineteenth.  This is because the underdogs learned not to go toe-to-toe with a stronger foe.  Instead, the underdogs pursued alternative tactics, like guerrilla warfare.  If you’re an underdog, complicate the game by injecting more luck.

Initially weaker companies almost never succeed by taking on established companies in their core markets.  But, by pursuing disruptive innovation—as described by Professor Clayton Christensen—weaker companies can overcome stronger companies.  The weaker companies pursue what is initially a low-margin part of the market.  The stronger companies have no incentive to invest in low-margin innovation when they have healthy margins in more established areas.  But over time, the low-margin technology improves to the point where demand for it increases and profit margins typically follow.  By then, the younger companies are already ahead by a few of years, and the more established companies usually are unable to catch up.

 

MAKE REVERSION TO THE MEAN WORK FOR YOU

Mauboussin writes:

We are all in the business of forecasting.

Reversion to the mean is difficult for our brains to understand.  As noted, System 1 always invents a cause for everything that happens.  But often there is no specific cause.

Mauboussin cites an example given by Daniel Kahneman: Julie is a senior in college who read fluently when she was four years old.  Estimate her GPA.

People often guess a GPA of around 3.7.  Most people assume that being precocious is correlated with doing well in college.  But it turns out that reading at a young age is not related to doing well in college.  That means the best guess for the GPA would be much closer to the average.

Mauboussin adds:

Reversion to the mean is most pronounced at the extremes, so the first lesson is to recognize that when you see extremely good or bad results, they are unlikely to continue that way.  This doesn’t mean that good results will necessarily be followed by bad results, or vice versa, but rather that the next thing that happens will probably be closer to the average of all things that happen.

 

KNOW YOUR LIMITATIONS

There is always a great deal that we simply don’t know and can’t know.  We must develop and maintain a healthy sense of humility.

Predictions are often difficult in many situations.  The sample size and the length of time over which you measure are essential.  And you need valid data.

Moreover, things can change.  If fiscal policy has become much more stimulative than it used to be, then bear markets may—or may not—be shallower and shorter.  And stocks may generally be higher than previously, as Ben Graham pointed out in a 1963 lecture, “Securities in an Insecure World”: http://jasonzweig.com/wp-content/uploads/2015/04/BG-speech-SF-1963.pdf

If monetary policy is much more stimulative than before—including a great deal of money-printing and zero or negative interest rates—then the long-term average of stock prices could conceivably make another jump higher.

The two fundamental changes just mentioned are part of why most great value investors never try to time the market.  As Buffett has said:

  • Forecasts may tell you a great deal about the forecaster;  they tell you nothing about the future.
  • I make no effort to predict the course of general business or the stock market.  Period.
  • I don’t invest a dime based on macro forecasts.

Henry Singleton—who has one of the best capital allocation records of all time—perhaps put it best:

I don’t believe all this nonsense about market timing.  Just buy very good value and when the market is ready that value will be recognized.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  http://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

There’s Always Something to Do

(Image:  Zen Buddha Silence by Marilyn Barbone.)

August 18, 2019

There’s Always Something to Do:  The Peter Cundill Investment Approach, by Christopher Risso-Gill (2011), is an excellent book.  Cundill was a highly successful deep value investor whose chosen method was to buy stocks below their liquidation value.

Here is an outline for this blog post:

  • Peter Cundill
  • Getting to First Base
  • Launching a Value Fund
  • Value Investment in Action
  • Going Global
  • A Decade of Success
  • Investments and Stratagems
  • Learning From Mistakes
  • Entering the Big League
  • There’s Always Something Left to Learn
  • Pan Ocean
  • Fragile X
  • What Makes a Great Investor?
  • Glossary of Terms with Cundill’s Comments

 

PETER CUNDILL

It was December in 1973 when Peter Cundill first discovered value investing.  He was 35 years old at the time.  Up until then, despite a great deal of knowledge and experience, Cundill hadn’t yet discovered an investment strategy.  He happened to be reading George Goodman’s Super Money on a plane when he came across chapter 3 on Benjamin Graham and Warren Buffett.  Cundill wrote about his epiphany that night in his journal:

…there before me in plain terms was the method, the solid theoretical back-up to selecting investments based on the principle of realizable underlying value.  My years of apprenticeship were over:  ‘THIS IS WHAT I WANT TO DO FOR THE REST OF MY LIFE!’

What particularly caught Cundill’s attention was Graham’s notion that a stock is cheap if it sells below liquidation value.  The farther below liquidation value the stock is, the higher the margin of safety and the higher the potential returns.  This idea is at odds with modern finance theory, according to which getting higher returns always requires taking more risk.

Peter Cundill became one of the best value investors in the world.  He followed a deep value strategy based entirely on buying companies below their liquidation values.

We do liquidation analysis and liquidation analysis only.

 

GETTING TO FIRST BASE

One of Cundill’s first successful investments was in Bethlehem Copper.  Cundill built up a position at $4.50, roughly equal to cash on the balance sheet and far below liquidation value:

Both Bethlehem and mining stocks in general were totally out of favour with the investing public at the time.  However in Peter’s developing judgment this was not just an irrelevance but a positive bonus.  He had inadvertently stumbled upon a classic net-net:  a company whose share price was trading below its working capital, net of all its liabilities.  It was the first such discovery of his career and had the additional merit of proving the efficacy of value theory almost immediately, had he been able to recognize it as such.  Within four months Bethlehem had doubled and in six months he was able to start selling some of the position at $13.00.  The overall impact on portfolio performance had been dramatic.

Riso-Gill describes Cundill as having boundless curiosity.  Cundill would not only visit the worst performing stock market in the world near the end of each year in search of bargains.  But he also made a point of total immersion with respect to the local culture and politics of any country in which he might someday invest.

 

LAUNCHING A VALUE FUND

Early on, Cundill had not yet developed the deep value approach based strictly on buying below liquidation value.  He had, however, concluded that most models used in investment research were useless and that attempting to predict the general stock market was not doable with any sort of reliability.  Eventually Cundill immersed himself in Graham and Dodd’s Security Analysis, especially chapter 41, “The Asset-Value Factor in Common-Stock Valuation,” which he re-read and annotated many times.

When Cundill was about to take over an investment fund, he wrote to the shareholders about his proposed deep value investment strategy:

The essential concept is to buy under-valued, unrecognized, neglected, out of fashion, or misunderstood situations where inherent value, a margin of safety, and the possibility of sharply changing conditions created new and favourable investment opportunities.  Although a large number of holdings might be held, performance was invariably established by concentrating in a few holdings.  In essence, the fund invested in companies that, as a result of detailed fundamental analysis, were trading below their ‘intrinsic value.’  The intrinsic value was defined as the price that a private investor would be prepared to pay for the security if it were not listed on a public stock exchange.  The analysis was based as much on the balance sheet as it was on the statement of profit and loss.

Cundill went on to say that he would only buy companies trading below book value, preferably below net working capital less long term debt (Graham’s net-net method).  Cundill also required that the company be profitable—ideally having increased its earnings for the past five years—and dividend-paying—ideally with a regularly increasing dividend.  The price had to be less than half its former high and preferably near its all time low.  And the P/E had to be less than 10.

Cundill also studied past and future profitability, the ability of management, and factors governing sales volume and costs.  But Cundill made it clear that the criteria were not always to be followed precisely, leaving room for investment judgment, which he eventually described as an art form.

Cundill told shareholders about his own experience with the value approach thus far.  He had started with $600,000, and the portfolio increased 35.2%.  During the same period, the All Canadian Venture Fund was down 49%, the TSE industrials down 20%, and the Dow down 26%.  Cundill also notes that 50% of the portfolio had been invested in two stocks (Bethlehem Copper and Credit Foncier).

About this time, Irving Kahn became a sort of mentor to Cundill.  Kahn had been Graham’s teaching assistant at Columbia University.

 

VALUE INVESTMENT IN ACTION

Having a clearly defined set of criteria helped Cundill to develop a manageable list of investment candidates in the decade of 1974 to 1984 (which tended to be a good time for value investors).  The criteria also helped him identify a number of highly successful investments.

For example, the American Investment Company (AIC), one of the largest personal loan companies in the United States, saw its stock fall from over $30.00 to $3.00, despite having a tangible book value per share of $12.00.  As often happens with good contrarian value candidates, the fears of the market about AIC were overblown.  Eventually the retail loan market recovered, but not before Cundill was able to buy 200,000 shares at $3.00.  Two years later, AIC was taken over at $13.00 per share by Leucadia.  Cundill wrote:

As I proceed with this specialization into buying cheap securities I have reached two conclusions.  Firstly, very few people really do their homework properly, so now I always check for myself.  Secondly, if you have confidence in your own work, you have to take the initiative without waiting around for someone else to take the first plunge.

…I think that the financial community devotes far too much time and mental resource to its constant efforts to predict the economic future and consequent stock market beaviour using a disparate, and almost certainly incomplete, set of statistical variables.  It makes me wonder what might be accomplished if all this time, energy, and money were to be applied to endeavours with a better chance of proving reliable and practically useful.

Meanwhile, Cundill had served on the board of AIC, which brought some valuable experience and associations.

Cundill found another highly discounted company in Tiffany’s.  The company owned extremely valuable real estate in Manhattan that was carried on its books at a cost much lower than the current market value.  Effectively, the brand was being valued at zero.  Cundill accumulated a block of stock at $8.00 per share.  Within a year, Cundill was able to sell it at $19.00.  This seemed like an excellent result, except that six months later, Avon Products offered to buy Tiffany’s at $50.00.  Cundill would comment:

The ultimate skill in this business is in knowing when to make the judgment call to let profits run.

Sam Belzberg—who asked Cundill to join him as his partner at First City Financial—described Cundill as follows:

He has one of the most important attributes of the master investor because he is supremely capable of running counter to the herd.  He seems to possess the ability to consider a situation in isolation, cutting himself off from the mill of general opinion.  And he has the emotional confidence to remain calm when events appear to be indicating that he’s wrong.

 

GOING GLOBAL

Partly because of his location in Canada, Cundill early on believed in global value investing.  He discovered that just as individual stocks can be neglected and misunderstood, so many overseas markets can be neglected and misunderstood.  Cundill enjoyed traveling to these various markets and learning the legal accounting practices.  In many cases, the difficulty of mastering the local accounting was, in Cundill’s view, a ‘barrier to entry’ to other potential investors.

Cundill also worked hard to develop networks of locally based professionals who understood value investing principles.  Eventually, Cundill developed the policy of exhaustively searching the globe for value, never favoring domestic North American markets.

 

A DECADE OF SUCCESS

Cundill summarized the lessons of the first 10 years, during which the fund grew at an annual compound rate of 26%.  He included the following:

  • The value method of investing will tend at least to give compound rates of return in the high teens over longer periods of time.
  • There will be losing years; but if the art of making money is not to lose it, then there should not be substantial losses.
  • The fund will tend to do better in slightly down to indifferent markets and not to do as well as our growth-oriented colleagues in good markets.
  • It is ever more challenging to perform well with a larger fund…
  • We have developed a network of contacts around the world who are like-minded in value orientation.
  • We have gradually modified our approach from a straight valuation basis to one where we try to buy securities selling below liquidation value, taking into consideration off-balance sheet items.
  • THE MOST IMPORTANT ATTRIBUTE FOR SUCCESS IN VALUE INVESTING IS PATIENCE, PATIENCE, AND MORE PATIENCE.  THE MAJORITY OF INVESTORS DO NOT POSSESS THIS CHARACTERISTIC.

 

INVESTMENTS AND STRATAGEMS

Buying at a discount to liquidation value is simple in concept.  But in practice, it is not at all easy to do consistently well over time.  Peter Cundill explained:

None of the great investments come easily.  There is almost always a major blip for whatever reason and we have learnt to expect it and not to panic.

Although Cundill focused exclusively on discount to liquidation value when analyzing equities, he did develop a few additional areas of expertise, such as distressed debt.  Cundill discovered that, contrary to his expectation of fire-sale prices, an investor in distressed securities could often achieve large profits during the actual process of liquidation.  Success in distressed debt required detailed analysis.

 

LEARNING FROM MISTAKES

1989 marked the fifteenth year in a row of positive returns for Cundill’s Value Fund.  The compound growth rate was 22%.  But the fund was only up 10% in 1989, which led Cundill to perform his customary analysis of errors:

…How does one reduce the margin of error while recognizing that investments do, of course, go down as well as up?  The answers are not absolutely clear cut but they certainly include refusing to compromise by subtly changing a question so that it shapes the answer one is looking for, and continually reappraising the research approach, constantly revisiting and rechecking the detail.

What were last year’s winners?  Why?—I usually had the file myself, I started with a small position and stayed that way until I was completely satisfied with every detail.

For most value investors, the investment thesis depends on a few key variables, which should be written down in a short paragraph.  It’s important to recheck each variable periodically.  If any part of the thesis has been invalidated, you must reassess.  Usually the stock is no longer a bargain.

It’s important not to invent new reasons for owning the stock if one of the original reasons has been falsified.  Developing new reasons for holding a stock is usually misguided.  However, you need to remain flexible.  Occasionally the stock in question is still a bargain.

 

ENTERING THE BIG LEAGUE

In the mid 1990’s, Cundill made a large strategic shift out of Europe and into Japan.  Typical for a value investor, he was out of Europe too early and into Japan too early.  Cundill commented:

We dined out in Europe, we had the biggest positions in Deutsche Bank and Paribas, which both had big investment portfolios, so you got the bank itself for nothing.  You had a huge margin of safety—it was easy money.  We had doubles and triples in those markets and we thought we were pretty smart, so in 1996 and 1997 we took our profits and took flight to Japan, which was just so beaten up and full of values.  But in doing so we missed out on some five baggers, which is when the initial investment has multiplied five times, and we had to wait at least two years before Japan started to come good for us.

This is a recurring problem for most value investors—that tendency to buy and to sell too early.  The virtues of patience are severely tested and you get to thinking it’s never going to work and then finally your ship comes home and you’re so relieved that you sell before it’s time.  What we ought to do is go off to Bali or some such place and sit in the sun to avoid the temptation to sell too early.

As for Japan, Cundill had long ago learned the lesson that cheap stocks can stay cheap for “frustratingly long” periods of time.  Nonetheless, Cundill kept loading up on cheap Japanese stocks in a wide range of sectors.  In 1999, his Value Fund rose 16%, followed by 20% in 2000.

 

THERE’S ALWAYS SOMETHING LEFT TO LEARN

Although Cundill had easily avoided Nortel, his worst investment was nevertheless in telecommunications: Cable & Wireless (C&W).  In the late 1990’s, the company had to give up many of its networks in newly independent former British colonies.  The shares dropped from 15 pounds per share to 6 pounds.

A new CEO, Graham Wallace, was brought in.  He quickly and skillfully negotiated a series of asset sales, which dramatically transformed the balance sheet from net debt of 4 billion pounds to net cash of 2.6 billion pounds.  Given the apparently healthy margin of safety, Cundill began buying shares in March 2000 at just over 4 pounds per share.  (Net asset value was 4.92 pounds per share.)  Moreover:

[Wallace was] generally regarded as a relatively safe pair of hands unlikely to be tempted into the kind of acquisition spree overseen by his predecessor.

Unfortunately, a stream of investment bankers, management consultants, and brokers made a simple but convincing pitch to Wallace:

the market for internet-based services was growing at three times the rate for fixed line telephone communications and the only quick way to dominate that market was by acquisition.

Wallace proceeded to make a series of expensive acquisitions of loss-making companies.  This destroyed C&W’s balance sheet and also led to large operating losses.  Cundill now realized that the stock could go to zero, and he got out, just barely.  As Cundill wrote later:

… So we said, look they’ve got cash, they’ve got a valuable, viable business and let’s assume the fibre optic business is worth zero—it wasn’t, it was worth less than zero, much, much less!

Cundill had invested nearly $100 million in C&W, and they lost nearly $59 million.  This loss was largely responsible for the fund being down 11% in 2002.  Cundill realized that his investment team needed someone to be a sceptic for each potential investment.

 

PAN OCEAN

In late 2002, oil prices began to rise sharply based on global growth.  Cundill couldn’t find any net-net’s among oil companies, so he avoided these stocks.  Some members of his investment team argued that there were some oil companies that were very undervalued.  Finally, Cundill announced that if anyone could find an oil company trading below net cash, he would buy it.

Cundill’s cousin, Geoffrey Scott, came across a neglected company:  Pan Ocean Energy Corporation Ltd.  The company was run by David Lyons, whose father, Vern Lyons, had founded Ocelot Energy.  Lyons concluded that there was too much competition for a small to medium sized oil company operating in the U.S. and Canada.  The risk/reward was not attractive.

What he did was to merge his own small Pan Ocean Energy with Ocelot and then sell off Ocelot’s entire North American and other peripheral parts of the portfolio, clean up the balance sheet, and bank the cash.  He then looked overseas and determined that he would concentrate on deals in Sub-Saharan Africa, where licenses could be secured for a fraction of the price tag that would apply in his domestic market.

Lyons was very thorough and extremely focused… He narrowed his field down to Gabon and Tanzania and did a development deal with some current onshore oil production in Gabon and a similar offshore gas deal in Tanzania.  Neither was expensive.

Geoffrey Scott examined Pan Ocean, and found that its share price was almost equal to net cash and the company had no debt.  He immediately let Cundill know about it.  Cundill met with David Lyons and was impressed:

This was a cautious and disciplined entrepreneur, who was dealing with a pool of cash that in large measure was his own.

Lyons invited Cundill to see the Gabon project for himself.  Eventually, Cundill saw both the Gabon project and the Tanzania project.  He liked what he saw.  Cundill’s fund bought 6% of Pan Ocean.  They made six times their money in two and a half years.

 

FRAGILE X

As early as 1998, Cundill had noticed a slight tremor in his right arm.  The condition worsened and affected his balance.  Cundill continued to lead a very active life, still reading and traveling all the time, and still a fitness nut.  He was as sharp as ever in 2005.  Risso-Gill writes:

Ironically, just as Peter’s health began to decline an increasing number of industry awards for his achievements started to come his way.

For instance, he received the Analyst’s Choice award as “The Greatest Mutual Fund Manager of All Time.”

In 2009, Cundill decided that it was time to step down, as his condition had progressively worsened.  He continued to be a voracious reader.

 

WHAT MAKES A GREAT INVESTOR?

Risso-Gill tries to distill from Cundill’s voluminous journal writings what Cundill himself believed it took to be a great value investor.

INSATIABLE CURIOSITY

Curiosity is the engine of civilization.  If I were to elaborate it would be to say read, read, read, and don’t forget to talk to people, really talk, listening with attention and having conversations, on whatever topic, that are an exchange of thoughts.  Keep the reading broad, beyond just the professional.  This helps to develop one’s sense of perspective in all matters.

PATIENCE

Patience, patience, and more patience…

CONCENTRATION

You must have the ability to focus and to block out distractions.  I am talking about not getting carried away by events or outside influences—you can take them into account, but you must stick to your framework.

ATTENTION TO DETAIL

Never make the mistake of not reading the small print, no matter how rushed you are.  Always read the notes to a set of accounts very carefully—they are your barometer… They will give you the ability to spot patterns without a calculator or spreadsheet.  Seeing the patterns will develop your investment insights, your instincts—your sense of smell.  Eventually it will give you the agility to stay ahead of the game, making quick, reasoned decisions, especially in a crisis.

CALCULATED RISK

… Either [value or growth investing] could be regarded as gambling, or calculated risk.  Which side of that scale they fall on is a function of whether the homework has been good enough and has not neglected the fieldwork.

INDEPENDENCE OF MIND

I think it is very useful to develop a contrarian cast of mind combined with a keen sense of what I would call ‘the natural order of things.’  If you can cultivate these two attributes you are unlikely to become infected by dogma and you will begin to have a predisposition toward lateral thinking—making important connections intuitively.

HUMILITY

I have no doubt that a strong sense of self belief is important—even a sense of mission—and this is fine as long as it is tempered by a sense of humour, especially an ability to laugh at oneself.  One of the greatest dangers that confront those who have been through a period of successful investment is hubris—the conviction that one can never be wrong again.  An ability to see the funny side of oneself as it is seen by others is a strong antidote to hubris.

ROUTINES

Routines and discipline go hand in hand.  They are the roadmap that guides the pursuit of excellence for its own sake.  They support proper professional ambition and the commercial integrity that goes with it.

SCEPTICISM

Scepticism is good, but be a sceptic, not an iconoclast.  Have rigour and flexibility, which might be considered an oxymoron but is exactly what I meant when I quoted Peter Robertson’s dictum ‘always change a winning game.’  An investment framework ought to include a liberal dose of scepticism both in terms of markets and of company accounts.

PERSONAL RESPONSIBILITY

The ability to shoulder personal responsibility for one’s investment results is pretty fundamental… Coming to terms with this reality sets you free to learn from your mistakes.

 

GLOSSARY OF TERMS WITH CUNDILL’S COMMENTS

Here are some of the terms.

ANALYSIS

There’s almost too much information now.  It boggles most shareholders and a lot of analysts.  All I really need is a company’s published reports and records, that plus a sharp pencil, a pocket calculator, and patience.

Doing the analysis yourself gives you confidence buying securities when a lot of the external factors are negative.  It gives you something to hang your hat on.

ANALYSTS

I’d prefer not to know what the analysts think or to hear any inside information.  It clouds one’s judgment—I’d rather be dispassionate.

BROKERS

I go cold when someone tips me on a company.  I like to start with a clean sheet: no one’s word.  No givens.  I’m more comfortable when there are no brokers looking over my shoulder.

They really can’t afford to be contrarians.  A major investment house can’t afford to do research for five customers who won’t generate a lot of commissions.

EXTRA ASSETS

This started for me when Mutual Shares chieftain Mike Price, who used to be a pure net-net investor, began talking about something called the ‘extra asset syndrome’ or at least that is what I call it.  It’s taking, you might say, net-net one step farther, to look at all of a company’s assets, figure the true value.

FORECASTING

We don’t do a lot of forecasting per se about where markets are going.  I have been burned often enough trying.

INDEPENDENCE

Peter Cundill has never been afraid to make his own decisions and by setting up his own fund management company he has been relatively free from external control and constraint.  He doesn’t follow investment trends or listen to the popular press about what is happening on ‘the street.’  He has travelled a lonely but profitable road.

Being willing to be the only one in the parade that’s out of step.  It’s awfully hard to do, but Peter is disciplined.  You have to be willing to wear bellbottoms when everyone else is wearing stovepipes.’ – Ross Southam

INVESTMENT FORMULA

Mostly Graham, a little Buffett, and a bit of Cundill.

I like to think that if I stick to my formula, my shareholders and I can make a lot of money without much risk.

When I stray out of my comfort zone I usually get my head handed to me on a platter.

I suspect that my thinking is an eclectic mix, not pure net-net because I couldn’t do it anyway so you have to have a new something to hang your hat on.  But the framework stays the same.

INVESTMENT STRATEGY

I used to try and pick the best stocks in the fund portfolios, but I always picked the wrong ones.  Now I take my own money and invest it with that odd guy Peter Cundill.  I can be more detached when I treat myself as a normal client.

If it is cheap enough, we don’t care what it is.

Why will someone sell you a dollar for 50 cents?  Because in the short run, people are irrational on both the optimistic and pessimistic side.

MANTRAS

All we try to do is buy a dollar for 40 cents.

In our style of doing things, patience is patience is patience.

One of the dangers about net-net investing is that if you buy a net-net that begins to lose money your net-net goes down and your capacity to be able to make a profit becomes less secure.  So the trick is not necessarily to predict what the earnings are going to be but to have a clear conviction that the company isn’t going bust and that your margin of safety will remain intact over time.

MARGIN OF SAFETY

The difference between the price we pay for a stock and its liquidation value gives us a margin of safety.  This kind of investing is one of the most effective ways of achieving good long-term results.

MARKETS

If there’s a bad stock market, I’ll inevitably go back in too early.  Good times last longer than we think but so do bad times.

Markets can be overvalued and keep getting expensive, or undervalued and keep getting cheap.  That’s why investing is an art form, not a science.

I’m agnostic on where the markets will go.  I don’t have a view.  Our task is to find undervalued global securities that are trading well below their intrinsic value.  In other words, we follow the strict Benjamin Graham approach to investing.

NEW LOWS

Search out the new lows, not the new highs.  Read the Outstanding Investor Digest to find out what Mason Hawkins or Mike Price is doing.  You know good poets borrow and great poets steal.  So see what you can find.  General reading—keep looking at the news to see what’s troubled.  Experience and curiosity is a really winning combination.

What differentiates us from other money managers with a similar style is that we’re comfortable with new lows.

NOBODY LISTENING

Many people consider value investing dull and as boring as watching paint dry.  As a consequence value investors are not always listened to, especially in a stock market bubble.  Investors are often in too much of a hurry to latch on to growth stocks to stop and listen because they’re afraid of being left out…

OSMOSIS

I don’t just calculate value using net-net.  Actually there are many different ways but you have to use what I call osmosis—you have got to feel your way.  That is the art form, because you are never going to be right completely; there is no formula that will ever get you there on its own.  Osmosis is about intuition and about discipline and about all the other things that are not quantifiable.  So can you learn it?  Yes, you can learn it, but it’s not a science, it’s an art form.  The portfolio is a canvas to be painted and filled in.

PATIENCE

When times aren’t good I’m still there.  You find bargains among the unpopular things, the things that everybody hates.  The key is that you must have patience.

RISK

We try not to lose.  But we don’t want to try too hard.  The losses, of course, work against you in establishing decent compound rates of return.  And I hope we won’t have them.  But I don’t want to be so risk-averse that we are always trying too hard not to lose.

STEADY RETURNS

All I know is that if you can end up with a 20% track record over a longer period of time, the compound rates of return are such that the amounts are staggering.  But a lot of investors want excitement, not steady returns.  Most people don’t see making money as grinding it out, doing it as efficiently as possible.  If we have a strong market over the next six months and the fund begins to drop behind and there isn’t enough to do, people will say Cundill’s lost his touch, he’s boring.

TIMING: “THERE’S ALWAYS SOMETHING TO DO”

…Irving Kahn gave me some advice many years ago when I was bemoaning the fact that according to my criteria there was nothing to do.  He said, ‘there is always something to do.  You just need to look harder, be creative and a little flexible.’

VALUE INVESTING

I don’t think I want to become too fashionable.  In some ways, value investing is boring and most investors don’t want a boring life—they want some action: win, lose, or draw.

I think the best decisions are made on the basis of what your tummy tells you.  The Jesuits argue reason before passion.  I argue reason and passion.  Intellect and intuition.  It’s a balance.

We do liquidation analysis and liquidation analysis only.

Ninety to 95% of all my investing meets the Graham tests.  The times I strayed from a rigorous application of this philosophy I got myself into trouble.

But what do you do when none of these companies is available?  The trick is to wait through the crisis stage and into the boredom stage.  Things will have settled down by then and values will be very cheap again.

We customarily do three tests: one of them asset-based—the NAV, using the company’s balance sheet.  The second is the sum of the parts, which I think is probably the most important part that goes into the balance sheet I’m creating.  And then a future NAV, which is making a stab (which I am always suspicious about) at what you think the business might be doing in three years from now.

WORKING LIFE

I’ve been doing this for thirty years.  And I love it.  I’m lucky to have the kind of life where the differentiation between work and play is absolutely zilch.  I have no idea whether I’m working or whether I’m playing.

My wife says I’m a workaholic, but my colleagues say I haven’t worked for twenty years.  My work is my play.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  http://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

Business Adventures

(Image:  Zen Buddha Silence by Marilyn Barbone.)

August 4, 2019

In 1991, when Bill Gates met Warren Buffett, Gates asked him to recommend his favorite business book.  Buffett immediately replied, “It’s Business Adventures, by John Brooks.  I’ll send you my copy.”  Gates wrote in 2014:

Today, more than two decades after Warren lent it to me—and more than four decades after it was first published—Business Adventures remains the best business book I’ve ever read.  John Brooks is still my favorite business writer.

It’s certainly true that many of the particulars of business have changed.  But the fundamentals have not.  Brooks’s deeper insights about business are just as relevant today as they were back then.  In terms of its longevity, Business Adventures stands alongside Benjamin Graham’s The Intelligent Investor, the 1949 book that Warren says is the best book on investing that he has ever read.

See:  https://www.gatesnotes.com/Books/Business-Adventures

I’ve had the enormous pleasure of reading Business Adventures twice.  John Brooks is quite simply a terrific business writer.

Each chapter of the book is a separate business adventure.  Outline:

  • The Fluctuation
  • The Fate of the Edsel
  • A Reasonable Amount of Time
  • Xerox Xerox Xerox Xerox
  • Making the Customers Whole
  • The Impacted Philosophers
  • The Last Great Corner
  • A Second Sort of Life
  • Stockholder Season
  • One Free Bite

 

THE FLUCTUATION

Brooks recounts J.P. Morgan’s famous answer when an acquaintance asked him what the stock market would do:  “It will fluctuate.”  Brooks then writes:

Apart from the economic advantages and disadvantages of stock exchanges – the advantage that they provide a free flow of capital to finance industrial expansion, for instance, and the disadvantage that they provide an all too convenient way for the unlucky, the imprudent, and the gullible to lose their money – their development has created a whole pattern of social behavior, complete with customs, language, and predictable responses to given events.

Brooks explains that the pattern emerged fully at the first important stock exchange in 1611 in Amsterdam.  Brooks mentions that Joseph de la Vega published, in 1688, a book about the first Dutch stock traders.  The book was aptly titled, Confusion of Confusions.

And the pattern persists on the New York Stock Exchange.  (Brooks was writing in the 1960’s, but many of his descriptions still apply.)  Brooks adds that a few Dutchmen haggling in the rain might seem to be rather far from the millions of participants in the 1960’s.  However:

The first stock exchange was, inadvertently, a laboratory in which new human reactions were revealed.  By the same token, the New York Stock Exchange is also a sociological test tube, forever contributing to the human species’ self-understanding.

On Monday, May 28, 1962, the Dow Jones Average dropped 34.95 points, or more than it had dropped on any day since October 28, 1929.  The volume was the seventh-largest ever.  Then on Tuesday, May 29, after most stocks opened down, the market reversed itself and surged upward with a large gain of 27.03.  The trading volume on Tuesday was the highest ever except for October 29, 1929.  Then on Thursday, May 31, after a holiday on Wednesday, the Dow rose 9.40 points on the fifth-greatest volume ever.

Brooks:

The crisis ran its course in three days, but needless to say, the post-mortems took longer.  One of de la Vega’s observations about the Amsterdam traders was that they were ‘very clever in inventing reasons’ for a sudden rise or fall in stock prices, and the Wall Street pundits certainly needed all the cleverness they could muster to explain why, in the middle of an excellent business year, the market had suddenly taken its second-worst nose dive ever up to that moment.

Many rated President Kennedy’s April crackdown on the steel industry’s planned price increase as one of the most likely causes.  Beyond that, there were comparisons to 1929.  However, there were more differences than similarities, writes Brooks.  For one thing, margin requirements were far higher in 1962 than in 1929.  Nonetheless, the weekend before the May 1962 crash, many securities dealers were occupied sending out margin calls.

In 1929, it was not uncommon for people to have only 10% equity, with 90% of the stock position based on borrowed money.  (The early Amsterdam exchange was similar.)  Since the crash in 1929, margin requirements had been raised to 50% equity (leaving 50% borrowed).

Brooks says the stock market had been falling for most of 1962 up until crash.  But apparently the news before the May crash was good.  Not that news has any necessary relationship with stock movements, although most financial reporting services seem to assume otherwise.  After a mixed opening – some stocks up, some down – on Monday, May 28, volume spiked as selling became predominant.  Volume kept going up thereafter as the selling continued.  Brooks:

Evidence that people are selling stocks at a time when they ought to be eating lunch is always regarded as a serious matter.

One problem in this crash was that the tape – which records the prices of stock trades – got delayed by 55 minutes due to the huge volume.  Some brokerage firms tried to devise their own systems to deal with this issue.  For instance, Merrill Lynch floor brokers – if they had time – would shout the results of trades into a floorside telephone connected to a “squawk box” in the firm’s head office.

Brooks remarks:

All that summer, and even into the following year, security analysts and other experts cranked out their explanations of what had happened, and so great were the logic, solemnity, and detail of these diagnoses that they lost only a little of their force through the fact that hardly any of the authors had had the slightest idea what was going to happen before the crisis occurred.

Brooks then points out that an unprecedented 56.8 percent of the total volume in the crash had been individual investors.  Somewhat surprisingly, mutual funds were a stabilizing factor.  During the Monday sell-off, mutual funds bought more than they sold.  And as stocks surged on Thursday, mutual funds sold more than they bought.  Brooks concludes:

In the last analysis, the cause of the 1962 crisis remains unfathomable;  what is known is that it occurred, and that something like it could occur again.

 

THE FATE OF THE EDSEL

1955 was the year of the automobile, writes Brooks.  American auto makers sold over 7 million cars, a million more than in any previous year.  Ford Motor Company decided that year to make a new car in the medium-price range of $2,400 to $4,000.  Brooks continues:

[Ford] went ahead and designed it more or less in comformity with the fashion of the day, which was for cars that were long, wide, low, lavishly decorated with chrome, liberally supplied with gadgets… Two years later, in September, 1957, Ford put its new car, the Edsel, on the market, to the accompaniment of more fanfare than had attended the arrival of any new car since the same company’s Model A, brought out thirty years earlier.  The total amount spent on the Edsel before the first specimen went on sale was announced as a quarter of a billion dollars;  its launching… was more costly than any other consumer product in history.  As a starter toward getting its investment back, Ford counted on selling at least 200,000 Edsels the first year.

There may be an aborigine somewhere in a remote rainforest who hasn’t yet heard that things failed to turn out that way… on November 19, 1959, having lost, according to some outside estimates, around $350 million on the Edsel, the Ford Company permanently discontinued its production.

Brooks asks:

How could this have happened?  How could a company so mightily endowed with money, experience, and, presumably, brains have been guilty of such a monumental mistake?

Many claimed that Ford had paid too much attention to public-opinion polls and the motivational research it conducted.  But Brooks adds that some non-scientific elements also played a roll.  In particular, after a massive effort to come up with possible names for the car, science was ignored at the last minute and the Edsel was named for the father of the company’s president.  Brooks:

As for the design, it was arrived at without even a pretense of consulting the polls, and by the method that has been standard for years in the designing of automobiles – that of simply pooling the hunches of sundry company committees.

The idea for the Edsel started years earlier.  The company noticed that owners of cars would trade up to the medium-priced car as soon as they could.  The problem was that Ford owners were not trading up to the Mercury, Ford’s medium-priced car, but to the medium-priced cars of its rivals, General Motors and Chrysler.

Late in 1952, a group called the Forward Product Planning Committee gave much of the detailed work to the Lincoln-Mercury Division, run by Richard Krafve (pronounced “Kraffy”).  In 1954, after two years’ work, the Forward Product Planning Committee submitted to the executive committee a six-volume report.  In brief, the report predicted that there would be seventy million cars in the U.S. by 1965, and more than 40 percent of all cars sold would be in the medium-price range.  Brooks:

On the other hand, the Ford bosses were well aware of the enormous risks connected with putting a new car on the market.  They knew, for example, that of the 2,900 American makes that had been introduced since the beginning of the automobile age… only about twenty were still around.

But Ford executives felt optimistic.  They set up another agency, the Special Products Division, again with Krafve in charge.  The new car was referred to as the “E”-Car among Ford designers and workers.  “E” for Experimental.  Roy A. Brown was in charge of the E-car’s design.  Brown stated that they sought to make a car that was unique as compared to the other nineteen cars on the road at the time.

Brooks observes that Krafve later calculated that he and his associates would make at least four thousand decisions in designing the E-Car.  He thought that if they got every decision right, they could create the perfectly designed car.  Krafve admitted later, however, that there wasn’t really enough time for perfection.  They would make modifications, and then modifications of those modifications.  Then time would run out and they had to settle on the most recent modifications.

Brooks comments:

One of the most persuasive and frequently cited explanations of the Edsel’s failure is that it was a victim of the time lag between the decision to produce it and the act of putting it on the market.  It was easy to see a few years later, when smaller and less powerful cars, euphemistically called “compacts,” had become so popular as to turn the old automobile status-ladder upside down, that the Edsel was a giant step in the wrong direction, but it far from easy to see that in fat, tail-finny 1955.

As part of the marketing effort, the Special Products Division tapped David Wallace, director of planning for market research.  Wallace:

‘We concluded that cars are a means to a sort of dream fulfillment.  There’s some irrational factor in people that makes them want one kind of car rather than another – something that has nothing to do with the mechanism at all but with the car’s personality, as the customer imagines it.  What we wanted to do, naturally, was to give the E-Car the personality that would make the greatest number of people want it.’

Wallace’s group decided to get interviews of 1,600 car buyers.  The conclusion, in a nutshell, was that the E-Car could be “the smart car for the younger executive or professional family on its way up.”

As for the name of the car, Krafve had suggested to the members of the Ford family that the new car be named the Edsel Ford – the name of their father.  The three Ford brothers replied that their father probably wouldn’t want the car named after him.  Therefore, they suggested that the Special Products Division look for another name.

The Special Products Division conducted a large research project regarding the best name for the E-Car.  At one point, Wallace interviewed the poet Marianne Moore about a possible name.  A bit later, the Special Products Division contacted Foote, Cone & Belding, an advertising agency, to help with finding a name.

The advertising agency produced 18,000 names, which they then carefully pruned to 6,000.  Wallace told them that was still way too many names from which to pick.  So Foote, Cone & Belding did an all-out three-day session to cut the list down to 10 names.  They divided into two groups for this task.  By chance, when each group produced its list of 10 names, 4 of the names were the same:  Corsair, Citation, Pacer, and Ranger.

Wallace thought that Corsair was clearly the best name.  However, the Ford executive committee had a meeting at a time when all three Ford brothers were away.  Executive vice-president Ernest R. Breech, chairman of the board, led the meeting.  When Breech saw the final list of 10 names, he said he didn’t like any of them.

So Breech and the others were shown another list of names that hadn’t quite made the top 10.  The Edsel had been kept on this second list – despite the three Ford brothers being against it – for some reason, perhaps because it was the originally suggested name.  When the group came to the name “Edsel,” Breech firmly said, “Let’s call it that.”  Breech added that since there were going to be four models of the E-Car, the four favorite names – Corsair, Citation, Pacer, and Ranger – could still be used as sub-names.

Brooks writes that Foote, Cone & Belding presumably didn’t react well to the chosen name, “Edsel,” after their exhaustive research to come up with the best possible names.  But the Special Products Division had an even worse reaction.  However, there were a few, including Krafve, would didn’t object to the name.

Krafve was named Vice-President of the Ford Motor Company and General Manager, Edsel Division.  Meanwhile, Edsels were being road-tested.  Brown and other designers were already working on the subsequent year’s model.  A new set of retail dealers was already being put together.  Foote, Cone & Belding was hard at work on strategies for advertising and selling Edsels.  In fact, Fairfax M. Cone himself was leading this effort.

Cone decided to use Wallace’s idea of “the smart car for the younger executive or professional family on its way up.”  But Cone amended it to: “the smart car for the younger middle-income family or professional family on its way up.”  Cone was apparently quite confident, since he described his advertising ideas for the Edsel to some reporters.  Brooks notes with amusement:

Like a chess master that has no doubt that he will win, he could afford to explicate the brilliance of his moves even as he made them.

Normally, a large manufacturer launches a new car through dealers already handling some of its other makes.  But Krafve got permission to go all-out on the Edsel.  He could contact dealers for other car manufacturers and even dealers for other divisions of Ford.  Krafve set a goal of signing up 1,200 dealers – who had good sales records – by September 4, 1957.

Brooks remarks that Krafve had set a high goal, since a dealer’s decision to sell a new car is major.  Dealers typically have one hundred thousand dollars – more than 8x that in 2019 dollars – invested in their dealerships.

J. C. (Larry) Doyle, second to Krafve, led the Edsel sales effort.  Doyle had been with Ford for 40 years.  Brooks records that Doyle was somewhat of a maverick in his field.  He was kind and considerate, and he didn’t put much stock in the psychological studies of car buyers.  But he knew how to sell cars, which is why he was called on for the Edsel campaign.

Doyle put Edsels into a few dealerships, but kept them hidden from view.  Then he went about recruiting top dealers.  Many dealers were curious about what the Edsel looked like.  But Doyle’s group would only show dealers the car if they listened to a one-hour pitch.  This approach worked.  It seems that quite a few dealers were so convinced by the pitch that they signed up without even looking at the car in any detail.

C. Gayle Warnock, director of public relations at Ford, was in charge of keeping public interest in the Edsel – which was already high – as strong as possible.  Warnock told Krafve that public interest might be too strong, to the extent that people would be disappointed when they discovered that the Edsel was a car.  Brooks:

It was agreed that the safest way to tread the tightrope between overplaying and underplaying the Edsel would be to say nothing about the car as a whole but to reveal its individual charms a little at a time – a sort of automotive strip tease…

Brooks continues:

That summer, too, was a time of speechmaking by an Edsel foursome consisting of Krafve, Doyle, J. Emmet Judge, who was Edsel’s director of merchandise and product planning, and Robert F. G. Copeland, its assistant general sales manager for advertising, sales promotion, and training.  Ranging separately up and down and across the nation, the four orators moved around so fast and so tirelessly, that Warnock, lest he lost track of them, took to indicating their whereabouts with colored pins on a map in his office.  ‘Let’s see, Krafve goes from Atlanta to New Orleans, Doyle from Council Bluffs to Salt Lake City,’ Warnock would muse of a morning in Dearborn, sipping his second cup of coffee and then getting up to yank the pins out and jab them in again.

Needless to say, this was by far the largest advertising campaign ever conducted by Ford.  This included a three-day press preview, with 250 reporters from all over the country.  On one afternoon, the press were taken to the track to see stunt drivers in Edsels doing all kinds of tricks.  Brooks quotes the Foote, Cone man:

‘You looked over this green Michigan hill, and there were those glorious Edsels, performing gloriously in unison.  It was beautiful.  It was like the Rockettes.  It was exciting.  Morale was high.’

Brooks then writes about the advertising on September 3 – “E-Day-minus-one”:

The tone for Edsel Day’s blizzard of publicity was set by an ad, published in newspapers all over the country, in which the Edsel shared the spotlight with the Ford Company’s President Ford and Chairman Breech.  In the ad, Ford looked like a dignified young father, Breech like a dignified gentleman holding a full house against a possible straight, the Edsel just looked like an Edsel.  The accompanying text declared that the decision to produce the car had been ‘based on what we knew, guessed, felt, believed, suspected – about you,’ and added, ‘YOU are the reason behind the Edsel.’  The tone was calm and confident.  There did not seem to be much room for doubt about the reality of that full house.

The interior of the Edsel, as predicted by Krafve, had an almost absurd number of push-buttons.

The two larger models – the Corsair and the Citation – were 219 inches long, two inches longer than the biggest of the Oldsmobiles.  And they were 80 inches wide, “or about as wide as passenger cars ever get,” notes Brooks.  Each had 345 horsepower, making it more powerful than any other American car at the time of launching.

Brooks records that the car received mixed press after it was launched.  In January, 1958, Consumer Reports wrote:

The Edsel has no important basic advantage over other brands.  The car is almost entirely conventional in construction…

Three months later, Consumer Reports wrote:

[The Edsel] is more uselessly overpowered… more gadget bedecked, more hung with expensive accessories than any other car in its price class.

This report gave the Corsair and the Citation the bottom position in its competitive ratings.

Brooks says there were several factors in the downfall of the Edsel.  It wasn’t just that the design fell short, nor was it simply that the company relied too much on psychological research.  For one, many of the early Edsels suffered from a surprising variety of imperfections.  It turned out that only about half the early Edsels functioned properly.

Brooks recounts:

For the first ten days of October, nine of which were business days, there were only 2,751 deliveries – an average of just over three hundred cars a day.  In order to sell the 200,000 cars per year that would make the Edsel operation profitable the Ford Motor Company would have to move an average of between six and seven hundred each business day – a good many more than three hundred a day.  On the night of Sunday, October 13th, Ford put on a mammoth television spectacular for Edsel, pre-empting the time ordinarily allotted to the Ed Sullivan show, but though the program cost $400,000 and starred Bing Crosby and Frank Sinatra, it failed to cause any sharp spurt in sales.  Now it was obvious that things were not going well at all.

Among the former executives of the Edsel Division, opinions differ as to the exact moment when the portents of doom became unmistakable… The obvious sacrificial victim was Brown, whose stock had gone through the roof at the time of the regally accoladed debut of his design, in August, 1955.  Now, without having done anything further, for either better or worse, the poor fellow became the company scapegoat…

Ford re-committed to selling the Edsel in virtually every way that it could.  Sales eventually increased, but not nearly enough.  Ultimately, the company had to stop production.  The net loss for Ford was roughly $350 million.

Krafve rejects that the Edsel failed due to a poor choice of the name.  He maintains that it was a mistake of timing.  Had they produced the car two years earlier, when medium-sized cars were still highly popular, the Edsel would have been a success.  Brown agrees with Krafve that it was a mistake of timing.

Doyle says it was a buyers’ strike.  He claims not to understand at all why the American public suddenly switched its taste from medium-sized cars to smaller-sized cars.

Wallace argued that the Russian launch of the sputnik had caused many Americans to start viewing Detroit products as bad, especially medium-priced cars.

Brooks concludes by noting that Ford did not get hurt by this setback, nor did the majority of people associated with the Edsel.  In 1958, net income per share dropped from $5.40 to $2.12, and Ford stock dropped from a 1957 high of $60 to a low of $40.  However, by 1959, net income per-share jumped to $8.24 and the stock hit $90.

The Ford executives associated with the Edsel advanced in their careers, for the most part.  Moreover, writes Brooks:

The subsequent euphoria of these former Edsel men did not stem entirely from the fact of their economic survival;  they appear to have been enriched spiritually.  They are inclined to speak of their Edsel experience – except for those still with Ford, who are inclined to speak of it as little as possible – with the verve and garrulity of old comrades-in-arms hashing over their most thrilling campaign.

 

A REASONABLE AMOUNT OF TIME

Brooks:

Most nineteenth-century American fortunes were enlarged by, if they were not actually founded on, the practice of insider trading…

Not until 1934 did Congress pass the Securities Exchange Act, which forbids insider trading.  Later, a 1942 rule 10B-5 held that no stock trader could “make any untrue statement of a material fact or… omit to state a material fact.”  However, observes Brooks, this rule had basically been overlooked for the subsequent couple of decades.  It was argued that insiders needed the incentive of being able to profit in order to bring forth their best efforts.  Further, some authorities said that insider trading helped the markets function more smoothly.  Finally, it was held that most stock traders “possess and conceal information of one sort or another.”

In short, the S.E.C. seemed to be refraining from doing anything regarding insider trading.  But this changed when a civil complaint was made against Texas Gulf Sulphur Company.  The case was tried in the United States District Court in Foley Square May 9 to June 21, 1966.  The presiding judge was Dudley J. Bonsal, says Brooks, who remarked at one point, “I guess we all agree that we are plowing new ground here to some extent.”

In March 1959, Texas Gulf, a New York-based company and the world’s leader producer of sulphur, began conducting aerial surveys over a vast area of eastern Canada.  They weren’t looking for sulphur or gold, but for sulphides – sulphur in combination with other useful minerals such as zinc and copper.  Texas Gulf wanted to diversify its production.

These surveys took place over two years.  Many areas of interest were noted.  The company concluded that several hundred areas were most promising, including a segment called Kidd-55, which was fifteen miles north of Timmins, Ontario, an old gold-mining town several hundred miles northwest of Toronto.

The first challenge was to get title to do exploratory drilling on Kidd-55.  It wasn’t until June, 1963, that Texas Gulf was able to begin exploring on the northeast quarter of Kidd-55.  After Texas Gulf engineer, Richard H. Clayton, completed a ground electromagnetic survey and was convinced the area had potential, the company decided to drill.  Drilling began on November 8.  Brooks writes:

The man in charge of the drilling crew was a young Texas Gulf geologist named Kenneth Darke, a cigar smoker with a rakish gleam in his eye, who looked a good deal more like the traditional notion of a mining prospector than that of the organization man that he was.

A cylindrical sample an inch and a quarter in diameter was brought out of the earth.  Darke studied it critically inch by inch using only his eyes and his knowledge.  On November 10, Darke telephoned his immediate superior, Walter Holyk, chief geologist of Texas Gulf, to report the findings at that point.

The same night, Holyk called his superior, Richard D. Mollison, a vice president of Texas Gulf.  Mollison then called his superior, Charles F. Fogarty, executive vice president and the No. 2 man at the company.  Further reports were made the next day.  Soon Holyk, Mollison, and Fogarty decided to travel to Kidd-55 to take a look for themselves.

By November 12, Holyk was on site helping Darke examine samples.  Holyk was a Canadian in his forties with a doctorate in geology from MIT.  The weather had turned bad.  Also, much of the stuff came up covered in dirt and grease, and had to be washed with gasoline.  Nonetheless, Holyk arrived at an initial estimate of the core’s content.  There seemed to be average copper content of 1.15% and average zinc content of 8.64%.  If true and if it was not just in one narrow area, this appeared to be a huge discovery.  Brooks:

Getting title would take time if it were possible at all, but meanwhile there were several steps that the company could and did take.  The drill rig was moved away from the site of the test hole.  Cut saplings were stuck in the ground around the hole, to restore the appearance of the place to a semblance of its natural state.  A second test hole was drilled, as ostentatiously as possible, some distance away, at a place where a barren core was expected – and found.  All of these camouflage measures, which were in conformity with long-established practice among miners who suspect that they have made a strike, were supplemented by an order from Texas Gulf’s president, Claude O. Stephens, that no one outside the actual exploration group, even within the company, should be told what had been found.  Late in November, the core was shipped off, in sections, to the Union Assay Office in Salt Lake City for scientific analysis of its contents.  And meanwhile, of course, Texas Gulf began discreetly putting out feelers for the purchase of the rest of Kidd-55.

Brooks adds:

And meanwhile other measures, which may or may not have been related to the events of north of Timmins, were being taken.  On November 12th, Fogarty bought three hundred shares of Texas Gulf stock;  on the 15th he added seven hundred more shares, on November 19th five hundred more, and on November 26th two hundred more.  Clayton bought two hundred on the 15th, Mollison one hundred on the same day; and Mrs. Holyk bought fifty on the 29th and one hundred more on December 10th.  But these purchases, as things turned out, were only the harbingers of a period of apparently intense affection for Texas Gulf stock among certain of its officers and employees, and even some of their friends.

The results of the sample test confirmed Holyk’s estimates.  Also found were 3.94 ounces of silver per ton.  In late December, while in the Washington, D.C. area, Darke recommended Texas Gulf stock to a girl he knew there and her mother.  They later became known as “tippees,” while a few people they later told naturally became “sub-tippees.”  Between December 30 and February 17, Darke’s tippees and sub-tippees purchased 2,100 shares of Texas Gulf stock and also bought calls on another 1,500 shares.

In the first three months of 1964, Darke bought 300 shares of Texas Gulf stock, purchased calls on 3,000 more shares, and added several more persons to his burgeoning list of tippees.  Holyk and his wife bought a large number of calls on Texas Gulf stock.  They’d hardly heard of calls before, but calls “were getting to be quite the rage in Texas Gulf circles.”

Finally in the spring, Texas Gulf had the drilling rights it needed and was ready to proceed.  Brooks:

After a final burst of purchases by Darke, his tippees, and his sub-tippees on March 30th and 31st (among them all, six hundred shares and calls on 5,100 more shares for the two days), drilling was resumed in the still-frozen muskeg at Kidd-55, with Holyk and Darke both on the site this time.

While the crew stayed on site, the geologists almost daily made the fifteen-mile trek to Simmins.  With seven-foot snowdrifts, the trip took three and a half to four hours.

At some stage – later a matter of dispute – Texas Gulf realized that it had a workable mine of large proportions.  Vice President Mollison arrived on site for a day.  Brooks:

But before going he issued instructions for the drilling of a mill test hole, which would produce a relatively large core that could be used to determine the amenability of the mineral material to routine mill processing.  Normally, a mill test hole is not drilled until a workable mine is believed to exist.  And so it may have been in this case;  two S.E.C. mining experts were to insist later, against contrary opinions of experts for the defense, that by the time Mollison gave his order, Texas Gulf had information on the basis of which it could have calculated that the ore reserves at Kidd-55 had a gross assay value of at least two hundred million dollars.

Brooks notes:

The famous Canadian mining grapevine was humming by now, and in retrospect the wonder is that it had been relatively quiet for so long.

On April 10, President Stephens had become concerned enough to ask a senior member of the board – Thomas S. Lamont of Morgan fame – whether Texas Gulf should issue a statement.  Lamont told him he could wait until the reports were published in U.S. papers, but then he should issue a statement.

The following day, April 11, the reports poured forth in the U.S. papers.  The Herald Tribune called it “the biggest ore strike since gold was discovered more than 60 years ago in Canada.”  Stephens instructed Fogarty to begin preparing a statement to be issued on Monday, April 13.  Meanwhile, the estimated value of the mine seemed to be increasing by the hour as more and more copper and zinc ore was brought to the surface.  Brooks writes:

However, Fogarty did not communicate with Timmins after Friday night, so the statement that he and his colleagues issued to the press on Sunday afternoon was not based on the most up-to-the-minute information.  Whether because of that or for some other reason, the statement did not convey the idea that Texas Gulf thought it had a new Comstock Lode.  Characterizing the published reports as exaggerated and unreliable, it admitted that recent drilling on ‘one property near Timmins’ had led to ‘preliminary indications that more drilling would be required for proper evaluation of the prospect;’  went on to say that ‘the drilling done to date has not been conclusive;’  and then, putting the same thought in what can hardly be called another way, added that ‘the work done to date has not been sufficient to reach definitive conclusions.’

The wording of this press release was sufficient to put a damper on any expectations that may have arisen due to the newspaper stories the previous Friday.  Texas Gulf stock had gone from around $17 the previous November to around $30 just before the stories.  On Monday, the stock went to $32, but then came back down and even dipped below $29 in the subsequent two days.

Meanwhile, at Kidd-55, Mollison, Holyk, and Darke talked with a visiting reporter who had been shown around the place.  Brooks:

The things they told the reporter make it clear, in retrospect, that whatever the drafters of the release may have believed on Sunday, the men at Kidd-55 knew on Monday that they had a mine and a big one.  However, the world was not to know it, or at least not from that source, until Thursday morning, when the next issue of the Miner would appear in subscribers’ mail and on newstands.

Mollison and Holyk flew to Montreal Tuesday evening for the annual convention of the Canadian Institute of Mining and Metallurgy.  They had arranged for that Wednesday, in the company of the Minister of Mines of the Province of Ontario and his deputy, to attend the convention.  En route, they briefed the minister on Kidd-55.  The minister decided he wanted to make an announcement as soon as possible.  Mollison helped the minister draft the statement.

According to the copy Mollison kept, the announcement stated that “the information now in hand… gives the company confidence to allow me to announce that Texas Gulf Sulphur has a mineable body of zinc, copper, and silver ore of substantial dimensions that will be developed and brought to production as soon as possible.”  Mollison and Holyk believed that the minister would make the announcement that evening.  But for some reason, the minister didn’t.

Texas Gulf was to have a board of directors meeting that Thursday.  Since better and better news had been coming in from Kidd-55, the company officers decided they should write a new press release, to be issued after the Thursday morning board meeting.  This statement was based on the very latest information and it read, in part, “Texas Gulf Sulphur Company has made a major strike of zinc, copper, and silver in the Timmins area… Seven drill holes are now essentially complete and indicate an ore body of at least 800 feet in length, 300 feet in width, and having a vertical depth of more than 800 feet.  This is a major discovery.  The preliminary data indicate a reserve of more than 25 million tons of ore.”

The statement also noted that “considerably more data has been accumulated,” in order to explain the difference between this statement and the previous one.  Indeed, the value of the ore was not the two hundred million dollars alleged to have been estimable a week earlier, but many times that.

The same day, engineer Clayton and company secretary Crawford bought 200 and 300 shares, respectively.  The next morning, Crawford doubled his order.

The directors’ meeting ended at ten o’clock.  Then 22 reporters entered the room.  President Stephens read the new press release.  Most reporters rushed out before he was finished to report the news.

The actions of two Texas Gulf directors, Coates and Lamont, during the next half hour were later to lead to the most controversial part of the S.E.C.’s complaint.  As Brooks writes, the essence of the controversy was timing.  The Texas Gulf news was released by the Dow Jones News Service, the well-known spot-news for investors.  In fact, a piece of news is considered to be public the moment it crosses “the broad tape.”

The morning of April 16, 1964, a Dow Jones reporter was among those who attended the Texas Gulf press conference.  He left early and called in the news around 10:10 or 10:15, according to his recollection.  Normally, a news item this important would be printed on the Dow Jones machines two or three minutes after being phoned in.  But for reasons unknown, the Texas Gulf story did not appear on the tape until 10:54.  This delay was left unexplained during the trial based on irrelevance, says Brooks.

Coates, the Texan, around the end of the press conference, called his son-in-law, H. Fred Haemisegger, a stockbroker in Houston.  Coates told Haemisegger about the Texas Gulf discovery, also saying that he waited to call until “after the public announcement” because he was “too old to get in trouble with the S.E.C.”  Coates next placed an order for 2,000 shares of Texas Gulf stock for four family trusts.  He was a trustee, but not a beneficiary.  The stock had opened at $30.  Haemisegger, by acting quickly, was able to buy a bit over $31.

Lamont hung around the press conference area for 20 minutes or so.  He recounts that he “listened to chatter” and “slapped people on the back.”  Then at 10:39 or 10:40, he called a friend at Morgan Guaranty Trust Company – Longstreet Hinton, the bank’s executive vice president and head of its trust department.  Hinton had asked Lamont earlier in the week if he knew anything about the rumors of an ore discovery made by Texas Gulf.  Lamont had said no then.

But during this phone call, Lamont told Hinton that he had some news now.  Hinton asked whether it was good.  Lamont replied either “pretty good” or “very good.”  (Brooks notes that they mean the same thing in this context.)  Hinton immediately called the bank’s trading department, got a quote on Texas Gulf, and placed an order for 3,000 shares for the account of the Nassau Hospital, of which he was treasurer.  Hinton never bothered to look at the tape – despite being advised to do so by Lamont – because Hinton felt he already had the information he needed.  (Lamont didn’t know about the inexplicable forty minute delay before the Texas Gulf news appeared on the tape.)

Then Hinton went to the office of the Morgan Guaranty officer in charge of pension trusts.  Hinton recommended buying Texas Gulf.  In less than half an hour, the bank had ordered 7,000 shares for its pension fund and profit-sharing account.

An hour after that – at 12:33 – Lamont purchased 3,000 shares for himself and his family, paying $34 1/2 for them.  The stock closed above $36.  It hit a high of over $58 later that month.  Brooks:

…and by the end of 1966, when commercial production of ore was at last underway at Kidd-55 and the enormous new mine was expected to account for one-tenth of Canada’s total annual production of copper and one-quarter of its total annual production of zinc, the stock was selling at over 100.  Anyone who had bought Texas Gulf between November 12th, 1963 and the morning (or even the lunch hour) of April 16th, 1964 had therefore at least tripled his money.

Brooks then introduces the trial:

Perhaps the most arresting aspect of the Texas Gulf trial – apart from the fact that a trial was taking place at all – was the vividness and variety of the defendants who came before Judge Bonsal, ranging as they did from a hot-eyed mining prospector like Clayton (a genuine Welchman with a degree in mining from the University of Cardiff) through vigorous and harried corporate nabobs like Fogarty and Stephens to a Texas wheeler-dealer like Coates and a polished Brahmin of finance like Lamont.

Darke did not appear at the trial, claiming his Canadian nationality.  Brooks continues:

The S.E.C., after its counsel, Frank E. Kennamer Jr. had announced his intention to “drag to light and pillory the misconduct of these defendants,” asked the court to issue a permanent injunction forbidding Fogarty, Mollison, Clayton, Holyk, Darke, Crawford, and several other corporate insiders who had bought stock or calls between November 8th, 1963 and April 15th, 1964, from ever again “engaging in any act… which operates or would operate as a fraud or deceit upon any person in connection with purchase or sale of securities”;  further – and here it was breaking entirely new ground – it prayed that the court order the defendants to make restitution to the persons they had allegedly defrauded by buying stock or calls from them on the basis of inside information.  The S.E.C. also charged that the pessimistic April 12th press release was deliberately deceptive, and asked that because of it Texas Gulf be enjoined from “making any untrue statement of material fact or omitting to state a material fact.”  Apart from any question of loss of corporate face, the nub of the matter here lay in the fact that such a judgment, if granted, might well open the way for legal action against the company by any stockholder who had sold his Texas Gulf stock to anybody in the interim between the first press release and the second one, and since the shares that had changed hands during that period had run into the millions, it was a nub indeed.

Regarding the November purchases, the defense argued that a workable mine was far from a sure thing based only on the first drill hole.  Some even argued that the hole could have turned out to be a liability rather than an asset for Texas Gulf, based on what was known then.  The people who bought stock or calls during the winter claimed that the hole had little or nothing to do with their decision.  They stated that they thought Texas Gulf was a good investment in general.  Clayton said his sudden appearance as a large investor was because he had just married a well-to-do wife.  Brooks:

The S.E.C. countered with its own parade of experts, maintaining that the nature of the first core had been such as to make the existence of a rich mine an overwhelming probability, and that therefore those privy to the facts about it had possessed a material fact.

The S.E.C. also made much of the fact that Fogarty based the initial press release on information that was two days old.  The defense countered that the company had been in a sensitive position.  If it had issued an optimistic report that later turned out to be false, it could well be accused of fraud for that.

Judge Bonsal concluded that the definition of materiality must be conservative.  He therefore decided that up until April 9th, when three converging drill holes positively established the three-dimensionality of the ore deposit, material information had not been in hand.  Therefore, the decisions of insiders to buy stock before that date, even if based on initial drilling results, were legal “educated guesses.”

Case was thus dismissed against all educated guessers who had bought stock or calls, or recommended others do so, before the evening of April 9th.  Brooks:

With Clayton and Crawford, who had been so injudicious as to buy or order stock on April 15th, it was another matter.  The judge found no evidence that they had intended to deceive or defraud anyone, but they had made their purchases with the full knowledge that a great mine had been found and that it would be announced the next day – in short, with material private information in hand.  Therefore they were found to have violated Rule 10B-5, and in due time would presumably be enjoined from doing such a thing again and made to offer restitution to the persons they bought their April 15th shares from – assuming, of course, that such persons can be found…

On the matter of the April 12th press release, the judge found that it was not false or misleading.

Still to be settled was the matter of Coates and Lamont making their purchases.  The question was when it can be said that the information has officially been made public.  This was the most important issue and would likely set a legal precedent.

The S.E.C. argued that the actions of Coates and Lamont were illegal because they occurred before the ore strike news had crossed the Dow Jones broad tape.  The S.E.C. argued, furthermore, that even if Coates and Lamont had acted after the “official” announcement, it still would be illegal unless enough time had passed so that those who hadn’t attended the press conference, or even those who hadn’t seen the initial news cross the broad tape, had enough time to absorb the information.

Defense argued first that Coates and Lamont had every reason to believe that the news was already out, since Stephens said it had been released by the Ontario Minister of Mines the previous evening.  So Coates and Lamont acted in good faith.  Second, counsel argued that for all practical purposes, the news was out, via osmosis and The Northern Miner.  Brokerage offices and the Stock Exchange had been buzzing all morning.  Lamont’s lawyers also argued that Lamont had merely told Hinton to look at the tape, not to buy any stock.  Defense argued that the S.E.C. was asking the court to write new rules and then apply them retroactively, while the plaintiff was merely asking that an old rule 10B-5, be applied broadly.

As for Lamont’s waiting for two hours, until 12:33, before buying stock for himself, the S.E.C. took issue, as Brooks records:

‘It is the Commission’s position that even after corporate information has been published in the news media, insiders, are still under a duty to refrain from securities transactions until there had elapsed a reasonable amount of time in which the securities industry, the shareholders, and the investing public can evaluate the development and make informed investment decisions… Insiders must wait at least until the information is likely to have reached the average investor who follows the market and he has had some opportunity to consider it.’

In the Texas Gulf case, the S.E.C. argued that one hour and thirty-nine minutes was not “a reasonable amount of time.”  What, then, is “a reasonable amount of time,” the S.E.C. was asked?  The S.E.C.’s counsel, Kennamer, said it “would vary from case to case.”  Kennamer added that it would be “a nearly impossible task to formulate a rigid set of rules that would apply in all situations of this sort.”

Brooks sums it up with a hint of irony:

Therefore, in the S.E.C.’s canon, the only way an insider could find out whether he had waited long enough before buying his company’s stock was by being hauled into court and seeing what the judge would decide.

Judge Bonsal rejected this argument by the S.E.C.  Moreover, he took a narrower view that, based on legal precedent, the key moment was when the press release was read.  The judge admitted that a better rule might be formulated according to which insiders had to wait at least some amount time after the initial press release so that other investors could absorb it.  However, he didn’t think he should write such a rule.  Nor should this matter be left up to the judge on a case-by-base basis.  Thus, the complaints against Coates and Lamont were dismissed.

The S.E.C. appealed all the dismissals.  Brooks concludes:

…in August, 1968, the U.S. Court of Appeals for the Second Circuit handed down a decision which flatly reversed Judge Bonsal’s findings on just about every score except the findings against Crawford and Clayton, which were affirmed.  The Appeals Court found that the original November drill hole had provided material evidence of a valuable ore deposit, and that therefore Fogarty, Mollison, Darke, Holyk, and all other insiders who had bought Texas Gulf stock or calls on it during the winter were guilty of violations of the law;  that the gloomy April 12th press release had been ambiguous and perhaps misleading;  and that Coates had improperly and illegally jumped the gun in placing his orders right after the April 16th press conference.  Only Lamont – the charges against whom had been dropped following his death shortly after the lower court decision – and a Texas Gulf office manager, John Murray, remained exonerated.

 

XEROX XEROX XEROX XEROX

There was no economical and practical way of making copies until after 1950.  Brooks writes that the 1950’s were the pioneering years for mechanized office copying.  Although people were starting to show a compulsion to make copies, the early copying machines suffered from a number of problems.  Brooks:

…What was needed for the compulsion to flower into a mania was a technological breakthrough, and the breakthrough came at the turn of the decade with the advent of a machine that worked on a new principle, known as xerography, and was able to make dry, good-quality, permanent copies on ordinary paper with a minimum of trouble.  The effect was immediate.  Largely as a result of xerography, the estimated number of copies (as opposed to duplicates) made annually in the United States sprang from some twenty million in the mid-fifties to nine and a half billion in 1964, and to fourteen billion in 1966 – not to mention billions more in Europe, Asia, and Latin America.  More than that, the attitude of educators towards printed textbooks and of business people toward written communication underwent a discernable change;  avant-garde philosophers took to hailing xerography as a revolution comparable in importance to the invention of the wheel;  and coin-operated copy machines began turning up in candy stores and beauty parlors…

The company responsible for the great breakthrough and the one on whose machines the majority of these billions of copies were made was of course, the Xerox Corporation, of Rochester, New York.  As a result, it became the most spectacular big-business success of the nineteen-sixties.  In 1959, the year the company – then called Haloid Xerox, Inc. – introduced its first automatic xerographic office copier, its sales were thirty-three million dollars.  In 1961, they were sixty-six million, in 1963 a hundred and seventy-six million, and in 1966 over half a billion.

The company was extremely profitable.  It ranked two hundred and seventy-first in Fortune’s ranking in 1967.  However, in 1966 the company ranked sixty-third in net profits and probably ninth in the ratio of profits to sales and fifteenth in terms of market value.  Brooks continues:

…Indeed, the enthusiasm the investing public showed for Xerox made its shares the stock market Golconda of the sixties.  Anyone who bought its stock toward the end of 1959 and held on to it until early 1967 would have found his holding worth about sixty-six times its original price, and anyone who was really fore-sighted and bought Haloid in 1955 would have seen his original investment grow – one might almost say miraculously – a hundred and eighty times.  Not surprisingly, a covey of “Xerox millionaires” sprang up – several hundred of them all told, most of whom either lived in the Rochester area or had come from there.

The Haloid company was started in Rochester in 1906.  It manufactured photographic papers.  It survived OK.  But after the Second World War, due to an increase in competition and labor costs, the company was looking for new products.

More than a decade earlier, in 1938, an obscure thirty-two year-old inventor, Chester F. Carlson, was spending his spare time trying to invent an office copying machine.  Carlson had a degree in physics from the California Institute of Technology.  Carlson had hired Otto Kornei, a German refugee physicist, to help him.  Their initial copying machine was unwieldy and produced much smoke and stench.  Brooks:

The process, which Carlson called electrophotography, had – and has – five basic steps:  sensitizing a photoconductive surface to light by giving it an electrostatic charge (for example, by rubbing it with fur);  exposing this surface to a written page to form an electrostatic image;  developing the latest image by dusting the surface with a powder that will adhere only to the charged areas;  transferring the image to some sort of paper;  and fixing the image by the application of heat.

Although each individual step was already used in other technologies, this particular combination of steps was new.  Carlson carefully patented the process and began trying to sell it.  Over the ensuing five years, Carlson tried to sell the rights to every important office-equipment company in the country.  He was turned down every time.  In 1944, Carlson finally convinced Battelle Memorial Institute to conduct further development work on the process in exchange for three-quarters of any future royalties.

In 1946, various people at Haloid, including Joseph C. Wilson – who was about to become president – had noticed the work that Battelle was doing.  Wilson asked a friend of his, Sol M. Linowitz, a smart, public-spirited lawyer just back from service in the Navy, to research the work at Battelle as a “one-shot” job.  The result was an agreement giving Haloid the rights to the Carlson process in exchange for royalties for Battelle and Carlson.

At one point in the research and development process, the Haloid people got so discouraged that they considered selling most of their xerography rights to International Business Machines.  The research process became quite costly.  But Haloid committed itself to seeing it through.  It took full title of the Carlson process and assumed the full cost of development in exchange for shares in Haloid (for Battelle and Carlson).  Brooks:

…The cost was staggering.  Between 1947 and 1960, Haloid spent about seventy-five million dollars [over $800 million in 2019 dollars] on research in xerography, or about twice what it earned from its regular operations during that period;  the balance was raised through borrowing and through the wholesale issuance of common stock to anyone who was kind, reckless, or prescient enough to take it.  The University of Rochester, partly out of interest in a struggling local industry, bought an enormous quantity for its endowment fund at a price that subsequently, because of stock splits, amounted to fifty cents a share.  ‘Please don’t be mad at us if we have to sell our Haloid stock in a couple of years to cut our losses on it,’ a university official nervously warned Wilson.  Wilson promised not to be mad.  Meanwhile, he and other executives of the company took most of their pay in the form of stock, and some of them went as far as to put up their savings and the mortgages on their houses to help the cause along.

In 1961, the company changed its name to Xerox Corporation.  One unusual aspect to the story is that Xerox became rather public-minded.  Brooks quotes Wilson:

‘To set high goals, to have almost unattainable aspirations, to imbue people with the belief that they can be achieved – these are as important as the balance sheet, perhaps more so.’

This rhetoric is not uncommon.  But Xerox followed through by donating one and a half percent of its profits to educational and charitable institutions in 1965-1966.  In 1966, Xerox committed itself to the “one-per-cent program,” also called the Cleveland Plan, according to which the company gives one percent of its pre-tax income annually to educational institutions, apart from any other charitable activities.

Furthermore, President Wilson said in 1964, “The corporation cannot refuse to take a stand on public issues of major concern.”  As Brooks observes, this is “heresy” for a business because it could alienate customers or potential customers.  Xerox’s chief stand was in favor of the United Nations.  Brooks:

Early in 1964, the company decided to spend four million dollars – a year’s advertising budget – on underwriting a series of network-television programs dealing with the U.N., the programs to be unaccompanied by commercials or any other identification of Xerox apart from a statement at the beginning and end of each that Xerox had paid for it.

Xerox was inundated with letters opposing the company’s support of the U.N.  Many said that the U.N. charter had been written by American Communists and that the U.N. was an instrument for depriving Americans of their Constitutional rights.  Although only a few of these letters came from the John Birch Society, it turned out later that most of the letters were part of a meticulously planned Birch campaign.  Xerox officers and directors were not intimidated.  The U.N. series appeared in 1965 and was widely praised.

Furthermore, Xerox consistently committed itself to informing the users of its copiers of their legal responsibilities.  It took this stand despite their commercial interest.

Brooks visited Xerox in order to talk with some of its people.  First he spoke with Dr. Dessauer, a German-born engineer who had been in charge of the company’s research and engineering since 1938.  It was Dessauer who first brought Carlson’s invention to the attention of Joseph Wilson.  Brooks noticed a greeting card from fellow employees calling Dessauer the “Wizard.”

Dr. Dessauer told Brooks about the old days.  Dessauer said money was the main problem.  Many team members gambled heavily on the xerox project.  Dessauer himself mortgaged his house.  Early on, team members would often say the damn thing would never work.  Even if it did work, the marketing people said there was only a market for a few thousand of the machines.

Next Brooks spoke with Dr. Harold E. Clark, who had been a professor of physics before coming to Haloid in 1949.  Dr. Clark was in charge of the xerography-development program under Dr. Dessauer.  Dr. Clark told Brooks that Chet Carlson’s invention was amazing.  Also, no one else invented something similar at the same time, unlike the many simultaneous discoveries in scientific history.  The only problem, said Dr. Clark, was that it wasn’t a good product.

The main trouble was that Carlson’s photoconductive surface, which was coated with sulphur, lost its qualities after it had made a few copies and became useless.  Acting on a hunch unsupported by scientific theory, the Battelle researchers tried adding to the sulphur a small quantity of selenium, a non-metallic element previously used chiefly in electrical resistors and as a coloring material to redden glass.  The selenium-and-sulphur surface worked a little better than the all-sulphur one, so the Battelle men tried adding a little more selenium.  More improvement.  They gradually kept increasing the percentage until they had a surface consisting entirely of selenium – no sulphur.  That one worked best of all, and thus it was found, backhandedly, that selenium and selenium alone could make xerography practical.

Dr. Clark went on to tell Brooks that they basically patented one of the elements, of which there are not many more than one hundred.  What is more, they still don’t understand how it works.  There are no memory effects – no traces of previous copies are left on the selenium drum.  A selenium-coated drum in the lab can last a million processes, or theoretically an infinite number.  They don’t understand why.  Dr. Clark concluded that they combined “Yankee tinkering and scientific inquiry.”

Brooks spoke with Linowitz, who only had a few minutes because he had just been appointed U.S. Ambassador to the Organization of American States.  Linowitz told him:

…the qualities that made for the company’s success were idealism, tenacity, the courage to take risks, and enthusiasm.

Joseph Wilson told Brooks that his second major had been English literature.  He thought he would be a teacher or work in administration at a university.  Somehow he ended up at Harvard Business School, where he was a top student.  After that, he joined Haloid, the family business, something he’d never planned on doing.

Regarding the company’s support of the U.N., Wilson explained that world cooperation was the company’s business, because without it there would be no world and thus no business.  He went on to explain that elections were not the company’s business.  But university education, civil rights, and employment of African-Americans were their business, to name just a few examples.  So far, at least, Wilson said there hadn’t been a conflict between their civic duties and good business.  But if such a conflict arose, he hoped that the company would honor its civic responsibilities.

 

MAKING THE CUSTOMERS WHOLE

On November 19th, 1963, the Stock Exchange became aware that two of its member firms – J. R. Williston & Beane, Inc., and Ira Haupt & Co. – were in serious financial trouble.  This later became a crisis that was made worse by the assassination of JFK on November 22, 1963.  Brooks:

It was the sudden souring of a speculation that these two firms (along with various brokers not members of the Stock Exchange) had become involved in on behalf of a single customer – the Allied Crude Vegetable Oil & Refining Co., of Bayonne, New Jersey.  The speculation was in contracts to buy vast quantities of cotton-seed oil and soybean oil for future delivery.

Brooks then writes:

On the two previous business days – Friday the fifteenth and Monday the eighteenth – the prices had dropped an average of a little less than a cent and a half per pound, and as a result Haupt had demanded that Allied put up about fifteen million dollars in cash to keep the account seaworthy.  Allied had declined to do this, so Haupt – like any broker when a customer operating on credit has defaulted – was faced with the necessity of selling out the Allied contracts to get back what it could of its advances.  The suicidal extent of the risk that Haupt had undertaken is further indicated by the fact that while the firm’s capital in early November had amounted to only about eight million dollars, it had borrowed enough money to supply a single customer – Allied – with some thirty-seven million dollars to finance the oil speculations.  Worse still, as things turned out it had accepted as collateral for some of these advances enormous amounts of actual cottonseed oil and soybean oil from Allied’s inventory, the presence of which in tanks at Bayonne was attested to by warehouse receipts stating the precise amount and kind of oil on hand.  Haupt had borrowed the money it supplied Allied from various banks, passing along most of the warehouse receipts to the banks as collateral.  All this would have been well and good if it had not developed later that many of the warehouse receipts were forged, that much of the oil they attested to was not, and probably never had been, in Bayonne, and that Allied’s President, Anthony De Angelis (who was later sent to jail on a whole parcel of charges), had apparently pulled off the biggest commercial fraud since that of Ivar Kreuger, the match king.

What began to emerge as the main issue was that Haupt had about twenty thousand individual stock-market customers, who had never heard of Allied or commodity trading.  Williston & Beane had nine thousand individual customers.  All these accounts were frozen when the two firms were suspended by the Stock Exchange.  (Fortunately, the customers of Williston & Beane were made whole fairly rapidly.)

The Stock Exchange met with its member firms.  They decided to make the customers of Haupt whole.  G. Keith Funston, President of the Stock Exchange, urged the member firms to take over the matter.  The firms replied that the Stock Exchange should do it.  Funston replied, “If we do, you’ll have to repay us the amount we pay out.”  So it was agreed that the payment would come out of the Exchange’s treasury, to be repaid later by the member firms.

Funston next led the negotiations with Haupt’s creditor banks.  Their unanimous support was essential.  Chief among the creditors were four local banks – Chase Manhattan, Morgan Guaranty Trust, First National City, and Manufacturers Hanover Trust.  Funston proposed that the Exchange would put up the money to make the Haupt customers whole – about seven and a half million dollars.  In return, for every dollar the Exchange put up, the banks would agree to defer collection on two dollars.  So the banks would defer collection on about fifteen million.

The banks agreed to this on the condition that the Exchange’s claim to get back any of its contribution would come after the banks’ claims for their loans.  Funston and his associates at the Exchange agreed to that.  After more negotiating, there was a broad agreement on the general plan.

Early on Saturday, the Exchange’s board met and learned from Funston what was proposed.  Almost immediately, several governors rose to state that it was a matter of principle.  And so the board agreed with the plan.  Later, Funston and his associates decided to put the Exchange’s chief examiner in charge of the liquidation of Haupt in order to ensure that its twenty thousand individual customers were made whole as soon as the Exchange had put up the cash.  (The amount of cash would be at least seven and a half million, but possibly as high as twelve million.)

Fortunately, the American banks eventually all agreed to the final plan put forth by the Exchange.  Brooks notes that the banks were “marvels of cooperation.”  But agreement was still needed from the British banks.  Initially, Funston was going to make the trip to England, but he couldn’t be spared.

Several other governors quickly volunteered to go, and one of them, Gustave L. Levy, was eventually selected, on the ground that his firm, Goldman, Sachs & Co., had had a long and close association with Kleinwort, Benson, one of the British banks, and that Levy himself was on excellent terms with some of the Kleinwort, Benson partners.

The British banks were very unhappy.  But since their loans to Allied were unsecured, they didn’t have any room to negotiate.  Still, they asked for time to think the matter over.  This gave Levy an opportunity to meet with this Kleinwort, Benson friends.  Brooks:

The circumstances of the reunion were obviously less than happy, but Levy says that his friends took a realistic view of their situation and, with heroic objectivity, actually helped their fellow-Britons to see the American side of the question.

The market was closed Monday for JFK’s funeral.  Funston was still waiting for the call from Levy.  After finally getting agreement from all the British banks, Levy placed the call to Funston.

Funston felt at this point that the final agreement had been wrapped up, since all he needed was the signatures of the fifteen Haupt general partners.  The meeting with the Haupt partners ended up taking far longer than expected.  Brooks:

One startling event broke the even tenor of this gloomy meeting… someone noticed an unfamiliar and strikingly youthful face in the crowd and asked its owner to identify himself.  The unhesitating reply was, ‘I’m Russell Watson, a reporter for the Wall Street Journal.’  There was a short, stunned silence, in recognition of the fact that an untimely leak might still disturb the delicate balance of money and emotion that made up the agreement.  Watson himself, who was twenty-four and had been on the Journal for a year, has since explained how he got into the meeting, and under what circumstances he left it.  ‘I was new on the Stock Exchange beat then,’ he said afterward.  ‘Earlier in the day, there had been word that Funston would probably hold a press conference sometime that evening, so I went over to the Exchange.  At the main entrance, I asked a guard where Mr. Funston’s conference was.  The guard said it was on the sixth floor, and ushered me into an elevator.  I suppose he thought I was a banker, a Haupt partner, or a lawyer.  On the sixth floor, people were milling around everywhere.  I just walked off the elevator and into the office where the meeting was – nobody stopped me.  I didn’t understand much of what was going on.  I got the feeling that whatever was at stake, there was general agreement but still a lot of haggling over details to be done.  I didn’t recognize anybody there but Funston.  I stood around quietly for about five minutes before anybody noticed me, and then everybody said, pretty much at once, “Good God, get out of here!”  They didn’t exactly kick me out, but I saw it was time to go.’

At fifteen minutes past midnight, finally all the parties signed an agreement.

As soon as the banks opened on Tuesday, the Exchange deposited seven and a half million dollars in an account on which the Haupt liquidator – James P. Mahony – could draw.  The stock market had its greatest one-day rise in history.  A week later, by December 2, $1,750,000 had been paid out to Haupt customers.  By December 12, it was $5,400,000.  And by Christmas, it was $6,700,000.  By March 11, the pay-out had reached nine and a half million dollars and all the Haupt customers had been made whole.

  • Note:  $9.5 million in 1963 would be approximately $76 million dollars today (in 2018), due to inflation.

Brooks describes the reaction:

In Washington, President Johnson interrupted his first business day in office to telephone Funston and congratulate him.  The chairman of the S.E.C., William L. Cary, who was not ordinarily given to throwing bouquets at the Stock Exchange, said in December that it had furnished ‘a dramatic, impressive demonstration of its strength and concern for the public interest.’

Brooks later records:

Oddly, almost no one seems to have expressed gratitude to the British and American banks, which recouped something like half of their losses.  It may be that people simply don’t thank banks, except in television commercials.

 

THE IMPACTED PHILOSOPHERS

Brooks opens this chapter by observing that communication is one of the biggest problems in American industry.  (Remember he was writing in the 1960’s).  Brooks:

This preoccupation with the difficulty of getting a thought out of one head and into another is something the industrialists share with a substantial number of intellectuals and creative writers, more and more of whom seemed inclined to regard communication, or the lack of it, as one of the greatest problems not just of industry, but of humanity.

Brooks then adds:

What has puzzled me is how and why, when foundations sponsor one study of communication after another, individuals and organizations fail so consistently to express themselves understandably, or how and why their listeners fail to grasp what they hear.

A few years ago, I acquired a two-volume publication of the United States Government Printing Office entitled Hearings Before the Subcommittee on Antitrust and Monopoly of the Committee on the Judiciary, United States Senate, Eighty-Seventh Congress, First Session, Pursuant to S. Res. 52, and after a fairly diligent perusal of its 1,459 pages I thought I could begin to see what the industrialists are talking about.

The hearings were conducted in April, May, and June of 1961 under the chairmanship of Senator Estes Kefauver of Tennessee.  They concerned price-fixing and bid-rigging in conspiracies in the electrical-manufacturing industry.  Brooks:

…Senator Kefauver felt that the whole matter needed a good airing.  The transcript shows that it got one, and what the airing revealed – at least within the biggest company involved – was a breakdown in intramural communication so drastic as to make the building of the tower of Babel seem a triumph of organizational rapport.

Brooks explains a bit later:

The violations, the government alleged, were committed in connection with the sale of large and expensive pieces of apparatus of a variety that is required chiefly by public and private electric-utility companies (power transformers, switchgear assemblies, and turbine-generator units, among many others), and were the outcome of a series of meetings attended by executives of the supposedly competing companies – beginning at least as early as 1956 and continuing into 1959 – at which noncompetitive price levels were agreed upon, nominally sealed bids on individual contracts were rigged in advance, and each company was allocated a certain percentage of the available business.

Brooks explains that in an average year at the time of the conspiracies, about $1.75 billion – $14 billion in 2019 dollars – was spent on the sorts of machines in question, with nearly a quarter of that local, state, and federal government spending.  Brooks gives a specific example, a 500,000-kilowatt turbine-generator, which sold for about $16 million (nearly $130 million in 2019 dollars), but was often discounted by 25 percent.  If the companies wanted to, they could effectively charge $4 million extra (nearly $32 million extra in 2019 dollars).  Any such additional costs as a result of price-fixing would, in the case of government purchases, ultimately fall on the taxpayer.

Brooks again:

To top it all off, there was a prevalent suspicion of hypocrisy in the very highest places.  Neither the chairman of the board nor the president of General Electric, the largest of the corporate defendants, had been caught on the government’s dragnet, and the same was true of Westinghouse Electric, the second-largest;  these four ultimate bosses let it be known that they had been entirely ignorant of what had been going on within their commands right up to the time the first testimony on the subject was given to the Justice Department.  Many people, however, were not satisfied by these disclaimers, and, instead, took the position that the defendant executives were men in the middle, who had broken the law only in response either to actual orders or to a corporate climate favoring price-fixing, and who were now being allowed to suffer for the sins of their superiors.  Among the unsatisfied was Judge Ganey himself, who said at the time of the sentencing, ‘One would be most naive indeed to believe that these violations of the law, so long persisted in, affecting so large a segment of the industry, and, finally, involving so many millions upon millions of dollars, were facts unknown to those responsible for the conduct of the corporation… I am convinced that in the great number of these defendants’ cases, they were torn between conscience and approved corporate policy, with the rewarding objectives of promotion, comfortable security, and large salaries.’

General Electric got most of the attention.  It was, after all, by far the largest of those companies involved.  General Electric penalized employees who admitted participation in the conspiracy.  Some saw this as good behavior, while others thought it was G.E. trying to save higher-ups by making a few sacrifices.

G.E. maintained that top executives didn’t know.  Judge Ganey thought otherwise.  But Brooks realized it couldn’t be determined:

…For, as the testimony shows, the clear waters of moral responsibility at G.E. became hopelessly muddied by a struggle to communicate – a struggle so confused that in some cases, it would appear, if one of the big bosses at G.E. had ordered a subordinate to break the law, the message would somehow have been garbled in its reception, and if the subordinate had informed the boss that he was holding conspiratorial meetings with competitors, the boss might well have been under the impression that the subordinate was gossiping idly about lawn parties or pinochle lessons.

G.E., for at least eight years, has had a rule, Directive Policy 20.5, which explicitly forbids price-fixing, bid-rigging, and similar anticompetitive practices.  The company regularly reissued 20.5 to new executives and asked them to sign their names to it.

The problem was that many, including those who signed, didn’t take 20.5 seriously.  They thought it was just a legal device.  They believed that meeting illegally with competitors was the accepted and standard practice.  They concluded that if a superior told them to comply with 20.5, he was actually ordering him to violate it.  Brooks:

Illogical as it might seem, this last assumption becomes comprehensible in light of the fact that, for a time, when some executives orally conveyed, or reconveyed, the order, they were apparently in the habit of accompanying it with an unmistakable wink.

Brooks gives an example of just such a meeting of sales managers in May 1948.  Robert Paxton, an upper-level G.E. executive who later became the company’s president, addressed the group and gave the usual warnings about antitrust violations.  William S. Ginn, a salesman under Paxton, interjected, “We didn’t see you wink.”  Paxton replied, “There was no wink.  We mean it, and these are the orders.”

Senator Kefauver asked Paxton how long he had known about such winks.  Paxton said that in 1935, he saw his boss do it following an order.  Paxton recounts that he became incensed.  Since then, he had earned a reputation as an antiwink man.

In any case, Paxton’s seemingly unambiguous order in 1948 failed to get through to Ginn, who promptly began pricing-fixing with competitors.  When asked about it thirteen years later, Ginn – having recently gotten out of jail and having lost his $135,000 a year job at G.E. – said he had gotten a contrary order from two other superiors, Henry V. B. Erben and Francis Fairman.  Brooks:

Erben and Fairman, Ginn said, had been more articulate, persuasive, and forceful in issuing their order than Paxton had been in issuing his;  Fairman, especially, Ginn stressed, had proved to be ‘a great communicator, a great philosopher, and, frankly, a great believer in stability of prices.’  Both Erben and Fairman had dismissed Paxton as naive, Ginn testified, and, in further summary of how he had been led astray, he said that ‘the people who were advocating the Devil were able to sell me better than the philosophers that were selling me the Lord.’

Unfortunately, Erben and Fairman had passed away before the hearing.  So we don’t have their testimonies.  Ginn consistently described Paxton as a philosopher-salesman on the side of the Lord.

In November, 1954, Ginn was made general manager of the transformer division.  Ralph J. Cordiner, chairman of the board at G.E. since 1949, called Ginn down to New York to order him to comply strictly with Directive 20.5.  Brooks:

Cordiner communicated this idea so successfully that it was clear enough to Ginn at the moment, but it remained so only as long as it took him, after leaving the chairman, to walk to Erben’s office.

Erben, Ginn’s direct superior, countermanded Cordiner’s order.

Erben’s extraordinary communicative prowess carried the day, and Ginn continued to meet with competitors.

At the end of 1954, Paxton took over Erben’s job and was thus Ginn’s direct superior.  Ginn kept meeting with competitors, but he didn’t tell Paxton about it, knowing his opposition to the practice.

In January 1957, Ginn became general manager of G.E.’s turbine-generator division.  Cordiner called him down again to instruct him to follow 20.5.  This time, however, Ginn got the message.  Why?  “Because my air cover was gone,” Ginn explained to the Subcommittee.  Brooks:

If Erben, who had not been Ginn’s boss since late in 1954, had been the source of his air cover, Ginn must have been without its protection for over two years, but, presumably, in the excitement of the price war he had failed to notice its absence.

In any case, Ginn apparently had reformed.  Ginn circulated copies of 20.5 among all his division managers.  He then instructed them not to even socialize with competitors.

It appears that Ginn had not been able to impart much of his shining new philosophy to others, and that at the root of his difficulty lay that old jinx, the problem of communicating.

Brooks quotes Ginn:

‘I have got to admit that I made a communication error.  I didn’t sell this thing to the boys well enough… The price is so important in the complete running of a business that, philosophically, we have got to sell people not only just the fact that it is against the law, but… that it shouldn’t be done for many, many reasons.  But it has got to be a philosophical approach and a communication approach…’

Frank E. Stehlik was general manager of the low-voltage-switchgear department from May, 1956 to February, 1960.  Stehlik not only heard 20.5 directly from his superiors in oral and written communications.  But, in addition, Stehlik was open to a more visceral type of communication he called “impacts.”  Brooks explains:

Apparently, when something happened within the company that made an impression on him, he would consult an internal sort of metaphysical voltmeter to ascertain the force of the jolt he had received, and, from the reading he got, would attempt to gauge the true drift of company policy.

In 1956, 1957, and for most of 1958, Stehlik believed that company policy clearly required compliance with 20.5.  But in the fall of 1958, Stehlik’s immediate superior, George E. Burens, told him that Paxton had told him (Burens) to have lunch with a competitor.  Paxton later testified that he categorically told Burens not to discuss prices.  But Stehlik got a different impression.

In Stehlik’s mind, this fact made an “impact.”  He felt that company policy was now in favor of disobeying 20.5.  So, late in 1958, when Burens told him to begin having price meetings with a competitor, he was not at all surprised.  Stehlik complied.

Brooks next describes the communication problem from the point of view of superiors.  Raymond W. Smith was general manager of G.E.’s transformer division, while Arthur F. Vinson was vice-president in charge G.E.’s apparatus group.  Vinson ended up becoming Smith’s immediate boss.

Smith testified that Cordiner gave him the usual order on 20.5.  But late in 1957, price competition for transformers was so intense that Smith decided on his own to start meeting with competitors to see if prices could be stabilized.  Smith thought company policy and industry practice both supported his actions.

When Vinson became Smith’s boss, Smith felt he should let him know what he was doing.  So on several occasions, Smith told Vinson, “I had a meeting with the clan this morning.”

Vinson, in his testimony, said he didn’t even recall Smith use the phrase, “meeting of the clan.”  Vinson only recalled that Smith would say things like, “Well, I am going to take this new plan on transformers and show it to the boys.”  Vinson testified that he thought Smith meant the G.E. district salespeople and the company’s customers.  Vinson claimed to be shocked when he learned that Smith was referring to price-fixing meetings with competitors.

But Smith was sure that his communication had gotten through to Vinson.  “I never got the impression that he misunderstood me,” Smith testified.

Senator Kefauver asked Vinson if he was so naive as to not know to whom “the boys” referred.  Vinson replied, “I don’t think it is too naive.   We have a lot of boys… I may be naive, but I am certainly telling the truth, and in this kind of thing I am sure I am naive.”

Kefauver pressed Vinson, asking how he could have become vice-president at $200,000 a year if he were naive.  Vinson:  “I think I could well get there by being naive in this area.  It might help.”

Brooks asks:

Was Vinson really saying to Kefauver what he seemed to be saying – that naivete about antitrust violations might be a help to a man in getting and holding a $200,000-a-year job at General Electric?  It seems unlikely.  And yet what else could he have meant?

Vinson was also implicated in another part of the case.  Four switchgear executives – Burens, Stehlik, Clarence E. Burke, and H. Frank Hentschel – testified before the grand jury (and later before the Subcommittee) that in mid-1958, Vinson had lunch with them in Dining Room B of G.E.’s switchgear works in Philadelphia, and that Vinson told them to hold price meetings with competitors.

This led the four switchgear executives to hold a series of meetings with competitors.  But Vinson told prosecutors that the lunch never took place and that he had had no knowledge at all of the conspiracy until the case broke.  Regarding the lunch, Burens, Stehlik, Burke, and Hentschel all had lie-detector tests, given by the F.B.I., and passed them.

Brooks writes:

Vinson refused to take a lie-detector test, at first explaining that he was acting on advice of counsel and against his personal inclination, and later, after hearing how the four other men had fared, arguing that if the machine had not pronounced them liars, it couldn’t be any good.

It was shown that there were only eight days in mid-1958 when Burens, Stehlik, Burke, and Hentschel all had been together at the Philadelphia plant and could have had lunch together.  Vinson produced expense accounts showing that he had been elsewhere on each of those eight days.  So the Justice Department dropped the case against Vinson.

The upper level of G.E. “came through unscathed.”  Chairman Cordiner and President Paxton did seem to be clearly against price-fixing, and unaware of all the price-fixing that had been occurring.  Paxton, during his testimony, said that he learned from his boss, Gerard Swope, that the ultimate goal of business was to produce more goods for people at lower cost.  Paxton claimed to be deeply impacted by this philosophy, explaining why he was always strongly against price-fixing.

Brooks concludes:

Philosophy seems to have reached a high point at G.E., and communication a low one.  If executives could just learn to understand one another, most of the witnesses said or implied, the problem of antitrust violations would be solved.  But perhaps the problem is cultural as well as technical, and has something to do with a loss of personal identity that comes with working in a huge organization.  The cartoonist Jules Feiffer, contemplating the communication problem in a nonindustrial context, has said, ‘Actually, the breakdown is between the person and himself.  If you’re not able to communicate successfully between yourself and yourself, how are you supposed to make it with the strangers outside?’  Suppose, purely as a hypothesis, that the owner of a company who orders his subordinates to obey the antitrust laws has such poor communication with himself that he does not really know whether he wants the order to be complied with or not.  If his order is disobeyed, the resulting price-fixing may benefit his company’s coffers;  if it is obeyed, then he has done the right thing.  In the first instance, he is not personally implicated in any wrongdoing, while in the second he is positively involved in right doing.  What, after all, can he lose?  It is perhaps reasonable to suppose that such an executive will communicate his uncertainty more forcefully than his order.

 

THE LAST GREAT CORNER

Piggly Wiggly Stores – a chain of retail self-service markets mostly in the South and West, and headquartered in Memphis – was first listed on the New York Stock Exchange in June, 1922.  Clarence Saunders was the head of Piggly Wiggly.  Brooks describes Saunders:

…a plump, neat, handsome man of forty-one who was already something of a legend in his home town, chiefly because of a house he was putting up there for himself.  Called the Pink Palace, it was an enormous structure faced with pink Georgia marble and built around an awe-inspiring white-marble Roman atrium, and, according to Saunders, it would stand for a thousand years.  Unfinished though it was, the Pink Palace was like nothing Memphis had ever seen before.  Its grounds were to include a private golf course, since Saunders liked to do his golfing in seclusion.

Brooks continues:

The game of Corner – for in its heyday it was a game, a high-stakes gambling game, pure and simple, embodying a good many of the characteristics of poker – was one phase of the endless Wall Street contest between bulls, who want the price of a stock to go up, and bears, who want it to go down.  When a game of Corner was underway, the bulls’ basic method of operation was, of course, to buy stock, and the bears’ was to sell it.

Since most bears didn’t own the stock, they would have to conduct a short sale.  This means they borrow stock from a broker and sell it.  But they must buy the stock back later in order to return it to the broker.  If they buy the stock back at a lower price, then the difference between where they initially sold the stock short, and where they later buy it back, represents their profit.  If, however, they buy the stock back at a higher price, then they suffer a loss.

There are two related risks that the short seller (the bear) faces.  First, the short seller initially borrows the stock from the broker in order to sell it.  If the broker is forced to demand the stock back from the short seller – either because the “floating supply” needs to be replenished, or because the short seller has insufficient equity (due to the stock price moving to high) – then the short seller can be forced to take a loss.  Second, technically there is no limit to how much the short seller can lose because there is no limit to how high a stock can go.

The danger of potentially unlimited losses for a short seller can be exacerbated in a Corner.  That’s because the bulls in a Corner can buy up so much of the stock that there is very little supply of it left.  As the stock price skyrockets and the supply of stock shrinks, the short seller can be forced to buy the stock back – most likely from the bulls – at an extremely high price.  This is precisely what the bulls are trying to accomplish in a Corner.

On the other hand, if the bulls end up owning most of the publicly available stock, and if the bears can ride out the Corner, then to whom can the bulls sell their stock?  If there are virtually no buyers, then the bulls have no chance of selling most of their holding.  In this case, the bulls can get stuck with a mountain of stock they can’t sell.  The achievable value of this mountain can even approach zero in some extreme cases.

Brooks explains that true Corners could not happen after the new securities legislation in the 1930’s.  Thus, Saunders was the last intentional player of the game.

Saunders was born to a poor family in Amherst County, Virginia, in 1881.  He started out working for practically nothing for a local grocer.  He then worked for a wholesale grocer in Clarksville, Tennessee, and then for another one in Memphis.  Next, he organized a retail food chain, which he sold.  Then he was a wholesale grocer before launching the retail self-service food chain he named Piggly Wiggly Stores.

By the fall of 1922, there were over 1,200 Piggly Wiggly Stores.  650 of these were owned outright by Saunders’ Piggly Wiggly Stores, Inc.  The rest were owned independently, but still paid royalties to the parent company.  For the first time, customers were allowed to go down any aisle and pick out whatever they wanted to buy.  Then they paid on their way out of the store.  Saunders didn’t know it, but he had invented the supermarket.

In November, 1922, several small companies operating Piggly Wiggly Stores in New York, New Jersey, and Connecticut went bankrupt.  These were independently owned, having nothing to do with Piggly Wiggly Stores, Inc.  Nonetheless, several stock-market operators saw what they believed was a golden opportunity for a bear raid.  Brooks:

If individual Piggly Wiggly stores were failing, they reasoned, then rumors could be spread that would lead the uninformed public to believe that the parent firm was failing, too.  To further this belief, they began briskly selling Piggly Wiggly short, in order to force the price down.  The stock yielded readily to their pressure, and within a few weeks its price, which earlier in the year had hovered around fifty dollars a share, dropped to below forty.

Saunders promptly announced to the press that he was going to “beat the Wall Street professionals at their own game” through a buying campaign.  At that point, Saunders had no experience at all with owning stock, Piggly Wiggly being the only stock he had ever owned.  Moreover, there is no reason to think Saunders was going for a Corner at this juncture.  He merely wanted to support his stock on behalf of himself and other stockholders.

Saunders borrowed $10 million dollars – about $140 million in 2019 dollars – from bankers in Memphis, Nashville, New Orleans, Chattanooga, and St. Louis.  Brooks:

Legend has it that he stuffed his ten million-plus, in bills of large denomination, into a suitcase, boarded a train for New York, and, his pockets bulging with currency that wouldn’t fit in the suitcase, marched on Wall Street, ready to do battle.

Saunders later denied this, saying he conducted his campaign from Memphis.  Brooks continues:

Wherever he was at the time, he did round up a corp of some twenty brokers, among them Jesse L. Livermore, who served as his chief of staff.  Livermore, one of the most celebrated American speculators of this century, was then forty-five years old but was still occasionally, and derisively, referred to by the nickname he had earned a couple of decades earlier – the Boy Plunger of Wall Street.  Since Saunders regarded Wall Streeters in general and speculators in particular as parasitic scoundrels intent only on battering down his stock, it seemed likely that his decision to make an ally of Livermore was a reluctant one, arrived at simply with the idea of getting the enemy chieftain into his own camp.

Within a week, Saunders had bought 105,000 shares – more than half of the 200,000 shares outstanding.  By January 1923, the stock hit $60 a share, its highest level ever.  Reports came from Chicago that the stock was cornered.  The bears couldn’t find any available supply in order to cover their short positions by buying the stock back.  The New York Stock Exchange immediately denied the rumor, saying ample amounts of Piggly Wiggly stock were still available.

Saunders then made a surprising but exceedingly crafty move.  The stock was pushing $70, but Saunders ran advertisements offering to sell it for $55.  Brooks explains:

One of the great hazards in Corner was always that even though a player might defeat his opponents, he would discover that he had won a Pyrrhic victory.  Once the short sellers had been squeezed dry, that is, the cornerer might find that the reams of stock he had accumulated in the process were a dead weight around his neck;  by pushing it all back into the market in one shove, he would drive its price down close to zero.  And if, like Saunders, he had had to borrow heavily to get into the game in the first place, his creditors could be expected to close in on him and perhaps not only divest him of his gains but drive him into bankruptcy.  Saunders apparently anticipated this hazard almost as soon as a corner was in sight, and accordingly made plans to unload some of his stock before winning instead of afterward.  His problem was to keep the stock he sold from going right back into the floating supply, thus breaking his corner;  and his solution was to sell his fifty-five-dollar shares on the installment plan.

Crucially, the buyers on the installment plan wouldn’t receive the certificates of ownership until they had paid their final installment.  This meant they couldn’t sell their shares back into the floating supply until they had finished making all their installment payments.

By Monday, March 19, Saunders owned nearly all of the 200,000 shares of Piggly Wiggly stock.  Livermore had already bowed out of the affair on March 12 because he was concerned about Saunders’ financial position.  Nonetheless, Saunders asked Livermore to spring the bear trap.  Livermore wouldn’t do it.  So Saunders himself had to do it.

On Tuesday, March 20, Saunders called for delivery all of his Piggly Wiggly stock.  By the rules of the Exchange, stock so called for had to be delivered by 2:15 the following afternoon.  There were a few shares around owned in small amounts by private investors.  Short sellers were frantically trying to find these folks.  But on the whole, there were basically no shares available outside of what Saunders himself owned.

This meant that Piggly Wiggly shares had become very illiquid – there were hardly any shares trading.  A nightmare, it seemed, for short sellers.  Some short sellers bought at $90, some at $100, some at $110.  Eventually the stock reached $124.  But then a rumor reached the floor that the governors of the Exchange were considering a suspension of trading in Piggly Wiggly, as well as an extension of the deadline for short sellers.  Piggly Wiggly stock fell to $82.

The Governing Committee of the Exchange did, in fact, made such an announcement.  They claimed that they didn’t want to see a repeat of the Northern Pacific panic.  However, many wondered whether the Exchange was just helping the short sellers, among whom were some members of the Exchange.

Saunders still hadn’t grasped the fundamental problem he now faced.  He still seemed to have several million in profits.  But only if he could actually sell his shares.

Next, the Stock Exchange announced a permanent suspension of trading in Piggly Wiggly stock and a full five day extension for short sellers to return their borrowed shares.  Short sellers had until 2:15 the following Monday.

Meanwhile, Piggly Wiggly Stores, Inc., released its annual financial statement, which revealed that sales, profits, and assets had all sharply increased from the previous year.  But everyone ignored the real value of the company.  All that mattered at this point was the game.

The extension allowed short sellers the time to find shareholders in a variety of locations around the country.  These shareholders were of course happy to dig out their stock certificates and sell them for $100 a share.  In this way, the short sellers were able to completely cover their short positions by Friday evening.  And instead of paying Saunders cash for some of his shares, the short sellers gave him more shares to settle their debt, which is the last thing Saunders wanted just then.  (A few short sellers had to pay Saunders directly.)

The upshot was that all the short sellers were in the clear, whereas Saunders was stuck owning nearly every single share of Piggly Wiggly stock.  Saunders, who had already started complaining loudly, repeated his charge that Wall Street had changed its own rule in order to let “a bunch of welchers” off the hook.

In response, the Stock Exchange issued a statement explaining its actions:

‘The enforcement simultaneously of all contracts for the return of stock would have forced the stock to any price that might be fixed by Mr. Saunders, and competitive bidding for the insufficient supply might have brought about conditions illustrated by other corners, notably the Northern Pacific corner in 1901.’

Furthermore, the Stock Exchange pointed out that its own rules allowed it to suspend trading in a stock, as well as to extend the deadline for the return of borrowed shares.

It is true that the Exchange had the right to suspend trading in a stock.  But it is unclear, to say the least, about whether the Exchange had any right to postpone the deadline for the delivery of borrowed shares.  In fact, two years after Saunders’ corner, in June, 1925, the Exchange felt bound to amend its constitution with an article stating that “whenever in the opinion of the Governing Committee a corner has been created in a security listed on the Exchange… the Governing Committee may postpone the time for deliveries on Exchange contracts therein.”

 

A SECOND SORT OF LIFE

According to Brooks, other than FDR himself, perhaps no one typified the New Deal better than David Eli Lilienthal.  On a personal level, Wall Streeters found Lilienthal a reasonable fellow.  But through his association with Tennessee Valley Authority from 1933 to 1946, Lilienthal “wore horns.”  T.V.A. was a government-owned electric-power concern that was far larger than any private power corporation.  As such, T.V.A. was widely viewed on Wall Street as the embodiment of “galloping Socialism.”

In 1946, Lilienthal became the first chairman of the United States Atomic Energy Commission, which he held until February, 1950.

Brooks was curious what Lilienthal had been up to since 1950, so he did some investigating.  He found that Lilienthal was co-founder and chairman of Development & Resources Corporation.  D. & R. helps governments set up programs similar to the T.V.A.  Brooks also found that as of June, 1960, Lilienthal was a director and major shareholder of Minerals & Chemicals Corporation of America.

Lastly, Brooks discovered Lilienthal had published his third book in 1953, “Big Business: A New Era.”  In the book, he argues that:

  • the productive superiority of the United States depends on big business;
  • we have adequate safeguards against abuses by big business;
  • big businesses tend to promote small businesses, not destroy them;
  • and big business promotes individualism, rather than harms it, by reducing poverty, disease, and physical insecurity.

Lilienthal later agreed with his family that he hadn’t spent enough time on the book, although its main points were correct.  Also, he stressed that he had conceived of the book before he ever decided to transition from government to business.

In 1957, Lilienthal and his wife Helen Lamb Lilienthal had settled in a house in Princeton.  It was a few years later, at this house, that Brooks went to interview Lilienthal.  Brooks was curious to hear about how Lilienthal thought about his civic career as compared to his business career.

Lilienthal had started out as a lawyer in Chicago and he done quite well.  But he didn’t want to practice the law.  Then – in 1950 – his public career over, he was offered various professorship positions at Harvard.  He didn’t want to be a professor.  Then various law firms and businesses approached Lilienthal.  He still had no interest in practicing law.  He also rejected the business offers he received.

In May, 1950, Lilienthal took a job as a part-time consultant for Lazard Freres & Co., whose senior partner, Andre Meyer, he had met through Albert Lasker, a mutual friend.  Through Lazard Freres and Meyer, Lilienthal became a consultant and then an executive of a small company, the Minerals Separation North American Corporation.  Lazard Freres had a large interest in the concern.

The company was in trouble, and Meyer thought Lilienthal was the man to solve the case.  Through a series of mergers, acquisitions, etc., the firm went through several name changes ending, in 1960, with the name, Minerals & Chemicals Philipp Corporation.  Meanwhile, annual sales for the company went from $750,000 in 1952 to more than $274,000,000 in 1960.  (In 2019 dollars, this would be a move from $6,750,000 to $2,466,000,000.)  Brooks writes:

For Lilienthal, the acceptance of Meyer’s commission to look into the company’s affairs was the beginning of a four-year immersion in the day-to-day problems of managing a business;  the experience, he said decisively, turned out to be one of his life’s richest, and by no means only in the literal sense of that word.

Minerals Separation North American, founded in 1916 as an offshoot from a British company, was a patent firm.  It held patents on processes used to refine copper ore and other nonferrous minerals.  In 1952, Lilienthal became the president of the company.  In order to gain another source of revenue, Lilienthal arranged a merger between Minerals Separation and Attapulgus Clay Company, a producer of a rare clay used in purifying petroleum products and also a manufacturer of various household products.

The merger took place in December, 1952, thanks in part to Lilienthal’s work to gain agreement from the Attapulgus people.  The profits and stock price of the new company went up from there.  Lilienthal managed some of the day-to-day business.  And he helped with new mergers.  One in 1954, with Edgar Brothers, a leading producer of kaolin for paper coating.  Two more in 1955, with limestone firms in Ohio and Virginia.  Brooks notes that the company’s net profits quintupled between 1952 and 1955.

Lilienthal received stock options along the way.  Because the stock went up a great deal, he exercised his options and by August, 1955, Lilienthal had 40,000 shares.  Soon the stock hit $40 and was paying a $0.50 annual dividend.  Lilienthal’s financial worries were over.

Brooks asked Lilienthal how all of this felt.  Lilienthal:

‘I wanted an entrepreneurial experience.  I found a great appeal in the idea of taking a small and quite crippled company and trying to make something of it.  Building.  That kind of building, I thought, is the central thing in American free enterprise, and something I’d missed in all my government work.  I wanted to try my hand at it.  Now, about how it felt.  Well, it felt plenty exciting.  It was full of intellectual stimulation, and a lot of my old ideas changed.  I conceived a great new respect for financiers – men like Andre Meyer.  There’s a correctness about them, a certain high sense of honor, that I’d never had any conception of.  I found that business life is full of creative, original minds – along with the usual number of second-guessers, of course.  Furthermore, I found it seductive.  In fact, I was in danger of becoming a slave… I found that the things you read – for instance, that acquiring money for its own sake can become an addiction if you’re not careful – are literally true.  Certain good friends helped keep me on track… Oh, I had my problems.  I questioned myself at every step.  It was exhausting.’

A friend of Lilienthal’s told Brooks that Lilienthal had a marvelous ability to immerse himself totally in the work.  The work may not always be important.  But Lilienthal becomes so immersed, it’s as if the work becomes important simply because he’s doing it.

On the matter of money, Lilienthal said it doesn’t make much difference as long as you have enough.  Money was something he never really thought about.

Next Brooks describes Lilienthal’s experience at Development & Resources Corporation.  The situation became ideal for Lilienthal because it combined helping the world directly with the possibility of also earning a profit.

In the spring of 1955, Lilienthal and Meyer had several conversations.  Lilienthal told Meyer that he knew dozens of foreign dignitaries and technical personnel who had visited T.V.A. and shown strong interest.  Many of them told Lilienthal that at least some of their own countries would be interested in starting similar programs.

The idea for D. & R. was to accomplish very specific projects and, incidentally, to make a profit.  Meyer liked the idea – although he expected no profit – so they went forward, with Lazard Freres owning half the firm.  The executive appointments for D.& R. included important alumni from T.V.A., people with deep experience and knowledge in management, engineering, dams, electric power, and related areas.

In September, 1955, Lilienthal was at a World Bank meeting in Istanbul and he ended up speaking with Abolhassan Ebtehaj, head of a 7-year development plan in Iran.  Iran had considerable capital with which to pay for development projects, thanks to royalties from its nationalized oil industry.  Moreover, what Iran badly needed was technical and professional guidance.  Lilienthal and a colleague later visited Iran as guests of the Shah to see what could be done about Khuzistan.

Lilienthal didn’t know anything about the region at first.  But he learned that Khuzistan was in the middle of the Old Testament Elamite kingdom and later of the Persian Empire.  The ruins of Persepolis are close by.  The ruins of Susa, where King Darius had a winter palace, are at the center of Khuzistan.  Brooks quotes Lilienthal (in the 1960’s):

Nowadays, Khuzistan is one of the world’s richest oil fields  – the famous Abadan refinery is at its southern tip – but the inhabitants, two and a half million of them, haven’t benefited from that.  The rivers have flowed unused, the fabulously rich soil has lain fallow, and all but a tiny fraction of the people have continued to live in desperate poverty.

D. & R. signed a 5-year agreement with the Iranian government.  Once the project got going, there were 700 people working on it – 100 Americans, 300 Iranians, and 300 others (mostly Europeans).  In addition, 4,700 Iranian-laborers were on the various sites.  The entire project called for 14 dams on 5 different rivers.  After D. & R. completed its first 5-year contract, they signed a year-and-a-half extension including an option for an additional 5 years.

Brooks records:

While the Iranian project was proceeding, D. & R. was also busy lining up and carrying out its programs for Italy, Colombia, Ghana, the Ivory Coast, and Puerto Rico, as well as programs for private business groups in Chile and the Philippines.  A job that D. & R. had just taken on from the United States Army Corps of Engineers excited Lilienthal enormously – an investigation of the economic impact of power from a proposed dam on the Alaskan sector of the Yukon, which he described as ‘the river with the greatest hydroelectric potential remaining on this continent.’  Meanwhile, Lazard Freres maintained its financial interest in the firm and now very happily collected its share of a substantial annual profit, and Lilienthal happily took to teasing Meyer about his former skepticism as to D. & R. financial prospects.

Lilienthal wrote in his journal about the extreme poverty in Ahwaz, Khuzistan:

…visiting villages and going into mud ‘homes’ quite unbelievable – and unforgettable forever and ever.  As the Biblical oath has it:  Let my right hand wither if I ever forget how some of the most attractive of my fellow human beings live – are living tonight, only a few kilometres from here, where we visited them this afternoon…

And yet I am as sure as I am writing these notes that the Ghebli area, of only 45,000 acres, swallowed in the vastness of Khuzistan, will become as well known as, say, the community of Tupelo… became, or New Harmony or Salt Lake City when it was founded by a handful of dedicated men in a pass of the great Rockies.

 

STOCKHOLDER SEASON

The owners of public businesses in the United States are the stockholders.  But many stockholders don’t pay much attention to company affairs when things are going well.  Also, many stockholders own small numbers of shares, making it not seem worthwhile to exercise their rights as owners of the corporations.  Furthermore, many stockholders don’t understand or follow business, notes Brooks.

Brooks decided to attend several annual meetings in the spring of 1966.

What particularly commended the 1966 season to me was that it promised to be a particularly lively one.  Various reports of a new “hard-line approach” by company managements to stockholders had appeared in the press.  (I was charmed by the notion of a candidate for office announcing his new hard-line approach to voters right before an election.)

Brooks first attended the A. T. & T. annual meeting in Detroit.  Chairman Kappel came on stage, followed by eighteen directors who sat behind him, and he called the meeting to order.  Brooks:

From my reading and from annual meetings that I’d attended in past years, I knew that the meetings of the biggest companies are usually marked by the presence of so-called professional stockholders… and that the most celebrated members of this breed were Mrs. Wilma Soss, of New York, who heads an organization of women stockholders and votes the proxies of its members as well as her own shares, and Lewis D. Gilbert, also of New York, who represents his own holdings and those of his family – a considerable total.

Brooks learned that, apart from prepared comments by management, many big-company meetings are actually a dialogue between the chairman and a few professional stockholders.  So professional stockholders can come to represent, in a way, many other shareholders who might otherwise not be represented, whether because they own few shares, don’t follow business, or other reasons.

Brooks notes that occasionally some professional stockholders get boorish, silly, on insulting.  But not Mrs. Soss or Mr. Gilbert:

Mrs. Soss, a former public-relations woman who has been a tireless professional stockholder since 1947, is usually a good many cuts above this level.  True, she is not beyond playing to the gallery by wearing bizarre costumes to meetings;  she tries, with occasional success, to taunt recalcitrant chairmen into throwing her out;  she is often scolding and occasionally abusive;  and nobody could accuse her of being unduly concise.  I confess that her customary tone and manner set my teeth on edge, but I can’t help recognizing that, because she does her homework, she usually has a point.  Mr. Gilbert, who has been at it since 1933 and is the dean of them all, almost invariably has a point, and by comparison with his colleagues he is the soul of brevity and punctilio as well as of dedication and diligence.

At the A. T. & T. meeting, after the management-sponsored slate of directors had been duly nominated, Mrs. Soss got up to make a nomination of her own, Dr. Frances Arkin, a psychoanalyst.  Mrs. Soss said A. T. & T. ought to have a woman on its board and, moreover, she thought some of the company’s executives would have benefited from periodic psychiatric examinations.  (Brooks comments that things were put back into balance at another annual meeting when the chairman suggested that some of the firm’s stockholders should see a psychiatrist.)  The nomination of Dr. Arkin was seconded by Mr. Gilbert, but only after Mrs. Soss nudged him forcefully in the ribs.

A professional stockholder named Evelyn Y. Davis complained about the meeting not being in New York, as it usually is.  Brooks observed that Davis was the youngest and perhaps the best-looking, but “not the best-informed or the most temperate, serious-minded, or worldly-wise.”  Davis’ complaint was met with boos from the largely local crowd in Detroit.

After a couple of hours, Mr. Kappel was getting testy.  Soon thereafter, Mrs. Soss was complaining that while the business affiliations of the nominees for director were listed in the pamphlet handed out at the meeting, this information hadn’t been included in the material mailed to stockholders, contrary to custom.  Mrs. Soss wanted to know why.  Mrs. Soss adopted a scolding tone and Mr. Kappel an icy one, says Brooks.  “I can’t hear you,” Mrs. Soss said at one point.  “Well, if you’d just listen instead of talking…”, Mr. Kappel replied.  Then Mrs. Soss said something (Brooks couldn’t hear it precisely) that successfully baited the chairman, who got upset and had the microphone in front of Mrs. Soss turned off.  Mrs. Soss marched towards the platform and was directly facing Mr. Kappel.  Mr. Kappel said he wasn’t going to throw her out of the meeting as she wanted.  Mrs. Soss later returned to her seat and a measure of calm was restored.

Later, Brooks attended the annual meeting of Chas. Pfizer & Co., which was run by the chairman, John E. McKeen.  After the company announced record highs on all of its operational metrics, and predicted more of the same going forward, “the most intransigent professional stockholder would have been hard put to it to mount much of a rebellion at this particular meeting,” observes Brooks.

John Gilbert, brother of Lewis Gilbert, may have been the only professional stockholder present.  (Lewis Gilbert and Mrs. Davis were at the U.S. Steel meeting in Cleveland that day.)

John Gilbert is the sort of professional stockholder the Pfizer management deserves, or would like to think it does.  With an easygoing manner and a habit of punctuating his words with self-deprecating little laughs, he is the most ingratiating gadly imaginable (or was on this occasion; I’m told he isn’t always), and as he ran through what seemed to be the standard Gilbert-family repertoire of questions – on the reliability of the firms’s auditors, the salaries of its officers, the fees of its directors – he seemed almost apologetic that duty called on him to commit the indelicacy of asking such things.

The annual meeting of Communications Satellite Corporation had elements of farce, recounts Brooks.  (Brooks refers to Comsat as a “glamorous space-age communications company.”)  Mrs. Davis, Mrs. Soss, and Lewis Gilbert were in attendance.  The chairman of Comsat, who ran the meeting, was James McCormack, a West Point graduate, former Rhodes Scholar, and retired Air Force General.

Mrs. Soss made a speech which was inaudible because her microphone wasn’t working.  Next, Mrs. Davis rose to complain that there was a special door to the meeting for “distinguished guests.”  Mrs. Davis viewed this as undemocratic.  Mr. McCormack responded, “We apologize, and when you go out, please go by any door you want.”  But Mrs. Davis went on speaking.  Brooks:

And now the mood of farce was heightened when it became clear that the Soss-Gilbert faction had decided to abandon all efforts to keep ranks closed with Mrs. Davis.  Near the height of her oration, Mr. Gilbert, looking as outraged as a boy whose ball game is being spoiled by a player who doesn’t know the rules or care about the game, got up and began shouting, ‘Point of order!  Point of order!’  But Mr. McCormack spurned this offer of parliamentary help;  he ruled Mr. Gilbert’s point of order out of order, and bade Mrs. Davis proceed.  I had no trouble deducing why he did this.  There were unmistakable signs that he, unlike any other corporate chairman I had seen in action, was enjoying every minute of the goings on.  Through most of the meeting, and especially when the professional stockholders had the floor, Mr. McCormack wore the dreamy smile of a wholly bemused spectator.

Mrs. Davis’ speech increased in volume and content, and she started making specific accusations against individual Comsat directors.  Three security guards appeared on the scene and marched to a location near Mrs. Davis, who then suddenly ended her speech and sat down.

Brooks comments:

Once, when Mr. Gilbert said something that Mrs. Davis didn’t like and Mrs. Davis, without waiting to be recognized, began shouting her objection across the room, Mr. McCormack gave a short irrepressible giggle.  That single falsetto syllable, magnificently amplified by the chairman’s microphone, was the motif of the Comsat meeting.

 

ONE FREE BITE

Brooks writes about Donald W. Wohlgemuth, a scientist for B. F. Goodrich Company in Akron, Ohio.

…he was the manager of Goodrich’s department of space-suit engineering, and over the past years, in the process of working his way up to that position, he had had a considerable part in the designing and construction of the suits worn by our Mercury astronauts on their orbital and suborbital flights.

Some time later, the International Latex Corporation, one of Goodrich’s three main competitors in the space-suit field, contacted Wohlgemuth.

…Latex had recently been awarded a subcontract, amounting to some three-quarters of a million dollars, to do research and development on space suits for the Apollo, or man-on-the-moon, project.  As a matter of fact, Latex had won this contract in competition with Goodrich, among others, and was thus for the moment the hottest company in the space-suit field.

Moreover, Wohlgemuth was not particularly happy at Goodrich for a number of reasons.  His salary was below average.  His request for air-conditioning had been turned down.

Latex was located in Dover, Delaware.  Wohlgemuth went there to meet with company representatives.  He was given a tour of the company’s space-suit-development facilities.  Overall, he was given “a real red-carpet treatment,” as he later desribed.  Eventually he was offered the position of manager of engineering for the Industrial Products Division, which included space-suit development, at an annual salary of $13,700 (over $109,000 in 2019 dollars) – solidly above his current salary.  Wohlgemuth accepted the offer.

The next morning, Wohlgemuth informed his boss at Goodrich, Carl Effler, who was not happy.  The morning after that, Wohlgemuth told Wayne Galloway – with whom he had worked closely – of his decision.

Galloway replied that in making the move Wohlgemuth would be taking to Latex certain things that did not belong to him – specifically, knowledge of the processes that Goodrich used in making space suits.

Galloway got upset with Wohlgemuth.  Later Effler called Wohlgemuth to his office and told him he should leave the Goodrich offices as soon as possible.  Then Galloway called him and told him the legal department wanted to see him.

While he was not bound to Goodrich by the kind of contract, common in American industry, in which an employee agrees not to do similar work for any competing company for a stated period of time, he had, on his return from the Army, signed a routine paper agreeing ‘to keep confidential all information, records, and documents of the company of which I may have knowledge because of my employment’ – something Wohlgemuth had entirely forgotten until the Goodrich lawyer reminded him.  Even if he had not made that agreement, the lawyer told him now, he would be prevented from going to work on space suits for Latex by established principles of trade-secrets law.  Moreover, if he persisted in his plan, Goodrich might sue him.

To make matters worse, Effler told Wohlgemuth that if he stayed at Goodrich, this incident could not be forgotten and might well impact his future.  Wohlgemuth then informed Latex that he would be unable to accept their offer.

That evening, Wohlgemuth’s dentist put him in touch with a lawyer.  Wohlgemuth talked with the lawyer, who consulted another lawyer.  They told Wohlgemuth that Goodrich was probably bluffing and wouldn’t sue him if he went to work for Latex.

The next morning – Thursday – officials of Latex called him back to assure him that their firm would bear his legal expenses in the event of a lawsuit, and, furthermore, would indemnify him against any salary losses.

Wohlgemuth decided to work for Latex, after all, and left the offices of Goodrich late that day, taking with him no documents.

The next day, R. G. Jeter, general counsel of Goodrich, called Emerson P. Barrett, director of industrial relations for Latex.  Jeter outlined Goodrich’s concern for its trade secrets.  Barrett replied that Latex was not interested in Goodrich trade secrets, but was only interested in Wohlgemuth’s “general professional abilities.”

That evening, at a farewell dinner given by forty or so friends, Wohlgemuth was called outside.  The deputy sheriff of Summit County handed him two papers.

One was a summons to appear in the Court of Common Pleas on a date a week or so off.  The other was a copy of a petition that had been filed in the same court that day by Goodrich, praying that Wohlgemuth be permanently enjoined from, among other things, disclosing to any unauthorized person any trade secrets belonging to Goodrich, and ‘performing any work for any corporation… other than plaintiff, relating to the design, manufacture and/or sale of high-altitude pressure suits, space suits and/or similar protective garments.’

For a variety of reasons, says Brooks, the trial attracted much attention.

On one side was the danger that discoveries made in the course of corporate research might become unprotectable – a situation that would eventually lead to the drying up of private research funds.  On the other side was the danger that thousands of scientists might, through their very ability and ingenuity, find themselves permanently locked in a deplorable, and possibly unconstitutional, kind of intellectual servitude – they would be barred from changing jobs because they knew too much.

Judge Frank H. Harvey presided over the trial, which took place in Akron from November 26 to December 12.  The seriousness with which Goodrich took this case is illustrated by the fact that Jeter himself, who hadn’t tried a case in 10 years, headed Goodrich’s legal team.  The chief defense counsel was Richard A. Chenoweth, of Buckingham, Doolittle & Burroughs – an Akron law firm retained by Latex.

From the outset, the two sides recognized that if Goodrich was to prevail, it had to prove, first, that it possessed trade secrets;  second, that Wohlgemuth also possessed them, and that a substantial peril of disclosure existed;  and, third, that it would suffer irreparable injury if injunctive relief was not granted.

Goodrich attorneys tried to establish that Goodrich had a good number of space-suit secrets.  Wohlgemuth, upon cross-examination from his counsel, sought to show that none of these processes were secrets at all.  Both companies brought their space suits into the courtroom.  Goodrich wanted to show what it had achieved through research.  The Latex space suit was meant to show that Latex was already far ahead of Goodrich in space-suit development, and so wouldn’t have any interest in Goodrich secrets.

On the second point, that Wohlgemuth possessed Goodrich secrets, there wasn’t much debate.  But Wohlgemuth’s lawyers did argue that he had taken no papers with him and that he was unlikely to remember the details of complex scientific processes, even if he wanted to.

On the third point, seeking injunctive relief to prevent irreparable injury, Jeter argued that Goodrich was the clear pioneer in space suits.  It made the first full-pressure flying suit in 1934.  Since then, it has invested huge amounts in space suit research and development.  Jeter characterized Latex as a newcomer intent on profiting from Goodrich’s years of research by hiring Wohlgemuth.

Furthermore, even if Wohlgemuth and Latex had the best of intentions, Wohlgemuth would inevitably give away trade secrets.  But good intentions hadn’t been demonstrated, since Latex deliberately sought Wohlgemuth, who in turn justified his decision in part on the increase in salary.  The defense disagreed that trade secrets would be revealed or that anyone had bad intentions.  The defense also got a statement in court from Wohlgemuth in which he pledged not to reveal any trade secrets of B. F. Goodrich Company.

Judge Harvey reserved the decision for a later date.  Meanwhile, the lawyers for each side fought one another in briefs intended to sway Judge Harvey.  Brooks:

…it became increasingly clear that the essence of the case was quite simple.  For all practical purposes, there was no controversy over facts.  What remained in controversy was the answer to two questions:  First, should a man be formally restrained from revealing trade secrets when he has not yet committed any such act, and when it is not clear that he intends to?  And, secondly, should a man be prevented from taking a job simply because the job presents him with unique temptations to break the law?

The defense referred to “Trade Secrets,” written by Ridsdale Ellis and published in 1953, which stated that usually it is not until there is evidence that the employee has not lived up to the contract, written or implied, that the former employer can take action.  “Every dog has one free bite.”

On February 20, 1963, Judge Harvey delivered his decision in a 9-page essay.  Goodrich did have trade secrets.  And Wohlgemuth could give these secrets to Latex.  Furthermore, there’s no doubt Latex was seeking to get Wohlgemuth for his specialized knowledge in space suits, which would be valuable for the Apollo contract.  There’s no doubt, wrote the judge, that Wohlgemuth would be able to disclose confidential information.

However, the judge said, in keeping with the one-free-bite principle, an injunction against disclosure of trade secrets cannot be issued before such disclosure has occurred unless there is clear and substantial evidence of evil intent on the part of the defendant.  In the view of the court, Wohlgemuth did not have evil intent in this case, therefore the injunction was denied.

On appeal, Judge Arthur W. Doyle partially reversed the decision.  Judge Doyle granted an injunction against Wohlgemuth from disclosing to Latex any trade secrets of Goodrich.  On the other hand, Wohlgemuth had the right to take a job in a competitive industry, and he could use his knowledge and experience – other than trade secrets – for the benefit of his employer.  Wohlgemuth was therefore free to work on space suits for Latex, provided he didn’t reveal any trade secrets of Goodrich.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  http://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: jb@boolefund.com

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.