Notes on — Investing: The Last Liberal Art — by Robert G. Hagstorm
Lessons for a Gazelle CEO
At great CEO is a great investor. Drawing on value investing principles, Robert G. Hagstorm presents a coherent argument against the efficient market hypothesis by drawing worldly wisdom from the major disciplines. I believe that a Gazelle CEO must study multi disciplinary ideas to become effective and this book is a great start to spur that curiosity. After reading this book, it becomes clear that although the market is far from efficient, value investing — or determining the true intrinsic value of a company — is far harder than must pundits make it out to be.
A Gazelle CEO must become a value investor of his business, Investing: The Last Liberal Art gives a good framework to approach this.
my notes
From each discipline the thoughtful person draws significant mental models, the key ideas that combine to produce cohesive understanding. Those who cultivate this broad view are well on their way to achieving worldly wisdom, that solid mental foundation without which success in the market — or anywhere else — is merely a short-lived fluke.
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That is the topmost payoff — broader understanding makes us better investors.
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How does one achieve worldly wisdom? To state the matter concisely, it is an ongoing process of, first, acquiring significant concepts — the models — from many areas of knowledge and then, second, learning to recognize patterns of similarity among them. The first is a matter of educating yourself; the second is a matter of learning to think and see differently.
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Thorndike is his groundbreaking research into how learning takes place, in humans as well as animals. Thorndike was the first to develop what we now recognize as the stimulus-response framework in which learning occurs when associations — connections — are formed between stimuli and response.
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That is, if we understand A, and recognize something in B that resembles A, then we are well on our way to understanding B. In this view, learning new concepts has less to do with a change in a person’s learning ability than with the existence of commonalties.
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Edward Thorndike’s theory of learning lies at the core of a contemporary theory in cognitive science called connectionism. (The cognitive sciences encompass how the brain works — how we think, learn, reason, remember, and make decisions.) Connectionism, building from Thorndike’s studies of stimulus-response patterns, holds that learning is a process of trial and error in which favorable responses to new situations (stimuli) actually alter the neural connections between brain cells.
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Artificial neural networks, in turn, are computers that mimic the basic structure of the brain: they consist of hundreds of processing units (analogous to neurons) that are cross connected into a complex network. (Surprisingly, neurons are several orders of magnitude slower than silicon chips, but the brain makes up for this lack of speed by having a massive number of connections that afford enormous efficiencies.)
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First, we must understand the basic disciplines from which we are going to draw knowledge; second, we need to be aware of the use and benefit of metaphors.
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In our case, the main subject we wish to understand better (the target model) is the stock market or the economy. Over the years we have accumulated countless source models within the finance discipline to explain these phenomena, but too often they fail us.
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true learning and lasting success come to those who make the effort to first build a latticework of mental models and then learn to think in an associative, multidisciplinary manner.
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Physics is the science that investigates matter, energy, and the interaction between them — the study, in other words, of how our universe works. It encompasses all the forces that control motion, sound, light, heat, electricity, and magnetism and their occurrence in all forms, from the smallest subatomic particles to the entire solar system. It is the intellectual foundation of many well-recognized principles such as gravitation and such mindboggling modern concepts as quantum mechanics and relativity.
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Our ability to answer even the most fundamental aspects of human existence depends largely upon measuring instruments available at the time and the ability of scientists to apply rigorous mathematical reasoning to the data. The second influence on Newton’s thinking
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Descartes promoted a mechanical view of the world. He argued that the only way to understand how something works is to build a mechanical model of it, even if that model is constructed only in our imagination.
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At its core, this is the definition of physics: reducing phenomena into a few fundamental particles and defining the forces that act on those particles.
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For his part, Samuelson believed stock prices bounced around because of the perceived uncertainty over a stock’s future value. Whether IBM is worth a hundred dollars per share or fifty dollars per share is a debate in the marketplace over the future growth of its earnings, the competitive landscape, and attitudes about inflation and interest rates.
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Fama’s message was clear. Stock prices are unpredictable because the market is too efficient. In an efficient market, a great many smart people (Fama called them “rational profit maximizers”) have simultaneous access to all the relevant information, and they aggressively apply that information in a way that causes prices to adjust instantaneously — thus restoring equilibrium — before anyone can profit. Predictions about the future therefore have no place in an efficient market, because share prices fully reflect all available information.
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“In equilibrium, there is a simple linear relationship between the expected return and standard deviation of return (defined as risk).”11 According to Sharpe, the only way to achieve a greater return is to incur additional risk. To increase expected returns, investors need only march further out the capital market line.
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complex adaptive systems — those systems with many interacting parts that are continually changing their behavior in response to changes in the environment.
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Every complex adaptive system is actually a network of many individual agents all acting in parallel and interacting with one another. The critical variable that makes a system both complex and adaptive is the idea that agents (neurons, ants, or investors) in the system accumulate experience by interacting with other agents and then change themselves to adapt to a changing environment.
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If a complex adaptive system is, by definition, continuously adapting, it is impossible for any such system, including the stock market, ever to reach a state of perfect equilibrium.
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The counterview from Santa Fe suggests the opposite: a market that is not rational, is organic rather than mechanistic, and is imperfectly efficient. It assumes the individual agents are, in fact, irrational and hence will misprice securities, creating the possibility for profitable strategies.
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Louis Bachelier’s equal number of buyers and sellers who exhibit Samuelson’s rationality and process Fama’s perfect information is obviously at odds with what occurs in the real world of investing. Investment professionals who continue to promote an idealized system over what is exhibited in the real system may be leading us down the wrong path.
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Turning to biology for insight into finance and investing may at first seem a startling move, but just as we did in our study of physics, we focus here on just one core idea from the field of biology: evolution. Whereas in nature the process of evolution is one of natural selection, seeing the market within an evolutionary framework allows us to observe the law of economic selection.
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By this process of natural selection, Darwin theorized, favorable variations are preserved and transmitted to succeeding generations. After several generations, small gradual changes in the species begin to add up to larger changes — thus, evolution occurs.
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Austrian-born Joseph Schumpeter, educated in both economics and law, published his first book, titled The Nature and Essence of Economic Theory. In it, he sought to differentiate the conventional static view of the economy with his more dynamic theory. In that first book, Schumpeter advanced the argument that economics is essentially an evolutionary process. He expanded that theme in his next book, The Theory of Economic Development (1911), and continued to develop it throughout his life.7 In fact, Christopher Freeman, a twentieth-century British economist who studied Schumpeter extensively, comments: “The central point of his whole life work is that capitalism can only be understood as an evolutionary process of continuous innovation and creative destruction.”
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Without the entrepreneur’s desire and willingness to press forward, many great ideas could never be launched. Lastly, Schumpeter explained, great innovations led by great entrepreneurs can thrive only in certain environments. Such things as property rights, stable currencies, and free trade are all important environmental factors, but credit is paramount. Without access to credit, the ability to promote innovation would be hamstrung.
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Principles of Economics (1920) was published. Here, for perhaps the first time, Marshall clearly and eloquently presented his ideas on evolutionary economics.
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Proponents of the dominant paradigm, when confronted with a new and alternative paradigm, are left with two choices. They can jettison their long-held beliefs and divorce themselves from a lifelong intellectual and professional investment, or they can stand and fight. In the second case, we have what is known as a “paradigm collision,” and the tactics for dealing with it are straightforward. First, you seek to discredit the new paradigm in any manner possible; then you begin to repair the dominant paradigm so it better explains the environment. For example, when the geocentric view of the solar system was challenged by Copernicus’s evidence that the earth was not the center of the universe, adherents to Ptolemy’s Almagest simply added orbital rings to his elliptical spheres to explain away the anomalies. When that didn’t work, they threw Copernicus in prison until he recanted his theory. In the midst of a paradigm collision, the scientific community bifurcates.
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the new paradigm is indeed robust, over time it will attract more scientists. If the older paradigm cannot compete, lacking any new recruits it will slowly fade away. It is, we might say, undergoing a kind of evolution.
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But no matter its pace, we must remember there is always change. And this is why we must leave Newton’s world and embrace Darwin’s. In Newton’s world, there is no change. You can run his physics experiments thousands of times for thousands of years and always get the same result. But not so with Darwin and not so with economics. Companies, industries, and economies may mark time with no discernible changes, but inevitably they do change. Whether gradually or suddenly, the familiar paradigm crumbles.
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Brian Arthur, formerly at Stanford University and a visiting professor at the Santa Fe Institute, was one of the first modern economists willing to take a fresh look at how economics really works.
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Common to the study of complexity is the notion that complex adaptive systems operate with multiple elements, each adapting or reacting to the patterns the system itself creates. Complex adaptive systems are in a constant process of evolving over time. These types of systems are familiar to biologists and ecologists, but the group at Santa Fe thought that perhaps the concept should be expanded, that maybe now the time had come to include the study of economic systems and stock markets within the overarching idea of complexity.
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1. Dispersed interaction: What happens in the economy is determined by the interactions of a great number of individual agents all acting in parallel. The action of any one individual agent depends on the anticipated actions of a limited number of agents as well as on the system they cocreate.
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2. No global controller: Although there are laws and institutions, there is no one global entity that controls the economy. Rather, the system is controlled by the competition and coordination between agents of the system.
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3. Continual adaptation: The behavior, actions, and strategies of the agents, as well as their products and services, are revised continually on the basis of accumulated experience. In other words, the system adapts. It creates new products, new markets, new institutions, and new behavior. It is an ongoing system.
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4. Out-of-equilibrium dynamics: Unlike the equilibrium models that dominate the thinking in classical economics, the Santa Fe group believed the economy, because of constant change, operates far from equilibrium.
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Will the market ever become efficient? If you accept the idea that evolution plays a role in financial markets the answer would have to be no. Each strategy that eliminates an inefficiency will soon be replaced in turn by a new strategy. The market will always maintain some level of diversity, and this we know is a principal cause of evolution.
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In Lo’s opinion, the market is neither exclusively efficient nor always behavioral — it is both. “Behavior is really the outcome of interactions between our logical faculties and our emotional responses,” he explains. “When logic and emotions are in proper balance, markets operate in a relatively efficient manner.”16 (We will look more closely at the tug-of-war between logic and emotion and its impact on investors in a later chapter.)
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Indeed, the movement from the mechanical view of the world to the biological view of the world has been called the “second scientific revolution.” After three hundred years, the Newtonian world, the mechanized world operating in perfect equilibrium, is now the old science.
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The new science is connected and entangled. In the new science, the system is nonlinear and unpredictable, with sudden and abrupt changes. Small changes can have large effects while large events may result in small changes. In nonlinear systems, the individual parts interact and exhibit feedback effects that may alter behavior. Complex adaptive systems must be studied as a whole, not in individual parts, because the behavior of the system is greater than the sum of the parts.
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The first principle: Never advise anyone to buy or sell shares. Where perspicacity is weakened, the most benevolent piece of advice can turn out badly. The second principle: Take every gain without showing remorse about missed profits. It is wise to enjoy what is possible without hoping for the continuance of a favorable conjuncture and the persistence of good luck. The third principle: Profits on the exchange are the treasures of goblins. At one time they may be carbuncle stones, then coals, then diamonds, then flint-stones, then morning dew, then tears. The fourth principle: Whoever wishes to win in this game must have patience and money, since values are so little constant and the rumors so little founded on truth. He who knows how to endure blows without being terrified by the misfortune resembles the lion who answers the thunder with a roar and is unlike the hind who, stunned by the thunder, tries to flee.
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The relationship between the individual investor and the stock market, which in itself is nothing more than a collection of individuals, is a profound puzzle. For over four hundred years, it has perplexed the rich and the poor as well as the genius and the dimwitted, and it is the story of our current chapter on social systems.
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Self-organized systems, explains Johnson, have three distinct characteristics. First, the complex global behavior occurs by simple connected local processors. In a social system, the local processors are individuals. Second, a solution arises from the diversity of the individual inputs. Third, the functionality of the system, its robustness, is far greater than any one of the individual processors.
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The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations. Written by James Surowiecki,
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According to Surowiecki, the two critical variables necessary for a collective to make superior decisions are diversity and independence. If a collective is able to tabulate decisions from a diverse group of individuals who have different ideas or opinions on how to solve a problem, the results will be superior to a decision made by a group of like-minded thinkers. Independence, the second critical variable, does not mean each member of the group must remain in isolation but rather each member of the group is basically free from the influence of other members.
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We know the stock market is an incentive-based system that can aggregate investor decisions. What we need to understand is the market’s level of diversity and the independence of its participants. If the stock market is adequately diversified and, most importantly, if the decisions of its participants have been reached independently, then it is likely the market is efficient. Surowiecki reminds us that just because we can observe some irrational investors, that does not necessarily mean the market is inefficient. Indeed, proponents of the efficient market hypothesis have latched onto the “wisdom of the crowds” as a plausible explanation for market efficiency.
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But what if independence is lost? What if the decisions of the market’s participants are not independent but are now coalesced into one opinion? When this occurs, the system has effectively lost its diversity and along with it any chance of generating an optimal solution. If diversity is the key to how collectives can best reach solutions, then diversity breakdowns are the cause of suboptimal outcomes — or in the case of the stock market, diversity breakdowns cause the market to become inefficient. Scientists are now turning their attention to understanding what causes diversity breakdowns. Michael Mauboussin, author of two very important books, More Than You Know: Finding Financial Wisdom in Unconventional Places and Think Twice: Harnessing the Power of Counterintuition, tells us “information cascades (which can lead to diversity breakdowns) occur when people make decisions based on the actions of others rather than on their own private information. These cascades help explain booms, fads, fashions, and crashes.”12 Social network theorists, who view social relationships in terms of nodes and ties, whereby nodes are the individual actors and ties are the relationships between the actors, consider this to be the proper framework for understanding how information cascades can sweep across large populations.
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Per Bak’s sand pile metaphor is a powerful tool that helps us understand the behavior of many different systems. In both natural and social systems, we can see the dynamic: the systems become a class of interlocking subsystems that organize themselves to the edge of criticality and, in some cases, break apart violently only to reorganize themselves at a later point. Is the stock market such a system? Absolutely, said Per Bak.
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At this point, we have a fixed compass on how to analyze social systems. Whether they are economic, political, or social, we can say these systems are complex (they have a large number of individual units), and they are adaptive (the individual units adapt their behavior on the basis of interactions with other units as well as with the overall system). We also recognize that these systems have self-organizing properties and that, once organized, they generate emergent behavior. Finally, we realize that complex adaptive systems are constantly unstable and periodically reach a state of self-organized criticality. We come to these conclusions by studying a large number of complex adaptive systems across a wide variety of fields in both the natural and the social sciences. In all our study, we are currently limited to understanding how the systems have behaved so far. We have not made the scientific leap that will enable us to predict the future behavior, particularly in complex social systems involving the highly unpredictable units known as human beings. But we may be on the track of something even more valuable. What separates the study of complex natural systems from complex social systems is the possibility that in social systems we can alter the behavior of their individual units. Whereas we cannot as of yet change the trajectory of hurricanes, where groups of people are concerned we may be able to affect the outcome by influencing how individuals respond in various situations. To say this another way, although self-organized criticality is an inherent property of all complex adaptive systems, including economic systems, and although some degree of instability is unavoidable, we may be able to alter potential landslides by better understanding what makes criticality inevitable.
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Fortunately, much of their research is included in a wonderful collection of essays titled Judgment under Uncertainty: Heuristics and Biases (1982). Here you will find all the customary behavioral finance terms we have come to know and understand: anchoring, framing, mental accounting, overconfidence, and overreaction bias. But perhaps the most significant insight into individual behavior was the concept of loss aversion.
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Kahneman and Tversky’s research demonstrated that how alternatives are framed can make a significant difference in how individuals reach conclusions.
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that people are generally risk averse when making a decision that offers hope of a gain but risk seeking when making a decision that will lead to certain loss.
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Under prospect theory, resting on its core notion of loss aversion, value is assigned to gains and losses. What Kahneman and Tversky were able to prove is that people do not look just at the final level of wealth but rather at the incremental gains and losses that contribute to this wealth.
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The most important discovery in prospect theory was the realization that individuals are, in fact, loss averse. Kahneman and Tversky were able to prove mathematically that individuals regret losses more than they welcome gains of the exact same size — two to two and one-half times more. It was a stunning revelation.
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Richard Thaler, a former Cornell economist, is now a professor of behavioral science and economics at the Chicago Booth School of Business. His research focus is questioning the rational behavior of investors.
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The Winner’s Curse: Paradoxes and Anomalies of Economic Life (1992).
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First, that the equity risk premium is puzzling, and second, that loss aversion, unequivocally identified by Kahneman and Tversky, is illogical and prevents investors from seeing long term; that is, it makes them myopic.
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Equity risk premium is a term many investors have heard but few actually understand. It refers to the potential for higher returns represented by the inherently risky stock market compared to the risk-free rate, defined as the rate of a ten-year U.S. Treasury bond in effect at whatever point you’re considering.
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If you don’t check your portfolio every day, you will be spared the angst of watching daily price gyrations; the longer you hold off, the less you will be confronted with volatility and therefore the more attractive your choices seem.
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principle.”2 In my opinion, the single greatest psychological obstacle that prevents investors from doing well in the stock market is myopic loss aversion.
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By way of example, Buffett the businessperson understands that so long as the economics of his companies continue to advance in a steady manner, the value of his investment will continue to march upward. He does not need the market’s affirmation to convince him of this. As he often states, “I don’t need a stock price to tell me what I already know about value.”
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to endure blows without being terrified by the misfortune resembles the lion who answers the thunder with a roar and is unlike the hind who, stunned by the thunder, tries to flee.”
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Investors must be prepared, he cautioned, for ups and downs in the market. And he meant prepared psychologically as well as financially–not merely knowing intellectually that a downturn will happen, but having the emotional wherewithal to react appropriately when it does. And what is the appropriate reaction? In his view, an investor should do just what a business owner would do when offered an unattractive price — ignore it.
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“The investor who permits himself to be stampeded or unduly worried by unjustified market declines in his holdings is perversely transforming his basic advantage into a basic disadvantage,”
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every case, he found two amazing trends: (1) the stocks that the investors bought consistently trailed the market, and (2) the stocks that they sold actually beat the market.
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“The Internet has brought changes to investing which may bolster the overconfidence of on-line investors by providing an illusion of knowledge and an illusion of control,” they explain. “When people are given more information on which to base a forecast or assessment, their confidence in the accuracy of their forecasts tends to increase more quickly (and this is the important part) than the accuracy of the forecasts.”
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Think back to the 1995 study conducted by Thaler and Benartzi, which gave us the term “myopic loss aversion.” They found that measuring stock performance in one-hour increments generated the worse negative utility for investors.
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investors are acting out what is called the “Walter Mitty effect.”10 Walter Mitty is a fictional character in James Thurber’s wonderful short story “The Secret Life of Walter Mitty.” It was first published in the The New Yorker in 1939 and later made into a movie (1947) starring Danny Kaye. Walter Mitty was a meek fellow totally intimidated by his overbearing wife. He coped by daydreaming he was magically transformed into a courageous hero. One minute he was dreading facing his wife’s sharp tongue; the next, he was a fearless bomber pilot undertaking a dangerous mission alone. Pruitt believes investors react to the stock market the way Walter Mitty reacted to life. When the market is doing well, they become brave in their own eyes and eagerly accept more risk. But when the market goes down, they rush for the door. So when you ask an investor directly to explain their risk tolerance, the answer comes from either a fearless bomber pilot (in a bull market) or a henpecked husband (in a bear market).
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The first person to propose this thesis was the Scottish psychologist Kenneth Craik. In a short but extraordinary work titled The Nature of Explanation ([1943] 1952), Craik wrote that people are processors of information and that they construct mental models of reality to help anticipate events. With a “small-scale model of external reality and of possible actions” in our head, he believed, we are able to “try out various alternatives, conclude which is the best of them, react to future situations before they arise, utilize the knowledge of past events in dealing with the present and the future, and in every way react in a much fuller, safer, and more competent manner to the emergencies which [we] face.”
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Ongoing research has shown that, overall, our use of mental models is frequently flawed. We construct incomplete representations of the phenomena we are trying to explain. Even when they are accurate, we don’t use them properly. We tend to forget details about the models, particularly when some time has passed, and so our models are often unstable. Finally, we have a distressing tendency to create mental models based on superstition and unwarranted beliefs. Because mental
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mental models determine our actions, we should not be surprised that poorly crafted mental models, built on weak information, lead to poor investment performance.
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Through the forces of evolution we are hardwired to seek patterns to explain our world, and those patterns form the foundation of our belief systems, even when they are inherently specious.
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We were not born in the prehistoric period, argues Shermer, but our minds were built there, and we function largely as we have throughout human history. We still succumb to magical thinking because as pattern-seeking animals we need explanations even for the unexplainable. We distrust chaos and disorder, so we demand answers, even if they are a product of magical rather than rational thinking. That which can be explained scientifically, is. That which cannot is left to magical thinking.
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The Believing Brain (2011), Shermer tells us our superstitions are a product of the spurious identification of patterns. As such, beliefs precede reasoning. Our brains are belief engines that naturally look for patterns, which are then infused with meaning. Not surprisingly, we look for information that confirms our beliefs while ignoring information that contradicts them.
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Fischer Black, a man I consider an extraordinary investment professional. Black was professor of finance at both the University of Chicago and MIT before joining Goldman Sachs. He is perhaps best remembered in the profession for developing, along with Myron Scholes and Robert Merton, the formula we now use for pricing options, but I remember him most for his presidential address to the American Finance Association in 1986. In his talk, titled simply “Noise,” this well-respected academician fearlessly took exception with his academic colleagues and challenged the widely accepted thesis that stock prices are rational. Rather than pure information leading to rational prices, Black believed that most of what is heard in the market is noise, leading to nothing but confusion. Investor confusion, in turn, further escalates the noise level. “Noise,” said Black, “is what makes our observations imperfect.”17 The net effect of noise that builds in the system, he explained, makes prices less informative for the producers and consumers who use them to guide their economic decisions.
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The other side of our correcting device, the side that faces the receiving terminal, is responsible for verifying that the information is properly passed through and accurately received, without interference of psychological biases. The process for doing this is also within our control, but it is challenging. We must make ourselves aware of all the ways that emotion-based errors and errors of thinking can interfere with good investing decisions, as described in this chapter, and we must constantly be on guard against our own psychological missteps.
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Charlie Munger, who gave us the concept of mental models, has spent much time thinking about how we accumulate bits of knowledge from various fields to achieve worldly wisdom. In investing, he says, obviously we need to understand basic accounting and finance. And as we will see in our chapter on mathematics, it is equally important to understand statistics and probabilities. But he believes one of the most important fields is psychology, especially what he calls the psychology of misjudgment.
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“First, what are the factors that really govern the interests involved, rationally considered? And second, what are the subconscious influences where the brain at a subconscious level is automatically doing these things — which by and large are useful, but often misfunction.”19 In his own way, Charlie has developed the kind of “correcting device” that
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A philosopher, then, is a person who loves wisdom and is dedicated to the search for meaning. The pursuit of wisdom is an active, unending process of discovery. The true philosopher is filled with the passion to understand, a process that never ends.
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categories. First, critical thinking as it applies to the general nature of the world is known as metaphysics.
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Metaphysics means “beyond physics.” When philosophers discuss metaphysical questions, they are describing ideas that exist independently from our own space and time.
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The second body of philosophical inquiry is the investigation of three related areas: aesthetics, ethics, and politics. Aesthetics is the theory of beauty.
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Ethics is the philosophical branch that studies the issues of right and wrong.
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Epistemology, the third body of inquiry, is the branch of philosophy that seeks to understand the limits and nature of knowledge.
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Thinking is much more than just acquiring knowledge, and the process of thinking can be done badly or well. By learning to think well, we can better avoid confusion, noise, and ambiguities. Not only will we become more aware of possible alternatives, we will be more capable of making reliable arguments. How we think about investing ultimately determines how we do it. If we
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One of the underlying themes that runs throughout this book is the idea that the market is a complex adaptive system, which reflects all the characteristics of such a system.
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Ontological limits are caused by the nature of things; epistemological limits are caused by limited understanding.
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Are complex adaptive systems really unexplainable (ontological) or are they only unexplainable because of our limited ability to understand them (epistemological)?
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Why should investors care about a half-century-old debate between humanists and scientists? Because the narratives investors use to explain the market or economy sometimes lack the statistical rigor required for a proper description. And as we have learned, if the description is faulty the explanation is likely wrong.
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It is through thinking that people resolve doubts and form their beliefs, and their subsequent actions follow from those beliefs and become habits. Therefore anyone who seeks to determine the true definition of a belief should look not at the belief itself but at the actions that result from it.
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state the matter as simply as possible, pragmatism holds that truth (in statements) and rightness (in actions) are defined by their practical outcomes. An idea or an action is true, and real, and good, if it makes a meaningful difference. To understand something, then, we must ask what difference it makes, what its consequences are. “Truth,” James wrote, “is the name of whatever proves itself to be good in the way of belief.”
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What happens, to summarize James, is that the new idea is adopted while the older truths are preserved with as little disruption as possible. The new truths are simply go-betweens, transition-smoothers, that help us get from one point to the next.
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For the pragmatist, the reliance is not on absolute standards and abstract ideals but rather on results — those things that are actually working and that help you reach your goals. Investors are acutely interested in understanding what is working in the market so they too can reach their goals. They recognize the limitations of investment models and are quick to recognize that any model is highly sensitive to the purposes for which it was developed.
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What is the best measure of value? Most believe John Burr Williams’s theory of discounted cash flow (DCF) is the best model for determining economic value. We should think of Williams’s DCF model as being a “first-order model.”
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Pragmatic investors can, and should, apply any second-order model that is fruitful and discard any that are worthless, all without violating the first order.
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The only way to do better than someone else, or more importantly, to outperform the stock market, is to have a way of interpreting the data that is different from other people’s interpretations. To that I would add the need to have sources of information and experiences that are different.26 In studying the great minds in investing, the one trait that stands out is the broad reach of their interests. Once your field of vision is widened, you are able to understand more fully what you observe, and then you use those insights for greater investment success.
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The “cash-value” of studying philosophy is very real. Put quite simply, it teaches you to think better. Once you commit yourself to philosophy, you find that you have set yourself on a course of critical thinking. You begin to look at situations differently and to approach investing in a different manner. You see more, you understand more. Because you recognize patterns, you are less afraid of sudden changes. With a perpetually open mind that relishes new ideas and knows what to do with them, you are set firmly on the right path.
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So we are talking about learning to be discriminating readers: to analyze what you read, to evaluate its worth in the larger picture, and to either reject it or incorporate it into your own latticework of mental models.
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You will recall from Chapter 5 that one way through the psychological quicksand threatening our ability to make good investment decisions is communication — the transmittal of accurate and complete information, free of noise. The entire communication chain must be noise-free, starting with the original information being transmitted. Ideally, that information will be accurate and true (otherwise all we are doing is correctly transmitting error), it will be reasonably relevant to the matter at hand (otherwise we are spinning our wheels), and it will address the underlying question (otherwise we are merely regurgitating data and not increasing insight).
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Once we develop the skills of a discerning reader, we will be able to decide whether what we are reading is worth passing through the communication channel. This is extremely important for those of us involved with investing and finance, for the sheer volume of reading material all but guarantees that some of it will be of marginal value. For simple self-preservation, we must be able to winnow out the good from the not so good. For us to be able to start the communication chain with good information, we need to develop the skill of discrimination: learning to select, from the sea of information that threatens to drown us, that which will truly add to our knowledge. That is the focus of this chapter: making good choices about what to read and reading in an intelligent, perceptive way so as to enhance knowledge. And here the students of St. John’s give us a very valuable tool.
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How to Read a Book, by Mortimer J. Adler.
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The central purpose for reading a book, Adler believes, is to gain understanding. (For the time being we will set aside the idea of reading for pleasure.) That is not the same as reading for information. The distinction is extremely important, and I believe it is especially important for investors.
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There is a simple way to tell the difference between collecting information and gaining understanding. Any time you read something and find you can easily “get it,” chances are you are just cataloging information. But when you come across a work that makes you stop, think, and reread for clarification, chances are this process is increasing your understanding.
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Adler proposes that all active readers need to keep four fundamental questions in mind:6 1. What is the book about as a whole? 2. What is being said in detail? 3. Is the book true, in whole or part? 4. What of it? No matter how long the material, its format (fiction or nonfiction), or the immediate purpose for reading it (to gain information, to enhance broader knowledge, or for sheer pleasure), you should always be evaluating the material from the perspective of these four fundamental questions if you want to read intelligently.
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To determine, as quickly as possible, what the book is about (question 1), Adler suggests a fast review. First, read the preface. Here the author typically gives a brief explanation of the book, the rationale for writing it, and perhaps an outline of what to expect. Next, look carefully at the table of contents; it will give you a good overview of what the book is about. Then turn to the back and run through the index, looking for familiar as well as unfamiliar terms. This will give you a sense of the book’s major topics. You can also learn much about the book from its bibliography. Do you recognize the names of the authors referenced and have you read any of their work? Then read a few paragraphs here or there, perhaps from a section that discusses a topic you are somewhat familiar with. After that systematic skimming, turn to the very end and read the author’s summation of the book, if there is one. This entire exercise, from reading the preface, table of contents, index, and bibliography to systematically skimming, should take at most thirty minutes to an hour.
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If you do, Adler suggests you start with a complete but somewhat superficial reading. Here you will begin to answer the second fundamental question: What is the book about in detail? That will tell you whether you want to invest the time for a serious, analytical read. The goal now is to get through the book without getting bogged down in small distractions such as unfamiliar vocabulary. Pay attention to what you understand, and skip over the parts that are difficult. Caution: This requires concentration. Even though you are skimming the book, you should not let yourself daydream. Stay alert and focus on what you are reading so that you can comprehend the basics of the material. Adler suggests we adopt the role of a detective, constantly looking for clues that will tell us if the book deserves a deeper examination. If it does, you move to what Adler calls analytical reading, the most thorough and complete way to absorb a book. Through analytical reading, you will reinforce your answers to the first two fundamental questions (what the book is about as a whole and in detail), and you will begin to answer the third question: Is the book true?
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Analytical reading has three goals: (1) to develop a detailed sense of what the book contains, (2) to interpret the contents by examining the author’s own particular point of view on the subject, and (3) to analyze the author’s success in presenting that point of view convincingly. You may find it helpful at first to approach analytical reading the way you would approach assigned reading in a college class. Have a notepad at hand, and make your own outline of the key topics, chapter by chapter. Write down, in your own words, what you deduce is the author’s main purpose in writing the book. List what you think are the author’s main primary arguments, and then compare that list against the outline of contents. Decide for yourself whether the author has fulfilled the original goals, defended the arguments, and convinced you of the main thesis. Ask yourself whether the author seems illogical or presents material that you know from other sources is inaccurate. If something seems incomplete or unsatisfactory, does the author candidly acknowledge that a full answer was not possible, rather than trying to bluff the readers?
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What of it? That is to say, what is the significance of this material? A full answer to that question, however, comes only at a still-deeper level of reading, what Adler calls synoptical reading, or comparative reading. (We’ll use the latter term here, for I believe it is more descriptive.) In this level of reading, we are interested in learning about a certain subject, and to do so we compare and contrast the work of several authors rather than focusing on just one work by one author. Adler considers this the most demanding and most complex level of reading. It involves two challenges: first, searching for other possible books on the subject and then deciding, after finding them, which books should be read. Once you have identified the subject you wish to study, the next step is to construct a bibliography. Depending on the subject, the bibliography might include a few books or many. To read that number of books analytically would take months, maybe years. Comparative readers must use shortcuts, inspecting each book to ensure it has something important to say about the subject and then discarding less relevant ones. Once you have decided which books to include, you are ready to begin.
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Looking back over Adler’s complete program, we take note of the connections he has carefully built. Each level of reading is connected to the next, and the process is cumulative. We cannot hope to reach the highest level of reading until we master the earlier ones.
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The challenge for us as readers is to receive that knowledge and integrate it into our latticework of mental models. How well we are able to do so is a function of two very separate considerations: the author’s ability to explain, and our skills as careful, thoughtful readers. We have little control over the first, other than to discard one particular book in favor of another, but the second is completely within our control.
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Richard Dawkins, James Gleick, Stephen Jay Gould, Stephen Hawking, George Johnson, Scott Page, Mitchell Waldrop, and others have written books on science that can be read by the average person. Richard Feynman wrote several books on physics that are accessible for nonphysicists, and Murray Gell-Mann’s The Quark and Jaquar manages to deal with physics and complexity without intimidating the rest of us.
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The mental skill of critical analysis is fundamental to success in investing. Perfecting that skill — developing the mind-set of thoughtful, careful analysis — is intimately connected to the skill of thoughtful, careful reading. Each one reinforces the other in a kind of double feedback loop. Good readers are good thinkers; good thinkers tend to be great readers and in the process learn to be even better thinkers.
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Or, to put the matter more directly, learning to be a careful reader has two enormous benefits to investors: it makes you smarter in an overall sense, and it makes you see the value of developing a critical mind-set, not necessarily taking information at face value.
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This is the power of imaginative literature: it helps us more poignantly know what we know, feel what we feel, believe what we believe.
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Just as we learn from our daily experiences how to become better mates, parents, citizens, and investors, so too can we learn from the fictional experiences that fine writers place in our imagination.
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a fundamental way, solving a mystery is similar to figuring out whether a security is priced accurately. Both are puzzles. The detective gathers clues to determine whether a suspect is guilty or innocent. A security analyst gathers financial data and industry facts to determine whether the market is accurately assessing a company’s value, in the form of its stock price, that particular day.
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1. Develop a skeptic’s mindset; don’t automatically accept conventional wisdom. 2. Conduct a thorough investigation.
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1. Begin an investigation with an objective and unemotional viewpoint. 2. Pay attention to the tiniest details. 3. Remain open-minded to new, even contrary, information. 4. Apply a process of logical reasoning to all you learn.
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1. Become a student of psychology. 2. Have faith in your intuition. 3. Seek alternative explanations and redescriptions.
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“The formula we use for evaluating stocks and businesses is identical. Indeed, the formula for valuing all assets that are purchased for financial gain has been unchanged since it was first laid out by a very smart man in about 600 B.C.E. The oracle was Aesop and his enduring, though somewhat incomplete, insight was ‘a bird in the hand is worth two in the bush.’ To flesh out this principle, you must answer only three questions. How certain are you that there are indeed birds in the bush? When will they emerge and how many will there be? What is the risk-free interest rate? If you can answer these three questions, you will know the maximum value of the bush — and the maximum number of birds you now possess that should be offered for it. And, of course, don’t literally think birds. Think dollars.”
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First, tabulate the cash. Second, estimate the growth probabilities of the cash coming and going over the life of the business. Then, discount the cash flows to present value.
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We now know probability theory is a potent instrument for forecasting. But, as we also know, the devil is in the details. In our case, the details are the quality of information, which forms the basis for the probability estimate. The first person to think scientifically about probabilities and information quality was Jacob Bernoulli, a member of the famed Dutch-Swiss family of mathematicians that also included both Johann and Daniel Bernoulli.
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Bayesian analysis is an attempt to incorporate all available information into a process for making inferences, or decisions. Colleges and universities use Bayes’s theorem to help students learn decision making. In the classroom, the Bayesian approach is more popularly called the “decision tree theory,” in which each branch of the tree represents new information that, in turn, changes the odds in making decisions.
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The answer is to expand your decision tree to include various time horizons and growth rates. Let’s say you want to determine the value of a certain company, and you know it has grown its cash at a rate of 10 percent in the past. You might reasonably start with an assumption that the company has a 50 percent chance of generating the same growth rate over the next five years, a 25 percent chance of a 12 percent rate, and a 25 percent chance of growing at 8 percent. Then, because the economic landscape invites competition and innovation, you might lower the assumptions for years six through eight, giving it a 50 percent probability of 8 percent growth, a 25 percent probability of 6 percent, and 25 percent probability of 10 percent. Then break the growth assumptions again for years nine and ten.
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Over the years, the Kelly criterion has become a part of the mainstream investment theory. Some believe that both Warren Buffett and Bill Gross, the famed bond portfolio manager at PIMCO, use Kelly methods in managing their portfolio.
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The most important lesson investors can learn from Gould’s experience is to appreciate the differences between the trend of the system and trends in the system. Put differently, investors need to understand the difference between the average return of the stock market and the performance variation of individual stocks. One of the easiest ways for investors to appreciate the differences is to study sideways markets. Most investors
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This experiment, along with others including the study of height variation between parents and their children, became known as regression, or reversion, to the mean. “Reversion,” said Galton, “is the tendency of the ideal filial type to depart from the parent type, reverting to what may be roughly and perhaps fairly described as the average ancestral type.”17 If this process were not at work, explained Galton, then large peas would produce ever-larger peas and small peas would produce ever-smaller peas until we had a world that consisted of nothing but giants and midgets.
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Overemphasizing the present without understanding the subtle shifts in composition can lead to perilous and faulty decisions. Although regression to the mean remains an important strategy, it is imperative that investors remember it is not inviolable. Stocks that are thought to be high in price can still move higher; stocks that are low in price can continue to decline. It is important to remain flexible in your thinking. Although reversion to the mean is the most likely outcome in markets, its presence is not sacrosanct.
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According to Taleb, our assumptions about what is going to happen grow out of the bell-shape curve of predictability — what he calls “Mediocristan.” Instead, the world is shaped by wild, unpredictable, and powerful events he calls “Extremistan.” In Taleb’s world, “history does not crawl, it jumps.”
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This is not to say probability, variance, regression to the mean, and fat tails are useless. Far from it. These mathematical tools have helped us narrow the cone of uncertainty that exists in markets — but not eliminate it. “The recognition of risk management as a practical art rests on a simple cliché with the most profound consequences: when our world was created, nobody remembered to include certainty,” said Peter Bernstein. “We are never certain; we are always ignorant to some degree. Much of the information we have is either incorrect or incomplete.”
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The real trouble with this world of ours is not that it is an unreasonable world, nor even that it is a reasonable one. The commonest kind of trouble is that it is nearly reasonable, but not quite. Life is not an illogicality; yet it is a trap for logicians. It looks just a little more mathematical and regular than it is; its exactitude is obvious, but its inexactitude is hidden; its wildness lies in the wait.23
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For years, psychologists have been interested in the idea that our cognitive processes are divided into two modes of thinking, traditionally referred to as intuition, which produces “quick and associative” cognition, and reason, described as “slow and rule-governed.” Today, these cognitive systems are commonly referred to as System 1 and System 2. System 1 thinking is intuitive. It operates automatically, quickly, and effortlessly with no sense of voluntary control. System 2 is reflective. It operates in a controlled manner, slowly and with effort. The operations of System 2 thinking require concentration and are associated with subjective experiences that have rule-based applications.
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Kahneman believes there are indeed cases where intuitive skill reveals the answer, but that such cases are dependent on two conditions. First, “the environment must be sufficiently regular to be predictable” second, there must be an “opportunity to learn these regularities through prolonged practice.” For familiar examples, think about the games of chess, bridge, and poker. They all occur in regular environments, and prolonged practice at them helps people develop intuitive skill. Kahneman also accepts the idea that army officers, firefighters, physicians, and nurses can develop skilled intuition largely because they all have had extensive experience in situations that, while obviously dramatic, have nonetheless been repeated many times over.
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Kahneman concludes that intuitive skill exists mostly in people who operate in simple, predictable environments and that people in more complex environments are much less likely to develop this skill. Kahneman, who has spent much of his career studying clinicians, stock pickers, and economists, notes that evidence of intuitive skill is largely absent in this group. Put differently, intuition appears to work well in linear systems where cause and effect is easy to identify. But in nonlinear systems, including stock markets and economies, System 1 thinking, the intuitive side of our brain, is much less effectual.
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his own writing about intuition, Kahneman called on a definition developed by Herbert Simon — another psychologist who also won a Nobel Prize in Economics based on his studies of decision making. “The situation has provided a cue; this cue has given the expert access to information stored in memory, and the information provides the answer. Intuition is nothing more and nothing less than recognition.”
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Improving the resource condition of our System 2 thinking — that is to say, deepening and broadening our reserves of relevant information — is the principal reason this book was written.
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appears experts are penalized, like the rest of us, by thinking deficiencies. Specifically, experts suffer from overconfidence, hindsight bias, belief system defenses, and lack of Bayesian process. You may remember these mental errors from our chapter on psychology. Such psychological biases are what penalize System 1 thinking. We rush to make an intuitive decision, not recognizing that our thinking errors are caused by our inherent biases and heuristics. It is only by tapping into our System 2 thinking that we can double-check the susceptibility of our initial decisions.
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Tetlock tells us Foxes have three distinct cognitive advantages. 1. They begin with “reasonable starter” probability estimates. They have better “inertial-guidance” systems that keep their initial guesses closer to short-term base rates. 2. They are willing to acknowledge their mistakes and update their views in response to new information. They have a healthy Bayesian process. 3. They can see the pull of contradictory forces, and, most importantly, they can appreciate relevant analogies.7
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Hedgehogs start with one big idea and follow through — no matter the logical implications of doing so. Foxes stitch together a collection of big ideas. They see and understand the analogies and then create an aggregate hypothesis. I think we can say the fox is the perfect mascot for the College of Liberal Arts Investing.
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Stanovich believes we process poorly. When solving a problem, he says, people have several different cognitive mechanisms to choose from. At one end of the spectrum are mechanisms with great computational power, but they are slow and require a great deal of concentration. At the opposite end of the spectrum are mechanisms that have low computational power, require very little concentration, and make quick action possible. “Humans are cognitive misers,” Stanovich writes, “because our basic tendency is to default to the processing mechanisms that require less computational effort, even if they are less accurate.”9 In a word, humans are lazy thinkers. They take the easy way out when solving problems and as a result, their solutions are often illogical.
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The second cause of dysrationalia is the lack of adequate content. Psychologists who study decision making refer to content deficiency as a “mindware gap.” First articulated by David Perkins, a Harvard cognitive scientist, mindware refers to the rules, strategies, procedures, and knowledge people have at their mental disposal to help solve a problem. “Just as kitchenware consists in tools for working in the kitchen, and software consists in tools for working with your computer, mindware consists in the tools for the mind,” explains Perkins. “A piece of mindware is anything a person can learn that extends the person’s general powers to think critically and creatively.”10
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My hope with this book is that it will inspire you to begin thinking about investing in a different way, as something more than a kaleidoscope of shifting numbers. But thinking about investing differently means thinking creatively. It requires a new and innovative approach to absorbing information and building mental models. You will recall from Chapter 1 that to construct a new latticework of mental models, we must first learn to think in multidisciplinary terms and to collect (or teach ourselves) fundamental ideas from several disciplines, and then we must be able to use metaphors to link what we have learned back to the investing world. Metaphor is the device for moving from areas we know and understand to new areas we don’t know much about. To build good mental models, we need a general awareness of the fundamentals of various disciplines, plus the ability to think metaphorically.
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The process is similar to genetic crossover that occurs in biological evolution. Indeed, biologists agree that genetic crossover is chiefly responsible for evolution. Similarly, the constant recombination of our existing mental building blocks will, over time, be responsible for the greatest amount of investment progress. However, there are occasions when a new and rare discovery opens up new opportunities for investors. In much the same way that a mutation can accelerate the evolutionary process, so too can newfound ideas speed us along in our understanding of how markets work. If you are able to discover a new building block, you have the potential
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But remember, none of this will happen if you conclude that you already know enough. Never stop discovering new building blocks. When a corporation cuts its research and development budget to focus on the here and now, that may produce greater profits in the short term, but more likely it places the company in competitive jeopardy at some point in the future. Likewise, if we stop exploring for new ideas, we may still be able to navigate the stock market for a while, but most likely we are putting ourselves at a disadvantage for tomorrow’s changing environment.
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Those who make an effort to acquire worldly wisdom are beneficiaries of a special gift. Scientists at the Santa Fe Institute call it emergence. Charlie Munger calls it the lollapalooza effect: the extra turbocharge that comes when basic concepts combine and move in the same direction, reinforcing each other’s fundamental truths. But whatever you decide to call it, this broad-based understanding is the foundation of worldly wisdom. The Roman poet Lucretius writes: Nothing is more sweet that full possession Of those calm heights, well built, well fortified By wise men’s teaching, to look down from here At others, wandering below, men lost, Confused, in hectic search for the right road.
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