Humans are Unreliable Experts,
Getting in the Way of Outstanding Performance

Categories Author: Jim O'Shaughnessy, Insight, Investing

“What ails the truth is that it is mainly uncomfortable, and often dull.
The human mind seeks something more amusing, and more caressing.”

— H. L. Mencken

Given our current state of affairs, it seems like right now is a very good time for a refresher on Human Behavior — the one predictable thing in the market. What follows is an adaptation of Chapter 2 from the most recent edition of What Works on Wall Street (WWOWS).1

Everyone is guilty of faulty decision-making, not just the scions of Wall Street. Some scenarios:

  • Accountants must offer an opinion on the credit worthiness of a firm.
  • College Administrators must decide which students to accept into a graduate program.
  • Psychologists must decide if the patient’s problem is neurosis or psychosis.
  • Doctors must decide if it’s liver cancer or not.
  • More prosaically, a Bookie must try to handicap the next horse race.

All the above are activities in which Experts predict outcomes. They occur every day and make up the fabric of our lives. Generally, predictions are made in two ways:

  1. Clinical, Intuitive (the most common approach)
    A person runs through a variety of possible outcomes in their head, essentially relying on personal knowledge, experience, and common sense to reach a decision. This is is how most traditional active Money Managers make choices. Stock Analysts might pore over a company’s financial statements, interview management, talk to customers and competitors, and finally try to make an overall forecast for that company’s health and long-term potential. The graduate school Administrator might use a host of data, from college GPA and interviews with applicants, to determine if a student should be accepted. This type of judgment relies on the perceptiveness of the forecaster. Psychologists have shown in numerous studies that when people are confronted with vast amounts of data, their brains create mental shortcuts, called heuristics,2 to make decisions.
  2. Actuarial, Quantitative
    In this approach, the forecaster makes no subjective judgments, nor do they rely on a rigid, rule-of-thumb heuristic. Rather, conclusions are only drawn after using empirical relationships between the data and the desired outcome are used to. This method relies solely on proven relationships using larger samples of data, in which the data are systematically weighed and integrated. It resembles a “structured portfolio selection process.”3 The graduate school Administrator might use a model that finds college GPA highly correlated to graduate school success and admit only those who made a certain grade. A Money Manager might rely on a stock selection technique that employs long-term, empirical tests to prove the strategies’ efficacy over the span of 50 or more years. In almost every instance, we naturally prefer qualitative, intuitive methods. In almost every instance, we’re wrong.

Human Judgment is Limited.

David Faust writes in his revolutionary book The Limits of Scientific Reasoning that “Human judgement is far more limited than we think. We have a surprisingly restricted capacity to manage or interpret complex information.” Studying a wide range of professionals, from medical doctors making diagnoses to experts making predictions of job success in academic or military training, Faust found that human judges were consistently outperformed by simple actuarial models. Like traditional money managers, most professionals cannot beat the passive implementation of time-tested formulas.

Another researcher, Paul Meehl, offered the first comprehensive review of Statistical Prediction (similar to an empirical, systematic approach) and Clinical Prediction (similar to an intuitive, traditional heuristic approach). In his 1954 study, Clinical Versus Statistical Prediction: A Theoretical Analysis and Review of the Literature, he reviewed 20 studies that compared clinical and statistical predictions for these three things: academic success, response to electric shock therapy,4 and criminal recidivism. In almost every instance, Meehl found that simple actuarial models outperformed the human judges’ ability to predict accurately.5

Psychology researcher L. R. Goldberg went even further. He devised a simple model based on the results of the Minnesota Multiphasic Personality Inventory (MMPI)6 His MMPI model achieved an impressive 70% success rate. No human Expert could match his model’s result.7

Humans: What’s Our Problem?

The problem doesn’t seem to be lack of insight on the part of human judges. One study of pathologists predicting survival time following the initial diagnosis of Hodgkin’s disease, a form of cancer, found that the human judges were vastly outperformed by a simple actuarial formula. Oddly, the model used criteria identical to what the judges themselves said they used. The judges were largely unable to use their own ideas properly. The judges used perceptive, intelligent criteria but were unable to harness its predictive capability. The judges themselves, not the value or their insights, were responsible for their own dismal predictive performance.

Why Do Models Beat Humans?

In a famous cartoon, Walt Kelly’s character Pogo proclaims, “We’ve met the enemy, and he is us.” This illustrates our dilemma. Models beat the human forecasters because they reliably and consistently apply the same criteria time after time. In almost every instance, it is the total reliability of application of the model that accounts for its superior performance. Models are always consistent — they never vary. They are never moody, never fight with their spouse, are never hung over from a night on the town, and never get bored. They don’t favor vivid, interesting stories over reams of statistical data. They never take anything personally. They don’t have egos. They’re not out to prove anything. If they were people, they’d be the death of any party!

People, on the other hand, are far more interesting. It’s natural to react emotionally or personalize the problem rather than dispassionately review broad statistical occurrences – and so much more “fun”! It’s more natural for us to look at the albeit limited set of our personal experiences, then generalize from that small sample. Creating “a rule-of-thumb heuristic” is unnatural to us. We are a bundle of inconsistencies and, although that makes us interesting, it plays havoc with our ability to successfully invest our money.

In most instances, Money Managers — just like the college Administrators, Doctors, and Accountants mentioned earlier in this article — favor the Intuitive forecasting method. All of their analysis follows the same path:

  1. analyze the company,
  2. interview the management,
  3. talk to customers & competitors,
  4. et cetera.

Most Money Managers, if not all, believe they have the superior insights and intelligence to help them pick winning stocks. Yet 70% of them are routinely outperformed by the S&P 500. They are victims of their own overconfidence in their ability to outsmart and outguess everyone else on Wall Street. Even though virtually every study conducted over the past 60 years finds that simple, actuarially-based models — created with a large data sample — will outperform traditional active Managers, they refuse to admit this simple fact, clinging to the belief that, while that may be true for other investors, it’s not true for them.

Each of us, it seems, believe that we are above average. Sadly, this cannot be true statistically. Yet, when surveying people’s belief in their own ability8 — virtually everyone ranks their own ability in the top 10% to 20%! It’s tempting to dismiss this as a foible that wouldn’t trip up highly-trained professionals. But Professor Nick Bostrom (Director of Oxford University’s Future of Humanity Institute) points out that “Bias seems to be present even among highly-educated people. According to one survey, almost half of all Sociologists believed that they would become one of the Top 10 in their field, and 94% consider themselves to be better at their jobs than their average colleagues.”9

Nobel laureate Daniel Kahneman says, “The biases of judgment and decision-making have sometimes been called cognitive illusions. Like visual illusions, the mistakes of intuitive reasoning are not easily eliminated… merely learning about illusions does not eliminate them.”10 Kahneman goes on to say that, like the investors profiled above, most investors are dramatically overconfident and optimistic, prone to an illusion of control even where none exists. Kahneman also points out that the reason it is so difficult for investors to correct the false beliefs is because they also suffer from hindsight bias.11

If Kahneman’s insight seems hard to believe, go back in time to see how many market “experts” were calling for a NASDAQ crash in the early part of 2000. Then contrast that with the number of people who now say it was inevitable. Or, go to the library and browse business magazines from Summer 2007. Were any of those biz rags filled with dire warnings about the coming crash in Real Estate and Credit markets and the worst stock market downturn since The Great Depression?12 My guess is that, no matter how diligently you search, you won’t find any. Yet, after the fact, a plethora of books, articles, and documentaries cropped up to chronicle the crash, with many of the authors claiming it was inevitable. That’s textbook hindsight bias.

What’s more, even investors who were guided by a quantitative stock selection system can let their human inconsistencies hogtie them. In a 2004 WSJ story centering on the Value Line Investment Survey, one of the leading independent stock research services with a remarkable long-term record of identifying winners. According to WSJ,

“The company also runs a mutual fund, and in one of Wall Street’s odd paradoxes, it has performed terribly. Investors following the Value Line approach to buying and selling stocks would’ve racked up cumulative gains of nearly 76% over the five years ended in December, according to the investment research firm. That period includes the worst bear market in a generation13 By contrast, the mutual fund — one of the nation’s oldest, having started in 1950 — lost the cumulative 19% over the same period. The discrepancy has a lot to do with the fact that the Value Line fund, despite its name, does not rigorously follow the weekly investment advice printed by its parent Value Line publishing.”14

In other words, the managers of the fund ignore their own data, under the belief they alone can improve on the quantitative selection process! The article goes on to point out that another closed-end fund, the First Trust Value Line Fund, adheres closely to the Value Line survey advice, and has earned gains more in sync with the underlying research.

“Base Rates” are Boring.

Case Study15

The majority of Investors, as well as anyone else using traditional, intuitive forecasting methods, are overwhelmed by their own fallible human nature. They use information unreliably, at one time including a stock in a portfolio and another time excluding it, even though in each instance the information is the same. Our decision-making is systematically flawed because we prefer gut reactions and individual, colorful stories rather than boring ol’ base rates.

When used in the stock market, the rates tell you what to expect from certain class of stocks (e.g., all stocks with high dividend yields) and what that variable shows for how the category of stocks have performed over many decades of data. Our research has shown us that, since the original 1996 publication of WWOWS, the performance of the various factors we study has persisted. Remember: base rates tell you nothing about how “each individual member” may behave. Rather, they inform at the level of “all stocks with high dividend yields” (or whichever factor is being reviewed).

The best way to predict the future is to bet with the base rate that is derived from a large sample. Yet, numerous studies have found that people make full use of base rate information only when there is a lack of descriptive data. It’s difficult to blame people for always defaulting to predictions based upon their individual experience and intuition. Base rates are boring; experience is vivid and fun. The only way anyone will pay 100 times a company’s earnings for a stock if it’s got a tremendous story. Never mind that stocks with high P/E ratios beat the market less than 1% of the time over all rolling 10-year periods between 1964 and 2009 — the story is so compelling, you wouldn’t think twice about throwing the base rates out the window.

The Individual vs. The Group

Human nature makes it virtually impossible to forgo the specific information of an individual case in favor of the results of a great number of cases. We’re interested in this stock and this company, not with this class of stocks or with this class of companies. Large numbers mean nothing to us. As Stalin chillingly said, “One death is a tragedy; a million, a statistic.” When making an investment, we almost always do so on a stock-by-stock basis, rarely thinking about the overall strategy. If a story about one stock is compelling enough, we’re willing to ignore what the base rates tell us about the rest of the group.

Imagine if the life insurance industry made decisions on a case-by-case basis. An Agent visits you at your home, checks out your spouse and children, and finally makes a judgment based on his gut feeling. How many people who should get coverage would be denied, and how many millions of dollars in premiums would be lost? The reverse is also true. Someone who should be denied life insurance might be extended coverage because the Agent’s gut feeling was this individual is different, despite what actuarial tables say. The company would lose millions in additional payouts.16

The tables tell you what to expect the central tendencies of a large group will be. If you’re a 33-year-old, have no family history of heart disease or cancer, are a non-smoker and moderate drinker, have normal blood pressure and excellent blood work your chances of being extended life insurance at a low rate are excellent.17

The same thing happens when we think in terms of “individual stocks” instead of “stock selection strategies”. A case-by-case approach wreaks havoc with returns, because it virtually guarantees that many of our choices will at least partially include our emotions. This is a highly unreliable, unsystematic way to buy stocks, yet it’s the most natural and the most common.

In the years since the 1st Edition of What Works on Wall Street, I have given hundreds of presentations about our findings. I am always noting how the Investors in the room nod their heads when I tell them that low price-to-sales stocks do vastly better than stocks with high price-to-sales. They agree because (1) this is a simple fact and (2) it makes intuitive sense to them.18

But when I give those same Investors some actual names of stocks that meet these criteria, their demeanor visibly changes. Hands raise, with statements such as “What a dog!” or “I’m wary of that Industry” … After providing them with specific individual stocks for which they may have ingrained prejudices. Even when made aware of the bias, suppressing personal feelings is a very difficult task indeed.

Personal Experience Preferred

We also rely more on personal experience than impersonal base rates. An excellent example is the 1972 presidential campaign. The reporters on the campaign trail with George McGovern unanimously agreed that he could not lose by more than 10%, even though they knew he lagged the in the polls by 20%, and also that no major poll had been off by more than 3% in 24 years. These tough, intelligent people bet against the base rate because they become overwhelmed seemingly concrete evidence of their personal experience them. They saw huge crowds of supporters, felt their enthusiasm, and trusted their feelings. In much the same way, a market Analyst who has visited the company and knows the president will largely ignore the statistical information that tells him the company is a poor investment if the company’s executives do a good job in persuading him that while that might be true in general, it does not hold for their company because of any number of colorful stories they might tell him. In social science terms, he’s overweighting the vivid story and underweighting the pallid statistics.

Investors do this all the time. A story told to me by a colleague clearly illustrates how this can lead to disastrous results. At an investment conference in 2001, a Portfolio Manager (PM) who owned a large stake in Enron was asked repeatedly about what’s going on with the company. Enron shares had fallen from a $90-per-share August 2000 high down to the mid-$40s and Investors demanded to know their PM’s take on Enron’s future. The PM responded that he felt everything was fine at Enron: “Matter of fact, I recently attended a barbecue at the CFO’s home where many upper management Execs were present. Relax, all the Execs assured me that everything’s fine at Enron!” The PM added, “I was so relieved with their explanation that I’ve recently started piling on even more Enron shares!”

Then Enron filed for bankruptcy in late 2001 and its shares traded at $1. Clearly, that PM’s judgment was clouded by his reliance on stories and personal relationships, both of which had blinded him to the facts. His relationship with the Execs led him to turn a blind eye to what the market seemed to think: “Something rotten is at Enron’s core.”19

There are many similar examples to prove the point. According to Barton Biggs in his book Wealth, War and Wisdom, ample evidence shows that so-called “experts” making intuitive forecasts are right “less than half the time” and that “They were worse than dart-throwing monkeys in forecasting outcomes when multiple probabilities were involved.”20 Biggs is not alone. In Value Investing: Tools and Techniques for Intelligent Investment, James Montier writes,

“One of the recurring themes of my research is that we just can’t forecast. There isn’t a shred of evidence to suggest that we can. This, of course, doesn’t stop everyone from trying. Last year, Rui Antunes of our quant team looked at the short-term forecasting ability of Analysts. The results aren’t kind to my brethren. The average 24-month forecast error is around 94%, the average 12-month forecast error is around 45%.”

In Expert Political Judgment Philip Tetlock observes that “Human performance suffers because we are, deep down, deterministic thinkers with an aversion to probabilistic strategies that accept the inevitability of error.” In other words, even though the rational thing to do is bet with the base rate and accept that we will not always be right, humans are forever rejecting long-term evidence in the face of a short-term hunch, even though the probability of being correct plummets.

Returning to the stock market, many have hypothesized that Analysts get increasingly confident about their predictions after they’ve met the company’s Management and formed personal opinions about their talent, or lack thereof. They can often be seen clinging to these opinions even after factual events have proved them wrong.21 Indeed, Investors began falling for new catch phrases after the heart-pounding losses of 2008 and early 2009, when many Investors started buying into the concept that the March 2009 Bear Market low was the “new normal.” Proponents of this “new normal” believed that returns in the future were destined to be disappointing and that Investors should, once again, ignore history and change their behavior based upon short-term market conditions.22 Yet through it all, most Investors held on to their inherent biases towards overconfidence.

Simple vs. Complex

We also prefer the complex and artificial to the simple and unadorned. We are certain that investment success requires an incredibly complex ability to judge a host of variables correctly and then act upon that knowledge. An example:

Professor Alex Bavelas designed a fascinating experiment in which two subjects, Smith and Jones, face individual projection screens. They cannot see or communicate with each other. They’re told that the purpose of the experiment is to learn to recognize the difference between healthy and sick cells. They must learn to distinguish between the two using trial and error. In front of each are two buttons marked Healthy and Sick, along with two signal lights marked Right and Wrong. Every time the slide is projected, they guess if it’s healthy or sick by pressing the button marked as such. After guessing, their signal light will flash Right or Wrong, informing them if they have guessed correctly.

The catch? Only Smith gets truthful feedback. If he’s correct, his light flashes Right. If he’s wrong, it flashes Wrong. Because he’s getting a true signal, Smith soon starts getting around 80% correct, because it’s a matter of simple discrimination.

Jones’s situation is entirely different. He doesn’t get true feedback based on guesses. Rather, the feedback he gets is based on Smith’s guesses! It doesn’t matter if he’s right or wrong about a particular slide; he gets told he’s right if Smith guessed right, or wrong if Smith guessed wrong. Of course, Jones doesn’t know this. He’s been told that a true order exists that he can discover from the feedback. He entered searching for order when there is no way to find it.

The moderator then asks Smith and Jones to discuss the rules they use for judging healthy and sick cells. Smith, who got true feedback, offers rules are simple, concrete, and to the point. Jones on the other hand, uses rules that are, out of necessity, subtle, complex, and highly adorned. After all, he had to base his opinions on contradictory guesses and hunches.

The amazing thing is that Smith doesn’t think Jones’s explanations are absurd, crazy, or unnecessarily complicated. He’s impressed by the “brilliance” of Jones’s method and feels inferior and vulnerable because of the pedestrian simplicity of his own rules. The more complicated and ornate Jones’s explanations, the more likely they are to convince Smith.

Before the next test, with new slides, both men are asked to guess who will improve on their results from the first go-around. All Joneses, and most Smiths, say that Jones will improve. As it turns out, Jones shows no improvement at all. Smith’s results, on the other hand, worsen significantly from his first time around.
Why? Because he’s now making guesses based on some of the complicated rules he learned from Jones.

A Simple Solution

William of Ockham, a 14th century Franciscan monk from the village of Ockham, in Surrey, England, developed the principle of parsimony, now called Occam’s Razor. For centuries it has been a guiding principle of modern science. Its axioms23 boil down to this: Keep it simple, sweetheart. Occam’s Razor shows that, most often, the simplest theory is the best.

Simplicity is also the key to successful investing — however, it runs contrary to human nature. Our brains make the simple complex, we follow the crowd, allow our love of a story about some stock inflame our emotions and govern our decisions, we buy & sell based on tips & hunches, and we approach each investment decision on a case-by-case basis, with no underlying consistency or strategy. We are optimistically over-confident in our own abilities, prone to hindsight bias, and quite willing to ignore 80+ years of facts that show this to be so.

When making investment decisions, we do everything in the present tense. And, because we time-weight information, we give the most recent events the greatest import. Behavioral Economists call this recency bias.24

On the heels of the 2008–2009 market crash, Investors have learned a far different lesson. Because the decade from 2000 through 2009 was the worst for U.S. stock performance in 110 years, Investors have pulled trillions of dollars from stocks and moved their investment into Bonds.25

It’s extremely difficult NOT to make decisions this way. Think about the last time you really blew it. Time passes and you reflect, “What was I thinking! It’s so obvious that I was wrong, why didn’t I see it?” The mistake becomes obvious when you see the situation historically, drained of emotion and feeling. When the mistake was made, you confronted your emotion. And emotion often wins. As John Junor asserts: “An ounce of emotion is equal to a ton of facts.”

This isn’t a phenomenon reserved for the unsophisticated. Pension sponsors have access to the best research and talent that money can buy, yet are notorious for investing heavily in stocks just as the Bear Market begins and for firing managers at the absolute bottom of their cycle.26

The Path to Achieving Investment Success?

  • Study long-term results and find a strategy or group of strategies that make sense.
  • Remember to consider risk (aka, Standard Deviation of Return) and choose a level of risk that is acceptable.
  • Then stay on that path.
  • To succeed, let history guide you.27
  • Don’t second guess. Don’t change your mind. Don’t try to outsmart.28
  • Understand, see the long-term, and let it work.

If you abandon these guidelines, no amount of knowledge or data will bail you out and you might find yourself among the 80% of underperformers left wondering, “What went wrong?”

But if you can stick with these guidelines, your chance of succeeding is very high.


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  1. Currently in its 4th Edition. For some outtakes, and other supplemental material omitted from this edition, visit wwows.com
  2. Heuristics are the rules of thumb on which most intuitive forecasters rely when making any number of complex decisions or forecasts in their field.
  3. See Chapter 1, What Works on Wall Street (4th Edition)
  4. Things certainly were different in 1954.
  5. For example, a model using only high school GPA coupled with the level attained on an aptitude test (such as the SAT), outperformed the judgment of Admissions Officers at several colleges. Robyn Dawes, in his book House of Cards: Psychology and Psychotherapy Built on Myth, tells us more. He refers to Jack Sawyer, a researcher who published a review of 45 studies comparing the two forecasting techniques: none of the studies found the clinical, intuitive method — the one favored by most people — to be superior. What’s more, Sawyer included instances in which the human judges had more information than the model and were given a result of the quantitative models before being asked for prediction. The actuarial models still beat the human judges!
  6. The MMPI (Minnesota Multiphasic Personality Inventory) is a personality test commonly used to distinguish neurosis from psychosis to determine the patient’s categorization.
  7. The best judge achieved an overall success ratio of 67%. Reasoning that his human judges might do better with practice, he gave training packets consisting of 300 additional MMPI profiles to his judges, along with immediate feedback on how accurate they are. Even after the practice sessions, not even one of the human judges matched the model’s success ratio of 70%.
  8. Typically people are asked to rank themselves on their driving skill.
  9. See Nick Bostrom’s paper “Existential Risks: Analyzing Human Extinction and Related Hazards”.
  10. See Daniel Kahneman’s 1997 paper “The Psychology of the Nonprofessional Investor”.
  11. Hindsight bias is a condition that Kahneman described thus: “psychological evidence indicates people can rarely reconstruct, after the fact, what they thought about the probability of an event before it occurred. Most are honestly deceived when they exaggerate their earlier estimate of the probability that the event would occur… because of another hindsight bias, events that the best-informed experts did not anticipate often appear almost inevitable after they occur.”
  12. Or, on New Year’s Day 2008, would a panel of Wall Street’s top Analysts, economists, market forecasters, stock pickers, and money managers ever have predicted that in less than 2 years, Bear Stearns would be forced to sell itself to J.P. Morgan Chase for a fraction of book value because of a run on the bank? That Lehman Brothers, a firm with 156+ years of operating history, would collapse into bankruptcy? That Merrill Lynch — the “thundering herd” — would be forced to sell itself to the Bank of America to avoid its own collapse? That Goldman Sachs and J.P. Morgan, kings of the investment bankers, would be forced to declare themselves “ordinary” banks?
  13. Author’s note: they were referring to the downturn on 2000–2003, not what turned out to be the even worse downturn of 2008–2009.
  14. See the Sept. 16, 2004 The Wall Street Journal article “A Winning Stock Picker’s Losing Fund”.
  15. In a future osamresearch.com article we’ll expand further on “base rates”, which are among the most illuminating statistics that exist (they’re just like batting averages). For now, here is some non-investment-related color:
    Most statistical prediction techniques use base rates: (1) 75% of university students with GPAs above 3.50 go on to do well in graduate school, (2) smokers are twice as likely to get cancer, (3) the average 70-year-old in the U.S. can expect, based on actuarial tables, to live an extra 13½ years, (4) stocks with low P/E ratios outperform the market 99% of all rolling 10-year periods between 1964 and 2009.

    In one survey, people are told that out of a sample of 100 people, 70 are Lawyers and 30 are Engineers. Therefore, the base rate for Lawyers is 70%. When provided with no additional information and asked to guess the occupation of a randomly selected 10, people use the base rate information, saying that all 10 are lawyers, by doing so they ensure themselves of getting the most right.

    However, when worthless yet descriptive data are added, such as “Dick is a highly motivated 30-year-old married man who is well-liked by his colleagues,” people largely ignored the base rate information in favor of their “feel” for the person. They’re certain that their unique insights will help them make a better forecast, even when the additional information is meaningless. We prefer descriptive data to impersonal statistics because it better represents our individual experience. Then, when stereotypical information is added, such as “Dick is 30 years old, married, shows no interest in politics or social issues, and likes to spend free time on his many hobbies, which include carpentry and mathematical puzzles,” people totally ignore the base rate and conclude that Dick’s an Engineer, despite a 70% chance that he is a lawyer. This bias has been proven time and again with numerous tests over a range of subjects.

  16. The reason life insurance companies are so profitable, however, is because they base coverage and premiums solely on what the actuarial tables tell them. Actuarial tables are developed using massive databases of human mortality statistics based on underlying characteristics such as weight, family history of disease, blood work, blood pressure, smoking and drinking habits, and prior history.
  17. Why? Because the mortality tables say that it is highly unlikely for you to die anytime soon. Does that mean that the life insurance companies will make money on all 33-year-olds? No. There will be rare instances where a freak accident kills some of these healthy young people, but the vast majority of them will go on living and paying their premiums to the life insurance company.
  18. Paying less for every dollar of sales should lead to higher returns than by paying more for every dollar of sales.
  19. Of course, many of the PM’s Enron buddies later pleaded guilty to securities fraud and a variety of other fraudulent management practices.
  20. The dart-throwing monkeys study did NOT use a small sample. Rather, it covered 284 experts who made 82,361 forecasts over a period of years. The book concluded that most of these errors were made because Analysts made decisions using intuitive, emotional heuristics.
  21. Think of all of the investors who, at the end of the 1990s, based their investment decisions on only their most recent personal experience in the market. For this intuitive Investor, the only game in town was technology stocks and other large-cap growth fare. Every bit of their personally experience suggested that it was different this time, that a “new era “had dawned, and that only those who implicitly rejected history would do well going forward. And the majority of them held on to that belief through the crash of 2000–2003, so certain were they that a rebound was right around the corner. Only after 2 ½ years of “new personal experience” did these hapless Intuitive Investors learn that it wasn’t different this time
  22. Years from now, I believe the “new normal” catchphrase will be added in to the same dustbin of history where the “new era” currently languishes.
  23. E.g., “What can be done with fewer assumptions is done in vain with more“ and “Entities are not to be multiplied without necessity”…
  24. “Recency bias” is the tendency to remember more recent events or observations more clearly and to overweight recent information and underweight events from the more distant past. We then extrapolate anything that has been working well recently very far out in time, assuming it will always be so. How else could the majority of Investors have concentrated their portfolios in large-cap growth stocks and Technology shares right before the Technology Bubble burst in 2000 followed by the biggest NASDAQ Bear Market since the 1970s??
  25. Bonds were the asset class that had done best recently. Investors ignored the 110 years of market history showing that Bonds almost never outperform equities over long periods of time!
  26. Institutional Investors are fond of saying they make decisions objectively and unemotionally but they don’t. The authors of the book Fortune and Folly found that, although Institutional Investors’ desks are cluttered with in-depth, analytical reports, the majority of pension Execs select outside managers by answering to their gut feelings. They also retain managers with consistently poor performance simply because they have good personal relationships with them!
  27. Successful investors look at history. They understand and react to the present in terms of the past. Yesterday and tomorrow, as well as today, are their “now.” Something as simple as looking at strategies best and worst years is a good example. Knowing the potential parameters of a strategy gives investors a tremendous advantage over the uninformed. If the maximum expected losses from a strategy are 35% and the strategy is down 15%, instead of panicking, an informed investor can feel happy that things aren’t as bad as they could be. This knowledge tempers expectations and emotions, giving Investors an informed perspective that acts as an emotional pressure valve. Thinking historically, they let what they know transcend how they feel. This is the only way to perform well.
  28. Data helps you understand the hills and valleys are part of every investment scenario should be expected, not feared. It tells you what to expect from various classes of stocks. As long as it meets the criteria of your strategy, don’t reject an individual stock because you think that individual security will do poorly. Looking at decades of data, you see that many strategies have periods during which they didn’t do as well as the S&P 500, but also had many that did much better.