Erlbaum Associates.
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The researchers then assembled groups of private and professional in-vestors for their study. Participants were given a half page of text describ-ing a hypothetical company, its fundamental economic statistics, and itsprice chart for the past year. The experimental subjects were asked to es-timate the 12-month price target for the stock and then decide whetherthey would like to buy or sell the stock. Presentation of the charts wasrandomized to the textual information, because the researchers were in-terested to assess the effect of the charts alone. The textual informa-tion was the same for all the participants in the study—only the chartsdiffered.
The authors explain their experimental design by referring to the “com-parison effect.” An investor who is confronted by a low on a stock chartwill selectively look for negative information to explain that low, while aninvestor confronted with a high price peak will preferentially search forpositive information to justify the higher price.
As the investigators expected, when subjects were asked to forecastthe 12-month stock return, they predicted a higher price in one year if thechart showed a salient high versus a prominent low during the 12-monthperiod. When a different group of investors was presented with the sameexperiment, and they were asked how much stock they would buy, theyagain chose to buy more (three times as much) of the stock with the pricepeak. When the experiment was reframed, and investors were asked howmuch stock they would sell, they chose to sell more (twice as much) ofthe stock with a salient low in its chart. Investors’ expectations of futurereturns, buying choices, and selling plans were contingent on the chartpatterns.
Importantly, when the researchers increased the amount of corporateinformation available to the subjects, the results became even more chartdependent. The investigators used the full textual information from a dis-count brokerage web site on a real Internet company—three and a halfpages of statistical and company information. Subjects (including profes-sional investors) bought almost twice as much of the stock with a salienthigh in its chart versus the salient low. Information overload drove in-vestors to rely more heavily on the charts.
In a postexperiment survey, subjects mentioned more positive aspectsof the stocks with salient high charts than those with the low price point.They justiﬁed their buy decision by citing more positive aspects of thetextual information, even though they were clearly choosing based on thechart, not a consideration of the data, since all the textual information wasrandomized across subjects. The subjects’ thinking changed depending onthe appearance of the past price chart, but they were unaware of this pro-found effect.
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THE GAMBLER’S FALLACY
The gambler’s fallacy is a logical mistake in which independent past eventsare believed to inﬂuence future events. After a series of gambles with thesame outcome, players often incorrectly believe that either (1) the pastseries will continue (the “hot-hand fallacy”), or (2) the past sequence willrevert toward the mean. Regarding the ﬁrst example, a continuation of atrend, a basketball player who has sunk several shots in a row is said tohave a “hot hand.” This player is incorrectly thought by fans to be morelikely to make his next shot. In the second example, “mean reversion”thinking is the tendency of market strategists to forecast that the mar-ket will have opposite performance the following year as compared tothe prior year. In fact, returns do not either trend or reverse from yearto year, but a belief in these tendencies is common. Academics and pro-fessionals are more susceptible to mean reversion errors, while novicesare more likely to project current trends into the future—”hot-hand”errors.
In both cases, investors are assuming that the markets have “memory”and are thus inﬂuenced by their past behavior. While that memory is in-ﬂuential to a small extent in the markets (as we’ll see in the momentumand long-term reversals examples in Chapter 23), it does not explain futureprices as much as investors think it does.
IRRATIONAL EXUBERANCE . . . CALLEDTOO EARLY
On December 3, 1996, Yale Professors Robert Shiller and John Campbellspoke to the Federal Reserve Board and Chairman Alan Greenspan. Theyexplained that historically, when the stock market price-to-earnings (P/E)ratio has been high, the return for holding stocks over the subsequent10 years has tended to be low. Shiller asserted that markets were behav-ing irrationally at that moment in time.18
Shiller outlined his argument in detail in his synonymous book Ir-rational Exuberance published in early 2000.19 Shiller noted, “The highrecent valuations in the U.S. stock market come about for no good rea-sons. . . . The market is high because of the combined effect of indifferentthinking by millions of people, very few of whom feel the need to performcareful research on the long-term investment value of the aggregate stockmarket, and who are motivated substantially by their own emotions,random attentions, and perceptions of conventional wisdom.”20
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Alan Greenspan, chairman of the Fed, used the phrase “irrational exu-berance” in a black-tie dinner speech on December 5, 1996, two days afterhis meeting with Shiller. Fourteen pages into his dinner speech, Greenspanasked a rhetorical question: “But how do we know when irrational exu-berance has unduly escalated asset values, which then become subject tounexpected and prolonged contractions as they have in Japan over the pastdecade?” He then qualiﬁed his question, “We as central bankers need not beconcerned if a collapsing ﬁnancial asset bubble does not threaten to impairthe real economy, its production, jobs and price stability.”21
Greenspan’s speech was televised live on C-SPAN. Shortly after heposed his question, the stock market in Tokyo declined sharply, closingdown 3 percent. Hong Kong’s Hang Seng index shed 3 percent. Thenmarkets in Frankfurt and London fell 4 percent. The stock market in theUnited States fell 2 percent at the open of trading the following morning.22But after the sell-off, the markets quickly rebounded, and over the nextthree years world stock markets experienced the biggest ﬁnancial bubblein history.
Shiller was correct to assert that P/E ratios predict 10-year returnsbased on historical data. However, the stock market’s memory is not sosimple. The historical mean for P/E is 14.2. For most of the time since 1872,average P/E ratios have moved in the range 8 to 20. In December 1996, theaverage P/E ratio stood at 28. As a result, Shiller and Campbell predictedthat between 1997 and 2006, the stock market would lose about 40 percentof its real value, to an S&P 500 value of 450. In fact, by July 26, 2006, theS&P 500 was at 1273 with a P/E ratio of 17.5. What these professors did notforesee was the tremendous growth in corporate productivity and earningsfacilitated by the adoption of information technology.
In 1996, Barron’s asked strategists at major Wall Street investmentbanks their stock market outlooks for 1997. The Dow Jones Industrial Av-erage (DJIA) had risen 33.5 percent in 1995 and 26 percent in 1996. Con-sistent with mean reversion, the seven strategists predicted, on average, aDJIA performance of –0.2 percent for 1997. The actual return of the DJIAin 1997 was 22.6 percent. When strategists were polled by Barron’s af-ter two down years in the markets, 2000 and 2001, they predicted largerises in the market over the subsequent year. For 2001 the strategists ex-pected, on average, a return of 18.7 percent for the S&P 500. For 2002 thestrategists predicted a 21 percent rise in the S&P 500. The S&P 500 ac-tually declined 13 percent in 2001 and then again declined 23.4 percentin 2002.23
Mean-reverting predictions affect professionals and respected aca-demics alike. But why not amateur investors? Why are they susceptible tothe “hot-hand” bias, not mean reversion? In the case of amateur investors,their memory recall and experience is relatively short. These investors’
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attention is attracted to recent returns, which they then extrapolate intothe future.
When one has a lot of information about past returns, as academicsand professionals do, their attention is attracted to the remarkable highs(such as historically high P/E ratios) and the salient lows. They then begintheir estimates of the future by anchoring their predictions from the salienthigh or low points, relative to the overall trend. If the market is currentlyat a relative high or low, then as can be seen in a review of history, it willprobably mean revert. The problem with such thinking is that the futureisn’t always like the past, and salient highs and lows are only recognized assuch in hindsight (they can extend much further before reverting).
Many professionals utilize strategies based on mean reversion. In or-der to cushion themselves from the effects of mistiming tops and bottoms,they trade small, they diversify, and they plan to hold for the long term. JimLeitner says, “It’s just a truism that, over time, mean reversion works. Totake advantage of it, a one-year-plus time horizon is required at minimum,but a ﬁve-year-plus time horizon is ideal.” Long-term time horizons workbecause many portfolio managers are under short-term performance pres-sures. “The large inefﬁciencies do not get arbitraged out because there’svery little capital that actually gets allocated toward extracting value overmultiple years.” Opportunities for mean-reversion investing exist, but onemust be very careful to properly manage risk when exploiting them.
THE SOOCHOW GAMBLING TASK
The gambler’s fallacy (hot-hand effect) may be playing a role in anunusual pattern of money-losing behavior in gambling college students.Researchers at the Soochow and National Yang-Ming Universities ofTaiwan found that gamblers choose a consistently losing strategy if itcontains a series of several small wins and infrequent large losses. Theresearchers discovered this odd behavior using an experimental taskcalled the Soochow Gambling Task (SGT),24,25 which is a variation of theIowa Gambling Task used by Damasio (described in Chapter 2).
In the SGT subjects are told to make as much money as possible by se-lecting cards from one of four decks. The decks are labeled A, B, C, and D.Each card displays a monetary gain or loss. Decks A and B have expectedvalues, for each ﬁve card ﬂips, of −$250. Decks C and D have expectedvalues of +$250 for each ﬁve cards. For each deck, the cards have verydifferent payoffs. Deck A has four $200 wins for each $1,250 loss. DeckB has four $100 wins for each $650 loss. Deck C has four $200 losses foreach $1,050 gain, and deck D has four $100 losses for each $650 gain.26
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TABLE 18.1
The Outcome Odds and Frequencies for Each Card Deck in theSoochow Gambling Task. Notice the Expected Values at the Far RightColumn
SoochowGamblingTask
Deck ADeck BDeck CDeck D
AverageNumber ofGains perFive Cards
4411
Size ofGains
$200$100$1,250$650
AverageNumber ofLosses perFive Cards
1144
Size ofLosses
−$1,250−$650−$200−$100
ExpectedValue perFive Cards
−$250−$250+$250+$250
Approximately 600 people have participated in the SGT studies so far. SeeTable 18.1 for details.
Unusually, most experimental participants prefer to select cards fromthe money-losing decks. Over 200 trials, more than 60 percent of the cardsﬂipped were from decks A and B, losing money for most participants. Sub-jects had a ﬁnancial incentive to make money in this game, but they con-sistently acted against their best interest, and they didn’t learn to do bettereven over 200 trials (see Figure 18.6).
As a result of their consistently poor performance, researchers Ching-Hung Lin and Yao-Chu Chiu decided to alert subjects, after 100 card ﬂips, to
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FIGURE 18.6 This is a plot of card deck choice frequency when individuals arenot informed of the probabilities and payoﬀ of each deck. Note that subjects do notlearn to select the positive expected value decks.Source: Courtesy of Ching-Hung Lin.
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FIGURE 18.7 This plot of card deck choice frequency demonstrates how indi-viduals continue to make negative expected value selections even after they are in-formed of the decks’ expected values at ﬂip 100.Source: Courtesy of Ching-Hung Lin.
the actual payoffs and odds of each deck.27 Yet even with this knowledge,the subjects continued to select almost half their cards from decks A andB. While participants stopped losing money once they knew the odds, theywere still only breaking even.
Note in Figure 18.7 how the selection rates for decks A and B beginto converge with those of decks C and D after subjects are informed of thepayoff probabilities on Trial 101 (See “101 120” on x-axis in Figure 18.7). ByTrial 200, the subjects’ choices were roughly equally distributed betweendecks A and B and decks C and D. According to researcher Ching-HungLin, “Almost everybody lost money in our experiment. We couldn’t ﬁgureout how to help them under this uncertain situation.”28
Why did the experimental subjects select from the lower paying decks?The series of wins was seductive. In decks A and B, the satisfaction ofwinning occurred four times, while the pain of the loss only happened onceper ﬁve cards. A series of wins is, in a way, addictive. Gains feel so goodthat investors will risk large periodic losses as an accepted consequence ofgetting the good feelings from a series of small gains.
In a way, these results are similar to predictions of loss aversion. In-vestors would rather cut their winners short, ensuring a small gain, andhold their losers too long, experiencing occasional very large losses. If sin-gle losses are twice as painful as single gains are pleasurable, then fourgains are twice as pleasurable as one loss is painful (size notwithstanding).
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Professional investors do not experience the aversion to small lossesthat the SGT participants did. Many professional traders have poor win-lossratios, numerically speaking. They may lose on 80 percent of their invest-ments, but their 20 percent of winning positions are so lucrative that theymore than make up for their small losses. Essentially, great investors arewilling to draw from decks C and D consistently, accepting a string of smalllosses as the price of doing business while holding out for the big winners.Excellent investors don’t change their philosophy in response to a fewlosing investments. They know their edge in advance, and they understandthat down periods are inevitable. Their high level of conﬁdence in theirjudgment and methodology allows them to brush off small losses.
For many small investors who seek small gains, dividend paymentsprovide a sense of security. Yet stocks paying high dividends may be a typeof psychological trap for risk-averse investors. The quarterly dividendsfeel good even while the stock price stagnates. Studies have shown thathigh-dividend-paying stocks appreciate signiﬁcantly less than lower-yieldstocks.
THE LEARNED CAUDATE
The series of gains in the SGT trigger subjects’ neural reward learning. Partof the brain, called the caudate nucleus (which arcs over the top of thenucleus accumbens), helps people learn to recognize the cues and patternsthat predict ﬁnancial rewards.
Researchers found that the caudate nucleus is more active as subjectsbegin playing a card game in which some cards (with a certain symbol) arepredictive of potential losses and others of gains. As subjects learn whichcards are predictive of gains and which of losses, the activity of the caudatedecreases every time those cards appear, as if individuals are learning theﬁnancial outcomes represented by each card.29
In investors, caudate deactivation may occur as habits are formed. In-vestors learn which cues to pay attention to—such as certain aspects ofearnings announcements or chart price patterns—in order to make bet-ter decisions. As they learn the connections, they become less vigilant andmore vulnerable to surprise.
When investors’ learned assumptions are unexpectedly violated, thenfollowing the initial surprise, they must reassess their environment. Sucha scenario of learning, surprise, and relearning happens when investorsbecome complacent about a company’s consistently positive or negativeearnings performance.
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PATTERNS IN EARNINGS REPORTS
When companies report good (or bad) earnings for a prolonged period,investors begin to expect that the pattern will continue. When a companyviolates the “pattern” by either missing (or beating) expectations, there isa delayed reaction from the investing public. That is, the price reactionoccurs over a longer time period than would be expected if all the good orbad news had been instantly incorporated into prices.
Researchers studied the reaction of stock prices to earnings surprisesfor listed U.S. companies over the period 1983–1999. The results suggestedthat investors show short-term underreaction to earnings announcements,incorporating the earnings news into the stock price with a delay. How-ever, there is an overreaction during earnings surprises. That is, stockprices initially move too little in the direction of a positive or negative earn-ings announcement unless it is a highly unexpected earnings surprise.30It is possible that investors learn to associate earnings trends with a com-pany and do not react adequately if they are in line with their forecasts,but surprises shock the caudate’s habit-driven decision making ofﬂine andrequire a readjustment and relearning of reward cues. In the meantime, asinvestors readjust their expectations, they protectively overreact.
Some mutual funds take advantage of stock overreaction and under-reaction around earnings announcements. Russ Fuller, president of Fullerand Thaler Asset Management in San Mateo, California, has commentedthat this is one of the strategies his ﬁrm exploited to achieve above-marketreturns. Several other “behavioral” funds have sprung up to capitalize oninvestors’ apathy or collective surprise around earnings announcements.
FOOLED BY RANDOMNESS
In the stock market, there are more days of large negative price movementsthan would be expected in a “normal distribution” of returns. This is calledthe fat tail or leftward skew of return variability. Put simply, there aremore big daily price drops in the stock market than would be expected bychance. Yet most investors tend to overlook the likelihood of a large dailyprice decline, especially if it has been a while since the last one.
As mentioned previously, put options are a type of insurance contractfor investments. Put values increase when the market drops. But if the mar-ket does not drop, over time, puts lose their premium value. When puts areout-of-the-money, they will expire worthless if the market does not dropsigniﬁcantly before their expiration date.
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When a large negative event has not happened recently in the mar-kets, put premiums are relatively low. During and immediately after priceshocks, put premiums are quite high. Low put premiums often reﬂect in-vestors’ poor long-term memories and their discounting of future risk.
In the SGT experiment described above, subjects were not adequatelyafraid of infrequent large losses, and they were enamored with small short-term gains. They essentially paid the experimenters so they would not haveto experience frequent small losses, even with the possibility of occasionallarge gains.
To take advantage of investors’ preference for small gains irregardlessof occassional large disasters, Nassim Taleb argues for a style of investingcalled catastrophe investing. Taleb may invest in markets where returnshave been good for some time. In such markets, investors are accustomedto small feel-good gains year after year, and the implied volatility of optionsin these markets has dropped (often to excessively low levels). Taleb maybuy put options far out-of-the-money. Taleb’s puts make money only if themarket drops sharply within a given time period. Usually, he loses moneyon these puts.
Taleb describes his strategy as one relying on “black swans.” Just be-cause one has never seen a black swan does not prove that they do notexist. Taleb’s strategies are constructed entirely on the premise that ran-dom and unexpected events do sweep the markets. “Taleb buys optionsbecause he is certain that, at root, he knows nothing, or, more precisely,that other people believe they know more than they do.”31
Taleb has designed a strategy that can lose gradually for years. Butover time, a large market event will earn him outsized long-run returns. InMalcolm Gladwell’s New Yorker magazine article “Blowing Up,” Taleb isquoted: “‘We cannot blow up, we can only bleed to death,’ Taleb says, andbleeding to death, absorbing the pain of steady losses, is precisely what hu-man beings are hardwired to avoid.” Taleb accepts fund investors who canthink for the long term, potentially suffering year after year of small losseswith the knowledge that one day, possibly several years hence, their pa-tience will pay off handsomely. These investors are rare; their bias againstlosses is innate, which is why his strategies are likely to be lucrative fordecades to come.
Taleb’s strategies are not sexy. Perhaps they are cowardly. How couldsomeone place himself in such a pessimistic position, gradually losingmoney while consistently betting on a large unexpected negative eventthat others were unprepared for? It goes against the risk-taking and hero-ism and optimism of the American business ethos. According to Gladwell,“That is the lesson of Taleb. . . . There is more courage and heroism in de-fying the human impulse, in taking the purposeful and painful steps to pre-pare for the unimaginable.”32
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C H A P T E R 19
Attentionand MemoryWhat’s in a Name?
I n 2003 I met a trader named Collin who was working at a proprietary
trading ﬁrm. His specialty was scalping small dislocations in price, be-tween markets, using high leverage. For example, he arbitraged smalldifferentials in the bid and ask prices of American depositary receipts(ADRs) between London and New York or New York and Tokyo.
Collin’s scalping opportunities often only lasted for seconds. Heneeded to enter nearly simultaneous buy and sell orders in order to cap-ture a proﬁt. If he was too slow, he lost transaction costs (and sometimesmore). He once told me that he could spot an opportunity, enter orders,and complete the entire transaction within two seconds. In order to ply histrade, Collin had to be constantly vigilant to price data. If he looked awayduring the trading day, he could miss an opportunity.
The devotion of his attention to the markets was exhausting, but overtime he adapted to the conditions and could watch prices for long periods.As Collin learned to focus his attention at work, he noticed deteriorationin other areas. He had trouble listening to his girlfriend, he couldn’t sit stillto read, he was easily irritated, and he didn’t feel comfortable when notengaged in high-intensity activities.
He enjoyed online poker when not trading, and he purchased threedesktop computers for playing simultaneous late-night poker games. Hejustiﬁed this activity by citing the extra proﬁts he earned (which were ac-tually far below his trading income).
Toward the end of 2003, Collin’s work was becoming more com-petitive due to computerized “robot traders.” He needed better atten-tion and faster reactions to exploit good opportunities. He tried chemical
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supplements to improve his attention and work performance. A low doseof the selective serotonin reuptake inhibitor (SSRI) ﬂuoxetine (Prozac) im-proved his irritability, but not his distractibility.
He tried modaﬁnil (Provigil) and noticed a striking improvement in hisability to focus. But then he started to abuse modaﬁnil. He stayed up lateplaying online poker and then awakened every trading day at 5 A.M. (Pa-ciﬁc Time) to prepare for the New York market open. He took a modaﬁniltablet each weekday morning to maintain his alertness during the workday.Neurally speaking, the dopamine receptors in Collin’s reward system werebecoming desensitized, leading to a chronic experience of decreased plea-sure, distractibility, and a drive to gamble and take risks (online poker)to stimulate adequate dopamine release. He began to need risk-induceddopamine release just to feel normal.
Collin’s sleep deﬁcit had its own dangers. In general, sleeping four toﬁve hours per night for a week leads to cognitive impairment equivalent tothat of a blood alcohol level of 0.1 percent (legally intoxicated). The sameresult occurs after not sleeping at all for one night. Delayed reaction time,poor problem solving, and impaired judgment are common the followingday.
Collin’s use of modaﬁnil decreased his desire to sleep (which is thepurported effect of the drug), but not his need to sleep. As a result, he ac-cumulated a massive sleep deﬁcit over months of four-hour-per-night sleepand daily modaﬁnil use, which resulted in poor judgment in complex tasks(such as human relationships and planning for the future).
Eventually, his girlfriend left him, breaking their engagement and can-celing her lease on the apartment. Collin kept up his unhealthy nocturnalpoker games. Due to the sleep debt and relationship problems, his trad-ing performance started to suffer. He forgot that his apartment’s lease hadbeen canceled, and he didn’t prepare to move out on time.
He fell asleep one weekend, and he was unrousable when his landlordarrived to check on the apartment. He had accumulated a large sleep debtduring the prior months, and now he was paying it back. Collin was passedout on the ﬂoor of his apartment in front of the television, and he didn’trespond to his landlord’s efforts to wake him. An ambulance was called,and Collin remained largely incoherent when the paramedics arrived. Hespent the night in a hospital emergency room, snoozing on a gurney in thehall.
After this experience Collin realized his life had to drastically change.He quit trading, moved into a hotel, and planned a long vacation. He bookedtickets for a year-long around-the-world trip.
When he returned to San Francisco a year and a half later, we wentto dinner. He was a very different (and very happy) person. He told me hehad attended a one-month silent mindfulness meditation retreat in India
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(Vipassana), and he credited this experience with the transformation inhis personality. He had also reconnected with family in Ireland, includingcousins he had never met.
During the year