making socioﬁnancial deci-sions that affect other people.
In a modiﬁed version of Asch’s experiment described above, neurosci-entists found that individuals who go against peer pressure and give thecorrect answer have increased amygdala and caudate activation, whichreﬂects the tremendous emotional strain undergone by nonconformistsand the courage needed to disagree with obviously erroneous acceptedopinion.13
ANALYSTS’ ABUSE OF AUTHORITY
“When I was an analyst . . . I placed too much stockin the opinions of those who seemed to know morethan I did (my fault, not theirs). More unsettlingly,I saw others do the same with me.”
—Henry Blodgett, former Wall Street analyst14
Security analysts work in a high pressure environment, often jockey-ing to develop cozy relationships with corporate management. The perfor-mance of their stock recommendations is monitored closely by rating agen-cies. It’s no wonder that new analysts might feel pressured to “follow theleader” when a more successful analyst changes a stock recommendation.Finance professor Ivo Welch found that analysts are signiﬁcantly inﬂu-enced by each other’s opinions. A change in the buy or sell recommenda-tion of an analyst inﬂuences the recommendations of the next two analystswho issue opinions.15
During the late 1990s, many Wall Street Internet stock analysts werepaid high salaries and encouraged to tout stocks that they actually held indisdain. Analysts’ employers, the major investment banks such as MerrillLynch, Deutsche Bank, and Morgan Stanley, wanted the lucrative bankingbusiness of the companies whose shares their analysts were promoting.The banks demoted or ﬁred analysts who put “sell” recommendations onthe companies whose business they were wooing.
Morgan Stanley made millions in fees raising money for Priceline. Oneof Morgan’s Internet analysts, Mary Meeker, recommended buying Price-line’s stock at $134 a share. When it fell to $78, she repeated her buy
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recommendation, and she kept recommending Priceline as a buy as it fellto less than $3 per share.16
Merrill Lynch analyst Henry Blodgett (quoted above) covered the stockof Pets.com. Much of Pets.com’s ﬁnancing was raised by Merrill Lynch,who made millions. Blodgett placed a buy recommendation on Pets.com at$16 per share. When it fell to $7, Blodgett said “buy” again. Again a “buy” at$2, and again at $1.69. When it hit $1.43 a share, Blodgett downgraded thestock to “accumulate.” Pets.com was ultimately delisted from the stock ex-change. Investors may have lost a fortune, but in 2000 Blodgett and Meekerwere paid about $15 million each.17
The Internet stock frenzy was enhanced by television business chan-nels. CNBC, CNNfn, and other ﬁnancial television channels needed guestsfor their programming segments. Some analysts appeared on CNBC’sSquawk Box, hosted by Mark Haines. After a stock was recommended bya guest, Haines remembered, “I’d look down at the quote machine, and allof a sudden it had jumped 5 bucks or 10 bucks.” Thousands, new to invest-ing, were watching the show’s guests with no idea that a conﬂict of interestmight exist between the objectivity of their stock recommendations andtheir income. CNBC now requires its guests reveal conﬂicts of interest be-fore they appear.18
Such conﬂicts were well known on Wall Street, and it is unfortunatethat they were not somehow made clear to amateur Internet stock in-vestors. Indignation arose in the political world to satisfy constituents whohad lost millions of dollars following analysts’ recommendations from 2000through 2002. Are the losses the fault of the analysts who unethically (butnot illegally) touted stocks that they privately despised, or can we blamethe millions of investors who, driven by greed, piled into worthless stockson the advice of a presumed expert?
THE HERDING HABIT
“It is difﬁcult to get a man to understand somethingwhen his salary depends upon his not understand-ing it.”
—Upton Sinclair
Mutual funds have also been found to exhibit herding behavior, espe-cially growth and small-cap oriented funds. For funds, herding is not al-ways irrational; in fact, herding is proﬁtable for the vanguard of mutualfunds. The stocks the herd buys tend to outperform those they sell by4 percent over the subsequent six months.19
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Herding by mutual funds is related to word-of-mouth communicationbetween portfolio managers. In fact, portfolio managers in the same citydemonstrate greater herding. In a study of 1,635 mutual funds headquar-tered in 15 large U.S. cities, funds located in the same city traded in concertmore often and to a greater degree than funds located in different cities.20Researchers looked at how analysts treat information that they believeis “private” to them. Their sample was comprised of 1.3 million forecasts by5,306 analysts, relative to 3,195 ﬁrms, from 1985 to 2001. On average, ana-lysts tended to overweight information that they believed to be private. Yetanalysts tend to overweight their private information more when their fore-cast is more favorable than the consensus forecast but underweight theirprivate information when their forecast is less favorable than the consen-sus. That is, most analysts interpret their private information as reﬂectingfavorably on the companies they cover. The authors found that this mis-weighting is not due to an explicit cognitive bias, rather analysts misweightin order to draw more trading commissions to their ﬁrm.21
If you’re an individual investor, learning about how your unconsciousbiases sabotage you might make you feel a little jaded. How can you possi-bly succeed under such adverse psychological circumstances? Some indi-vidual investors adopt a contrarian perspective on the markets.
LIVING THE CONTRARIAN LIFESTYLE
“The most important quality for an investor is tem-perament, not intellect. . . . You need a temperamentthat neither derives great pleasure from being withthe crowd or against the crowd.”
—Warren Buffett
Investors who consider themselves contrarians are generally “counter-trend” specialists. There is no uniform personality style, though many con-trarians are suspicious of popular investing techniques and the “in-crowd.”I had a fascinating telephone conversation with one die-hard contrarian.
James had been a ﬂoor trader in London and emigrated to the UnitedStates to set up a hedge fund. He had done well for himself on the ﬂoor,and he had an excellent reputation as a trader. Unfortunately, he had littlerespect for his colleagues, explaining, “They just play ‘follow the leader.’It’s stupid, really, makes me sick.”
He was having trouble marketing his hedge fund because he despisedthe people he was marketing to, “They don’t know squat about the markets.
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They just have some fancy degree and they knew the right people at somepension fund.”
I mentioned that he seemed pretty hostile to his potential clientele.James responded, “Look, I don’t like morons!” He didn’t lack opinions.I asked, “How are you going to sell this fund?”“I need to ﬁnd people like me, who really understand what’s going on
in the markets.”
I asked why that was hard. He told me, “I make my money off the idiotswho follow the trends. I know when they are turning, and that’s my talent.I NEVER trade with a trend! But most people are part of the trend—that’swhat makes the trend in the ﬁrst place. None of the big investors or pensionfunds understand why I would go against what everyone else is doing. Theylook at me like I’m crazy.”
“If you make them money, I don’t see why they’d think you’re crazy.”“It’s my personality, okay? I won’t do anything that other people do. Ilive a contrarian life. If I go to see a movie at a theater and there’s a line forit, then I’ll go to a different movie. I won’t do anything that other people do.I married my wife because people didn’t think I would.”
He was contrarian to the core, and he had found a niche in the marketsthat ﬁt his personality. And he was probably right—few investors success-fully identify the top or bottom of a trend because they are caught up in thestory and unable to think clearly or critically.
Value and countertrend investing strategies are more characteristic ofcontrarians. Contrarians have the innate advantage of being able to searchfor opportunities away from the crowd. They often seek for solid invest-ments in overlooked sectors. Every sector rotates into and out of popular-ity, given time.
Contrarians are also inclined to short emerging trends, which can bevery painful. If you’re contrarian, be sure to identify clear signs of reversalor exhaustion before shorting a popular security or sector.
Contrarianism can be a destructive habit. Some investors who callthemselves contrarians see only danger, thus missing out on long bull mar-kets while fretting about an impending crash. There is a difference betweenexcessive anxiety and healthy critical thinking, so be sure to understandwhere you fall between those two. Anxious people won’t last long as con-trarians.
ADVICE FOR HERD ANIMALSAND TREND FOLLOWERS
In the marketplace, individual investors who herd are more likely to fol-low stock tips, the recommendations of “gurus,” and rumors, rather than
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making independently researched investment decisions. Most investors ﬁtinto this group.
If you like to be aware of crowds and trends, be sure to keep perspec-tive on the rationale behind the crowd’s behavior. If you are a trend fol-lower, consider adopting a strategy (such as momentum or growth) thataccounts for your social personality. Investing in the “hot” sector is okayas long as you are aware that trends change, and you have deﬁnite rules forwhen to close out your positions.
If you’re social, use it to your advantage. Learn about the new trendsand fads, and see if you can identify strategies to take advantage of these.Avoid listening to ﬁnancial media outlets such as CNBC and popular maga-zines. Due to the authority effect, virtually everyone is susceptible to jump-ing on the latest investment bandwagon publicized in the media, and it istypically a very bumpy ride.
ADVICE FOR INVESTMENT COMMITTEES
Investment committee decisions are often constrained by the members’ de-sire to conform. Standing out from the group, especially for new or youngemployees, can be quite embarrassing. Their fear of making a foolish com-ment often restricts their input.
Professor Hersh Shefrin described several techniques for debiasingimvestment committees in his 2006 book, Behavioral Corporate Finance:
1. Ask group members to refrain from stating personal preferences at the
outset of the discussion.
2. Explicitly cultivate debate, disagreement, and the sharing of informa-
tion.
3. Designate one member of the group to play devil’s advocate for each
major proposal.
4. Regularly invite outside experts to attend meetings, with the charge thatthey challenge the group not to behave like meek conformists . . . in thedrive for consensus. . . .22
This chapter has examined the psychological origins of herding. Most in-vestors inadvertently follow the advice of leaders, experts, and the welldressed. Herding is more likely when decisions are complex or the fu-ture appears especially uncertain. Contrarians actively use their outsiderperspective to their advantage. Investment committee members are con-strained by internal pressure to conform.
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C H A P T E R 18
Charting andData MiningReading Tea Leaves
“If enough independent phenomena are studiedand correlations sought, some will of course befound . . . It is only what statisticians call‘thefallacy of the enumeration of favorable circum-stances.’”
—Carl Sagan1
H uman ingenuity has an amazing capability for ﬁnding predictive
power in random stock data. Historically, forecasters looked forpredictive power in nature, such as from the movements of thestars, bird migrations, and tides. Even today numerous “astroﬁnance” websites offer newsletters prognosticating on the direction of the ﬁnancial mar-kets using astrology.
Throughout the twentieth century, a variety of stock market lead-ing indicators achieved notoriety. In the 1920s, the “hemline” indicatorwas popular. The market was said to rise as the length of women’sskirts retreated—this was thought due to liberal attitudes during economicbooms. The Super Bowl indicator, so called because the market was saidto rise in years that an NFL team won the Super Bowl, was 90 percent ac-curate in predicting the annual stock market direction from 1967 to 1997.However, both the Super Bowl and hemline indicators are random coinci-dences.
Quantitative tools for forecasting markets have an allure of certainty,which can be visualized graphically. The earliest technical tools for pre-dicting markets included candlestick charting, practiced as early as theseventeenth century by Japanese rice merchants. In the early twentieth
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century, practitioners such as Charles Dow used price charts to predictmarkets. These graphical tools were incorporated into computers as pro-cessing power increased dramatically in the late twentieth century.
In the early 1980s, computerized trading was successfully employed toﬁnd simple predictive mathematical price relationships. In the late 1980s,artiﬁcial intelligence was celebrated as the future of decision making onWall Street, but it didn’t live up to its promise. Then in the early 1990s,chaos and complexity theory appeared to be innovative techniques for mar-ket predictions, but they too did not provide consistently market-beatingmodels. In the mid-1990s, neural networks arose as popular statistical toolsfor ﬁnding predictive patterns in historical price data. All of the above sta-tistical tools have some value, but none were as paradigm shifting as theirtrue believers proclaimed.
As computing power increases, predictive correlations can be soughtin previously inaccessible places. Data-mining techniques allow re-searchers to examine millions of data points for market predictive poten-tial. Unfortunately, using computers for data analysis also creates thou-sands of new ways for self-deception.
ARTIFICIAL NEURAL NETWORKS
“In this business if you’re good, you’re right sixtimes out of ten. You’re never going to be right ninetimes out of ten.”
—Peter Lynch
Computers now serve as the platform for virtually every form of stockanalysis on Wall Street—quantitative analysis, technical analysis, and fun-damental analysis, among others. Some practitioners believe computersnow have the capacity to make better investment decisions than humans.That belief assumes human technology operators will not interject theirown failings into their systems.
I have had painful personal experience with the failings of quantitativeforecasting tools. In 1995 and 1996, I developed ﬁnancial software usingneural network and genetic learning algorithms to ﬁnd patterns in mar-ket price data. The forecasting software I designed predicted the marketwith signiﬁcant accuracy, which at ﬁrst felt like an enormous victory. Thesoftware had 59 percent directional accuracy during its ﬁrst six months ofreal-time trading use. Not bad.
But then the software’s predictions started to decay. By 1996, one yearafter implementation, the accuracy had dropped to 53 percent (essentially
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random). As a result, I developed new forecasting software. The new soft-ware worked well for a few months, but then its accuracy also dropped.By 1997, the advantage in developing new quantitative software was neg-ligible. Clearly, what had been a decent tool for pattern recognition in theearly and mid-1990s had expired.
When investors ﬁnd a mispricing, their buying and selling activity(arbitrage) pushes prices towards fair values, gradually reducing their ad-vantage. When enough money starts exploiting mispricings in the markets,the edge will disappear completely. That’s probably why my neural net-works stopped predicting the market—too many people using similar toolsidentiﬁed the same proﬁt opportunities. They exploited it until it vanished.I share my story to illustrate a point. When we ﬁnd patterns in data,chances are that someone else has stumbled upon exactly the same thing.And when enough money chases opportunities, they quickly disappear. I’vehad ﬁrsthand experience with such vanishing proﬁts. The markets are con-stantly evolving, and patterns that can be quantiﬁed will eventually be dis-covered. However, the price patterns whose origin lies in the human brainare likely to be the most enduring.
DATA MINING AND SELF-DECEPTION
“People in both ﬁelds [baseball and trading] operatewith beliefs and biases. To the extent you can elim-inate both and replace them with data, you gain aclear advantage.”
—John Henry, hedge fund manager and owner ofthe Boston Red Sox2
Searching for predictive patterns in ﬁnancial data is called datamining. Data mining often leads to the discovery of random coincidences.“Numerologists” and careless statisticians can be easily fooled. Massivecomputing power has increased the problem of statistical coincidence.Enormous databases of government economic statistics, past ﬁnancialprices, Internet musings, and corporate data can be scoured to ﬁndpredictive relationships with the stock market.
David Leinweber holds a doctorate in mathematics from Harvard, andhe has worked in trading technology for many large Wall Street ﬁrms, in-cluding First Quadrant and Codex. During the early 1990s, Leinweber no-ticed so many Wall Streeters using computers to ﬁnd spurious correlationsin data that he decided to make light of the many useless results comingforth. He wrote a paper entitled “Stupid Data-mining Tricks: Overﬁtting theS&P 500.”3
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Leinweber analyzed a database of the United Nation’s commodity pro-duction ﬁgures. He was curious if he could ﬁnd correlations among thesestatistics and the Standard and Poor’s (S&P) 500. He found that butter pro-duction in Bangladesh had a 75 percent predictive power over the S&P 500(in sample). When sheep production in New Zealand was factored into hismodel, accuracy jumped to over 90 percent. He did this analysis lightly—toillustrate how easy it is to ﬁnd highly predictive relationships in randomdata.
Unfortunately, the ﬁnancial media often promote spurious relation-ships in market data. On July 17, 2006, just after the 2006 Israeli invasion ofLebanon commenced, the Money and Investing section of the Wall StreetJournal, in the headline article entitled “Keeping Cool Amid Global Strife,”presented a table of the daily and one year U.S. stock market returns fol-lowing ﬁve Mid-East wars since 1967.4 Out of ﬁve previous wars, the one-week and one-year returns were positive for three. The Journal concludes,“Almost never has Middle East ﬁghting done long-term damage to the U.S.stock market.” While I support the Journal’s intent, the article fails to men-tion that a sample of ﬁve provides insufﬁcient statistical power to drawsuch a conclusion.
Whether we’re predicting the stock market with baseball scores,Bangladeshi butter production, neural networks, or even historical pricesand charts, we have to be very careful to avoid statistical overﬁtting, un-dersampling, and self-deception.
FINDING PATTERNS IN THE NOISE
“Like astrology, graphology seems to rest on the no-tion of ‘pareidolia’: the human infusion of patternsor meaning on random audio or visual events.”
—Steven Goldstein5
Pareidolia describes a psychological phenomenon involving a vagueand random stimulus being mistakenly perceived as recognizable.6 Com-mon examples are often religious in nature. In 1978, some 8,000 peoplemade pilgrimages to the home of a New Mexico woman who discovered apicture of Jesus in a burned tortilla. Similar misperceptions happen withcelebrities, such as Elvis “sightings.” In ﬁnance, pareidolia can creep intochart reading and technical analysis.
Academics who have studied chart patterns, speciﬁcally those basedon technical analysis, have found limited (but signiﬁcant) predictive value.For example, economists at the Federal Reserve created a mathematical
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algorithm for automatically detecting head-and-shoulders patterns in for-eign exchange data. They found that for two of the six currencies in theirsample, the yen and the Deutsche mark, trading strategies based on thehead-and-shoulders pattern could lead to statistically signiﬁcant proﬁts.7
Professor Andrew Lo at the Massachusetts Institute of Technology(MIT) quantiﬁed the principles underlying eight technical price chart pat-terns. In introducing his research, Lo acknowledged that “it is difﬁcultto dispute the potential value of price/volume charts when confrontedwith the visual evidence.” Lo noted that, “Technical analysis has survivedthrough the years, perhaps because its visual mode of analysis is more con-ducive to human cognition, and because pattern recognition is one of thefew repetitive activities for which computers do not have an absolute ad-vantage (yet).”8
Lo translated the visual cues that technical analysts seek to ﬁnd incharts into mathematical algorithms. Chart patterns such as the head-and-shoulders and double-bottom patterns were tested for their predictivepower. Stock prices from 1962 to 1996 were used in the analysis. Theresearchers found that some technical patterns appeared to provide“incremental information” during intraday price movement. While notafﬁrming that technical analysis can be reliably used for proﬁt, Lo andcolleagues concluded, “It does raise the possibility that technical analysiscan add value to the investment process.”9 Lo now is head of research athis own hedge fund (AlphaSimplex), which employs quantitative analytictechniques.
THE TREND AND MEAN-REVERSIONBIASES IN CHART READING
“The greater the uncertainty, the more people areinﬂuenced by the market trends; and the greater theinﬂuence of trend following speculation, the moreuncertain the situation becomes.”
—George Soros
Many economists in academia have argued against the merits ofcharting techniques such as technical analysis. According to Andrew Lo,“One of the main obstacles is the highly subjective nature of technicalanalysis—the presence of geometric shapes in historical price charts is of-ten in the eyes of the beholder.”10 In his book, A Random Walk down WallStreet, Burton Malkiel (1996) concludes that: “Under scientiﬁc scrutiny,chart-reading must share a pedestal with alchemy.” See Figure 18.1 for an
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FIGURE 18.1 A chart of monthly gold prices in the late 1990s and early 2000s.Notice the support, resistance, and other metrics used by technical analysts’ to gaugeprice trend.Source: Securitytrader.com.
example of a chart that has been marked with the visual cues of the tech-nical analyst.
Regardless of the academic skepticism, many individual investors usecharts to aid their decision making. However, an excessive reliance oncharts, in lieu of other information, can be detrimental to performance.
Psychologists have found that investors often preferentially buy stocksdepending on how price information is displayed in a chart. Informationsuch as the salient high and low, the most recent price trend, and thelong-term average of the stock price inﬂuence investor judgments. Wheninvestors see a chart depicting a long-term upward price trend, they aremore likely to want to buy stock, because they extrapolate that trend intothe future. When they see a chart displaying a long-term downward trend,they prefer to sell.11
Stocks that are trading above or below their long-term average trendare thought likely to “mean revert.” Mean reversion refers to the commonbelief that a stock price that has deviated away from its average price will
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eventually reverse course and return back to its average level. Mean rever-sion is a complicated way of saying, “What goes up must come down.” InFigure 18.1, the “support” and “resistance” lines represent points at whichthe price is anticipated to mean revert, and if it does not, the price move-ment is called a “breakout.”
Additionally, recent price trends, relative to the long term average,inﬂuence investor buy and sell preferences. In general, investors aremore likely to forecast future positive returns if the recent price trendis up and to forecast future negative returns if the recent price trend isdown.12
For example, in a study I perform during behavioral ﬁnance work-shops, I ask participants which of the following hypothetical stocks theywould rather buy and which they would rather sell. See Figures 18.2 and18.3 for the charts I have used.
Each stock depicts a conﬂict between mean-reversion and the recentprice trend. The majority of investors (60 percent on average) select to buythe stock depicted in Figure 18.2, while 40 percent select the stock depictedin Figure 18.3. Essentially, they are voting more strongly in favor of recentprice trend as an inﬂuence on their decision making.
If the recent price up trend in Figure 18.2 was shorter and ended belowthe average trend, then it is likely that fewer investors would choose to buythe stock in Figure 18.2 due to both mean reversion and recent price-trendeffects. In general, investors using charts weigh both the recent price trendand mean reversion in their assessments of future stock direction.
FIGURE 18.2 The recent up-trend.
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FIGURE 18.3 The recent down-trend.
Whether investors believe in the continuation of a trend or mean re-version depends on the length of the price trend depicted. When a trend isshorter, people tend to follow the “hot hand” bias, believing that it will con-tinue. When the trend is longer, investors are more likely to expect meanreversion.13
Professor Paul Slovic has remarked that in long term thinking and fore-casting, people generalize from available information. In short-term predic-tions they base their forecasts on details.14 There is a systematic bias ininformation processing depending on the type of forecast (short or long-term) that one is making.
OVERRELIANCE ON CHARTS
When stock price charts depict a salient high or low, then the mean-reversion bias kicks in. German researchers Thomas Mussweiler and KarlSchneller set out to test the hypothesis that investors “anchor” their expec-tations to recent highs and lows in stock charts.15 The researchers created12-month stock price charts that either: (1) began in the midline, then roseto a peak as time passed, and then fell back to just above the midline (seeFigure 18.4); or (2) began in the midline, dropped to a bottom as time pro-gressed, and then rose to just above the midline by the end of the period(see Figure 18.5). Overall, both charts showed a 20 percent return from thebeginning to the end of the 12-month period depicted.
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FIGURE 18.4 The high-point salience chart used by Mussweiler and Schneller.16Source: Lawrence Erlbaum Associates.
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FIGURE 18.5 The low-point salience chart used by Mussweiler and Schneller.17Source: Lawrence