Chapter 2) don’t work that way fortrained scientists any more than they do for common folk. Cynical ob-servers of science say adherents to a theory that has been falsified neverreally change their minds. They have far too much invested in its survivalto do that. With time, they just fade away.
With EMH having served, and served well, for 40+ years, as the foun-dation of finance, its backers were just not about to go away with headshanging low. Two of its standard bearers (read pall bearers), Eugene Famaand Kenneth French, said gains earned by stale-information strategiessuch as price-to-book value and market capitalization were nothing morethan fair compensation for risk. Recall that EMH does not deny the possi-bility that stale public information can earn profits. It merely says thatwhen those gains are adjusted for risk, they will not be better than invest-ing in an index fund.
So long as the term risk is left undefined, EMH defenders are free toconjure up new forms of risk after the fact. As Elliott wavers haveproven, after-the-fact fiddling allows any prior observations to be ex-plained or explained away. And that, it seems to me, is what Fama andFrench did.*” They invented a new, ad hoc risk model using three riskfactors to replace the old standby, the capital asset pricing model,™*which uses only one risk factor, a stock’s volatility relative to the marketindex. Quite conveniently, the two new risk factors that Fama andFrench decided to add were the price-to-book ratio and market capital-ization. By citing these as proxies for risk, Fama and French neatly ex-plained away their predictive power. “According to the new risk model,stocks of smaller firms (low-market cap) or firms with low market-to-book ratios are fundamentally riskier companies and thus must offerhigher average returns to compensate the investors willing to own them.Conversely, large capitalization stocks because they are safer, and highprice-to-book ratio stocks, which are in effect growth stocks with morecertain future prospects, earn lower average returns because they ex-pose their owners to lower risk.”**
This was nothing more than an ad hoc after-the-fact explanation con-jured up to save a dying theory. Had Fama and French predicted thatprice-to-book and market capitalization were valid risk factors beforethese variables were discovered to earn excess returns, that would havebeen a much different story. It would have shown that EMH was a power-ful theory whose deductive consequences were confirmed by subsequentobservations. However, Fama and French did not do this. They inventedTheories of Nonrandom Price Motion
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the new risk factors after price-to-book and market cap had been shownto produce excess returns.
As pointed out earlier, Fama and French justified their new risk modelby suggesting that low market-cap and low price relative to book are sig-nals of corporations at higher risk of failure. If this were true, it would im-ply (predict) that value strategies (buying low price-to-book stocks) andsmall cap strategies would earn subpar returns in bad economic times,when distressed companies are most likely to suffer.
Empirical evidence contradicts this prediction. A 1994 study found noevidence that value strategies do worse when the economy suffers.” Also,the disappearance of the excess return to small-cap stocks in the last 15years presents a problem. If cap size were indeed a legitimate risk factor,the returns to strategies based on it should continue to earn a risk pre-mium. Finally, neither the Fama-French risk model, nor any other EMHmodel, is able to explain the predictive power of price momentum indica-tors or the conjoint effect of price momentum and trading volume.
BEHAVIORAL FINANCE: A THEORY OF NONRANDOMPRICE MOTION
 
New theory is needed when existing theory confronts dissonant evidence.EMH has now been bombarded with a lot of evidence that contradicts itspredictions. It is time for a new theory.
The relatively new field of behavioral finance has come forward withseveral variations of a new theory of financial market behavior that ex-plains phenomena that EMH cannot. These theories are scientificallymeaningful in that they do not simply explain (fit) what has occurred, butthey make testable predictions that have been confirmed by subsequentobservational studies.
Behavioral finance incorporates elements of cognitive psychology,economics, and sociology to explain why investors depart from full ratio-nality and therefore why markets depart from full efficiency. By consider-ing the impact of emotions, cognitive errors, irrational preferences, andthe dynamics of group behavior, behavioral finance offers succinct expla-nations of excess market volatility as well as the excess returns earned bystale information strategies.
Behavioral finance does not assume that all investors are irrational.Rather, it views the market as a mixture of decision makers who vary intheir degree of rationality. When irrational investors (noise traders) tradewith rational investors (arbitrageurs), markets can depart from efficientpricing. In fact, market efficiency is a rather special condition that is less356 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
probable than other more plausible market conditions, where prices arelikely to diverge from rational levels and thus likely to experience system-atic predictable movements toward those levels.®! This explains why sys-tematic strategies based on stale information can profit.
This is all quite ironic with respect to TA. As discussed in Chapter 2,cognitive errors can explain how people form erroneous beliefs in thevalidity of subjective TA in the absences of sound supportive evidenceor even in the face of contradictory evidence. At the same time, how-ever, cognitive errors may also explain the existence of market ineffi-ciencies that spawn the systematic price movements that allow objectiveTA methods to work. That which explains the foolishness of subjectiveTA practitioners also explains the reasonableness of some objective TAmethods.
Foundations of Behavioral Finance
Behavioral finance rests on two foundational pillars: The limited ability ofarbitrage to correct pricing errors, and the limits of human rationality.When both notions are combined, behavioral finance is able to predictspecific departures from market efficiency that produce systematic pricemovements. For example, under certain circumstances, trends are pre-dicted to persist, whereas, under other circumstances, trends are pre-dicted to reverse. We will consider each of these pillars.
Limits of Arbitrage. To recap briefly, arbitrage is not the perfect en-forcer of efficient pricing that EMH assumes. The lack of perfect securitysubstitutes turns arbitrage from a risk-free, no-investment-required trans-action into one that requires capital and incurs risk. Even where there arevery good substitute securities, there is a risk that prices will diverge fur-ther from rational values before returning to them. Moreover, the in-vestors who supply trading capital to arbitrageurs don’t have unlimitedpatience, or unlimited capital. Consequently, they do not grant unlimitedlatitude to arbitrageurs in terms of the types of opportunities that can beconsidered for exploitation.
These constraints explain why security prices do not always reactproperly to new information. Sometimes, prices underreact, and some-times, they overreact. Add to this the impact of noise traders, who act onuninformative signals, and it becomes clear how security prices can sys-tematically depart from rational levels for extended periods of time.
Limits of Human Rationality. The constraints on arbitrage predictthat inefficiencies will occur, but they alone do not predict under whatconditions the inefficiencies will manifest. For example, arbitrage con-Theories of Nonrandom Price Motion 357
straints do not tell us under which circumstances markets are more likelyto underreact to new information than overreact. That is where the sec-ond pillar of behavioral finance, the limits of human rationality, comes in.
As we learned in Chapter 2, cognitive psychology has revealed that,under conditions of uncertainty, human judgment tends to err in pre-dictable (systematic) ways. By taking into account the systematic errorsof human judgment, behavioral finance can predict the type of departurefrom market efficiencies that are most likely to occur in a given set of cir-cumstances.
Of the two pillars of behavioral finance, more is understood about thelimits of arbitrage than about investor irrationality. This is because arbi-trageurs are expected to be rational, and economic theory has a firmergrasp on the behavior of rational actors than irrational ones. For example,it is not yet clear which specific cognitive biases and systematic judgmenterrors are most important in finance, but a picture is gradually emerging.This section describes the current state of behavioral finance’s under-standing of irrational investor behavior and the systematic price move-ments that arise as a result of them.
In contrast to traditional finance theory, behavioral finance asserts in-vestors make biased judgments and choices. In effect, market prices sys-tematically deviate from rational values because investors systematicallydeviate from full rationality. However, these mistakes are not a sign of ig-norance, but rather a consequence of the generally effective ways humanintelligence has developed to cope with complexity and uncertainty.
Behavioral finance and EMH are similar in that they both contend thatthe market eventually does get it right—that is, prices ultimately convergetoward rational valuations. They merely differ about the character ofthese departures and their duration. EMH says prices depart from rationallevels randomly and briefly. Behavioral finance says some departures aresystematic and last long enough to be exploited by certain investmentstrategies.
Psychological Factors
In Chapter 2 we saw the way cognitive errors impact subjective techni-cians and result in erroneous beliefs. In the context of behavioral finance,we will consider how cognitive errors impact investors and result in sys-tematic price movements. The cognitive errors discussed in the sectionsthat follow are not completely distinct, nor do they operate independently.However, they are presented separately for the purpose of clarity.
Conservatism Bias, Confirmation Bias, and Belief Inertia.The conservatism bias™ is the tendency to give too little weight to new358 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
information. Consequently, people fail to modify their prior beliefs asmuch as the new information would warrant. Prior beliefs tend to be con-served. Because of this, investors tend to underreact to new informationthat is relevant to security values and so security prices fail to respond ad-equately. Over time, however, prices do systematically catch up withvalue. This gradual, purposeful adjustment appears on a chart as a pricetrend. This is illustrated in Figure 7.5.
This conservatism bias is encouraged by the confirmation bias, whichcauses people to accept evidence that is consistent with a prior belief andreject or give too little credence to evidence that contradicts it. Thus,when investors hold an existing view about a security and new informa-tion arrives that confirms that belief, they will tend to give it more weightthan it deserves and overreact to it. Conversely, when new information ar-rives that contradicts the prior belief, they tend to treat it skeptically andunderreact.
The confirmation bias also causes investors’ beliefs, whatever theymay be, to become more extreme over time. The reason is as follows:Over time, news items arrive as a random mixture of confirmatory andcontradictory information. However, the confirmation bias causes themto be treated differently. Confirmatory information is given credence,thus strengthening the prior belief, while contradictory information willtend not to be believed and thus have a negligible impact on the in-vestors’ prior belief. Consequently, over time it is likely that prior beliefsstrengthen.
However, prior beliefs can be subject to a radical and irrational weak-ening if several bits of contradictory information arrive in a streak. Eventhough streaks are common in a random sequence, investors may committhe crime of small numbers and attribute too much significance when sev-eral pieces of information that contradict the prior beliefs arrive sequen-tially. In other words, the random streak may be erroneously interpretedas an authentic trend. These ideas are illustrated in Figure 7.8.
Too Much Anchoring and Too Little Adjustment. In Chapter 2we saw that, in uncertain situations, people rely on heuristics to simplifyand speed complex cognitive tasks like estimating probabilities. Oneheuristic, not discussed thus far, is called anchoring. It is relied upon toestimate quantities. The rule is applied as follows: An initial estimate ofthe quantity is made, based on preliminary information called the an-chor. Then upward or downward adjustments are made to the initial es-timate based on additional information. The anchoring rule seems tomake sense.
However, in practice, people commonly make two mistakes when ap-plying this heuristic. First, initial estimates can be strongly influenced by aTheories of Nonrandom Price Motion 359
{ Confirmatory News Contradictory News |
 
 
 
 
Strong | 1 tl |robo 'mar ' ut| er t 'ota { iBelief ' 'Strength F ---' ! ,Confirmation Bias :& Crime ofConservatism Bias Small NumbersWeak
 
Time
FIGURE 7.8 Revising beliefs.
completely irrelevant anchor. This occurs even when the anchor’s irrele-vance is completely obvious. Second, even in cases where the initial esti-mate is based on relevant anchoring information, there is a tendency forsubsequent adjustments up or down to be too small (the conservatismbias). In other words, the additional information is given too little weightrelative to the initial estimate.
The tendency to seize on an irrelevant anchor was demonstrated in astudy in which people were asked to estimate the length of the MississippiRiver (actual length = 2,348 miles). Subjects were first asked if the lengthwas greater than or less than some arbitrary length. This number assumedthe role of anchor in the subject’s minds. When the question posed was: Isthe length greater or less than 800 miles? Most people correctly said theMississippi was longer than 800 miles. Subjects were then asked to give anestimate of its actual length. Other subjects were given the number 5,000miles in the greater than or less than part of the question. Again, most peo-ple correctly said the river’s length was less than 5,000 miles, but their es-timates of the river’s actual length were, on average, greater than theestimates given by subjects who were asked to make a comparison with800 miles. This revealed that the subjects seized on the number of milesgiven in the question as an anchor, and then made inadequate adjustments360 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
from there. If the figure 800 or 5,000 were not truly influencing the esti-mate, the estimates of length would have been about the same, irrespec-tive of whether the question involved a comparison with 800 miles or5,000 miles. The anchor had a strong influence.
Another experiment showed that people will anchor on a numbereven when it is obviously irrelevant.” Subjects were asked about factsthat are typically not part of common knowledge—for example, “Whatis the percentage of African nations in the United Nations?” The an-swers to each question involved a percentage (0 to 100). Before eachquestion, a wheel of fortune with 100 numbered slots was spun in frontof the subjects. The study showed that the subjects’ answers werestrongly affected by the wheel’s obviously random result. For example,if the wheel landed on the number 10, the median estimate was 25 per-cent, but when the wheel stopped on 65 the median estimate was 45percent.
The anchoring heuristic is thought to be related to investor underre-action. Underreactions to bullish news cause asset prices to remain toocheap, whereas underreactions to bearish news leave prices too dear.Over time, the market’s temporary state of inefficiency is resolved asprices drift (trend) to the rational level. Thus, anchoring can help explainthe occurrence of price trends.
Anchoring may explain the profitability of a momentum strategy al-luded to earlier.” It is based on a simple technical indicator, a stock’sproximity to its 52-week high. Because this information is available innewspapers and on various web sites, investors may fixate (anchor) on it.In other words, investors may fixate or anchor on the 52-week high priceif a stock is currently trading near that level. In effect, the stock gets stucknear its 52-week high. Thus the price fails to respond properly to new bull-ish information, and it becomes temporarily underpriced. Ultimately thepricing error is corrected as the stock responds with a systematic move-ment higher.
Anchoring to Stories. Investors not only become anchored to num-bers; they can get stuck on intuitively compelling® stories, too. The effectis the same; prices do not respond efficiently to new information and thusdepart from rational valuations.
Stories are compelling because “much of human thinking that resultsin action is not quantitative, but instead takes the form of storytelling andjustification.” As pointed out in Chapter 2, investors rely more on causalnarratives than on weighing and combining evidence to estimate probabil-ities and make decisions. People seem to need simple rationales to justifytheir actions.” The most compelling rationales are plausible, easily under-stood, easily retold, cause and effect chains filled with concrete and color-Theories of Nonrandom Price Motion 361
ful details. These are the tales that captivate investors and incite buyingand selling. These are the stories that get stuck in investors’ minds.
Optimism and Overcontidence. To recap, people are generally tooconfident about the quality and precision of their knowledge. Thus, in-vestors tend to be overconfident about their private interpretations ofpublic information and overly optimistic about the profits they willachieve. The combined effect of overconfidence and overoptimism leadsinvestors to overreact to their private information, and in turn pushes se-curity prices too far. Overextended price movements lead to price rever-sals and systematic movements back toward rational levels.
The Crime of Small Numbers (Sample Size Neglect). The crimeof small numbers is the failure to consider the number of observationscomprising a sample being used to form a conclusion. Thus, it is fallaciousto judge whether a datum was produced by a random or nonrandomprocess or to estimate a population parameter on the basis of a small sam-ple. For example, if a sequence of 10 coin flips produces 7 heads, it wouldbe invalid to conclude the coin has a 0.7 probability of producing heads.The true head rate for a coin can be reliably inferred from only a largenumber of flips.
Investors who neglect sample size are easily deceived when trying todecide if a time series is random or not. For example, by generalizing toohastily from too little evidence, investors may incorrectly conclude that afew quarters of positive earnings growth indicate that the company has es-tablished a valid growth trend. We have already seen that a small clusterof positive quarters can easily occur in a random-earnings stream. Thus,the crime of small numbers helps explain why investors may overreact toa short sequence of good earnings reports.
The crime of small numbers can cause two different judgment errors:the gambler’s fallacy and the clustering illusion. Which of these two errorsresults depends on the observer's prior belief about the process beingwatched. Suppose an investor is watching a sequence of price changesthat are truly independent of each other (a random walk).
If the investor holds a prior belief that the process is random, the er-ror will most likely be the gambler’s fallacy. Recall that on the basis ofcommon sense we expect random processes to display more flip-flops andfewer streaks (trends) than actually do occur. As a result, a streak of posi-tive price changes will lead naive observers to falsely conclude that atrend reversal (a negative price change) is due. Thus, after five heads, theobserver erroneously concludes a tail is more likely than 50/50, its truelikelihood. In reality, a random-walk sequence has no memory. Thus, theoccurrence of a positive streak does not alter the probability of the next3G2_ METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
outcome in any way. The false expectation of a reversal is called the “gam-bler’s fallacy” because the statistically naive tend to make this mistakewhen watching a roulette wheel; a sequence of black outcomes is thoughtto increase the chance of a red. Wrong!
The other fallacy stemming from sample-size neglect is the clusteringillusion. It occurs when an observer has no prior belief about whether theprocess generating the data is random or nonrandom The clustering illu-sion is the misperception of order (nonrandomness) in data that is actu-ally a random walk. Again, imagine someone observing the outcomes of aprocess that is truly a random walk trying to determine if the process israndom or nonrandom (orderly, systematic).”| Recall that small samplesof random walks often appear more trended (clustered) than commonsense would lead us to expect (the hot hand in basketball). As a result ofthe clustering illusion, a sequence of positive price or earnings changes iswrongly interpreted as a legitimate trend, when it is nothing more than anordinary streak in a random walk.
Social Factors: Imitative Behavior, Herding,and Information Cascades”
We have just seen how investor behavior viewed at the level of the individ-ual investor can explain several types of systematic price movement. Thissection examines investor behavior at the group level to explain system-atic price movements. In contrast to conventional TA theories of investorpsychology, we will see that some group behaviors are quite rational.
When faced with uncertain choices, people often to look to the behav-ior of others for cues and imitate their actions. This is what is meant byherd behavior. I should clarify that herd behavior is not defined by similar-ity of action. Similar actions by many individuals can also occur when in-dividuals have made similar choices but those choices have been arrivedat independently. Herd behavior refers specifically to similarity of actionarising from imitation. As I will point out, the choice to imitate can be en-tirely rational.
There is a major difference between similar behaviors arising fromherding and similar behaviors arising from many individual decision mak-ers making the same choice independently. Herd behavior stops the diffu-sion of information throughout a group, but independent decision makingdoes not. When information diffusion is impeded, it becomes more likelythat investors will make the same mistakes and prices will systematicallydiverge from rational levels.
To see why herd behavior impedes the spread of information, con-sider the opposite case, where individuals evaluate information indepen-dently and make autonomous choices. Suppose a large number ofTheories of Nonrandom Price Motion 363
investors are independently evaluating a stock. Some may mistakenlyvalue it too high, while others mistakenly value it too low. Because themistakes are made independently, they will tend to be offsetting, makingthe average valuation error close to zero. Here, each investor has made anindividual appraisal and in effect acted on a unique signal. This makes itfar more likely that all the available information about the stock will havebeen considered and built into the price of the stock. Even if no single in-vestor was in full possession of all the facts, the group, taken as a whole,is likely to be nearly fully informed.
In contrast, when investors elect to imitate the behavior of othersrather than deciding independently, all the relevant information about thestock is less likely to be fully diffused throughout the entire group, mak-ing it more likely that the stock’s price will not reflect all relevant infor-mation. However, an individual investor electing to imitate may bemaking a perfectly rational choice. Not everyone has the time or exper-tise to evaluate a stock, so it can make sense to copy an investor with agood track record, such as Warren Buffett. Once Buffett does his analysisand makes his buy or sell decision known, investors may cease their ownefforts to gather information and evaluate the stock. This makes it morelikely that, if some factor has escaped Buffett’s notice, it will remain un-noticed by the community of investors. So here is a case where individu-als take actions that are fully rational on an individual basis but harmfulto them as a group.
Why Do We Imitate? At one time it was thought that imitative behav-ior was a consequence of social pressure to conform. Early experimentalevidence seemed to confirm this.” In one study, an authentic subject wasasked to estimate the length of line segments in the presence of fake sub-jects who also gave estimates. The fakes, who were actually part of the ex-periment, intentionally gave wrong answers. Even though the correctanswer was obvious, the authentic subjects often went along with thegroup rather than give the obviously correct answer. However, a later ex-periment,” in which the authentic subject was isolated from the group ofconfederates, showed the subject still imitated the group’s obviouslywrong answer. This finding implied that social pressure does not explainimitative behavior. A better explanation emerged. The subjects seemed tobe relying on a social heuristic: When one’s own judgment is contradictedby the majority, follow the majority. In other words, people operate withan implicit rule: The majority is unlikely to be wrong.
“This behavior is a matter of rational calculation: in everyday livingwe have learned that when a large group of people is unanimous in itsjudgment on a question of simple fact, the members of the group are al-most certainly right.” Similarly, most of us operate with the principle364 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
that, when an expert tells us something that contradicts our commonsense, we tend to heed the expert. So it is entirely rational under condi-tions of uncertainty to look to the advice of experts or the actions of themajority.
Information Cascades and Herd Behavior. The uncertainties ofinvesting make it likely that investors would rely on an imitation heuris-tic rather than independent decision making. This has important implica-tions for the emergence of systematic price behavior because imitationgives rise to “information cascades.”” An information cascade is a chainof imitative behavior that was initiated by the action of one or just a fewindividuals. In some instances that initiating action may have been a ran-dom choice.
The emergence of an information cascade can be illustrated with a hy-pothetical example. Suppose two new restaurants open for business onthe same day, right next door to each other. The first hungry customer toarrive must choose one, and has little information on which to base a de-cision. That customer’s choice is, therefore, a random decision. However,when the second customer arrives, there is an additional piece of informa-tion: one of the restaurants has a patron. This may trigger the second cus-tomer to go into the same place. Because the first customer's selectionwas a guess, we know that the second customer's choice was based on anuninformative signal. When the third customer arrives and sees onerestaurant empty and the other with two customers, the chance is in-creased that the third customer will also make an imitative choice. In theend, a cascade of dependent choices, triggered by the first customer's ran-dom choice, may ultimately cause one establishment to thrive and theother to fail, and it is entirely possible that the failing establishment of-fered better food.
Thus, an initial random event sets the course of history down one par-ticular path (restaurant 1 succeeds) rather than another path (restaurant 2succeeds). As time goes on, the likelihood that one restaurant will thriveincreases while the likelihood that the second establishment will fail in-creases. In the same way, an initial random price movement can trigger suc-cessive rounds of imitative investor behavior, resulting in a long-durationlarge amplitude price swing.
Now consider what would have happened if many customers hadmade independent restaurant choices, allowing both establishments to besampled by many customers. By combining their findings, they wouldhave discovered which was the better of the two restaurants. However, inan information cascade, imitative behavior prevents the accumulation andsharing of many independent appraisals. Thus, information cascadesblock the diffusion of information and rational choice.Theories of Nonrandom Price Motion 365
The information cascade model reveals the flaw in the common sensenotion that financial market prices are determined by a process like vot-ing, in which many individuals make their own assessment.” In an infor-mation cascade, investors make the rational choice of copying the actionsof others, rather than expending the considerable effort required to arriveat an independent choice. Voters do one thing, and investors sometimesdo something entirely different.
The Diffusion of Information Among Investors. We have seenthat information cascades can explain why the rational behavior of individ-ual investors can impede the diffusion of information. This is important forTA because the rate at which information spreads among investors can ex-plain the occurrence of systematic price movements. EMH assumes almostinstantaneous information diffusion and, consequently, almost instanta-neous price adjustments. Such price adjustments are too quick to be ex-ploited by TA methods. However, information that diffuses slowly allows forgradual, systematic price movements that can be exploited by TA methods.
Despite the prevalence of advanced communications technology, thepreferred method of exchanging investment information is still a goodstory told person to person. This predilection, a result of several millionyears of evolution, has been confirmed by the studies of behavioral-econo-mist Robert Shiller. He has shown that the word-of-mouth effect is strongeven among people who read a lot. A story about a hot new issue hasgreater impact in conversation than a statistic about the high failure rateof new companies.
The way stories spread among investors has been studied with mathe-matical models similar to those used by epidemiologists to study the spreadof disease within a population. Unfortunately, these models have not beenas accurate in the investor domain as they have been in the realm of biology.This is explained by the fact that the mutation rate of circulating ideas is farhigher than the mutation rate of organisms. However, the models have beenenlightening in another way; they explain how stories spread so rapidly.
One reason a “new” story can spread so rapidly is that it is not reallynew. Investors’ minds are already infested with many familiar story scripts,and it does not matter that the panoply of stories residing in investors’minds contradict each other. For example, investors can be comfortablewith the notion that the stock market cannot be predicted and at the sametime hold the opposing notion that it can. No doubt, most investors havebeen exposed to experts who have espoused both points of view. Our mindsare able to accommodate a wide variety of contradictory ideas because thescripts lie there inert with no demand that we choose one side or the other.
Nevertheless, this state of affairs can change rapidly. Even a slightrandom perturbation in the news or the market’s behavior can cause an366 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
idea that had been hibernating in investors’ minds to spring to life andconsume their attention.
Shifts in Investor Attention. Where investors are focusing their at-tention at any given moment can shift dramatically due to minor changesin the news. “The human brain is structured to have essentially a single fo-cus of attention at a time and to move rapidly from one focus to an-other.”* The brain had to evolve a capacity to selectively attend to only atiny trickle of the torrent of information that pours in from the world. Thisfiltering process is both a mark of human intelligence and a source ofjudgment error. Numerous instances of expert error are attributable tomissing important details. However, before outcomes are known, wenever know which details deserve our attention.
Among the automatic unconscious rules used by the brain to filter rel-evant from irrelevant information is the rule to look to other people forcues. In other words, we presume that what grabs the attention of othersmust be worthy of our attention as well. According to economist RobertShiller, “the phenomenon of social attention is one of the great creationsof behavioral evolution and is critical for the functioning of human soci-ety.” Although communal attention has great social value, because it pro-motes collaborative action, it has a downside. It can lead an entire groupto hold an incorrect view and take similar mistaken actions.
In the opposite situation, where individuals form their own views in-dependently, errors of attention would be random and self-canceling. Al-though this mode of thought makes it harder to organize joint