Biological Basis for Errors in Risk Assessment

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Biological Basis for Errors in Risk AssessmentOctober 2009



Biological Basis for Errors in Risk Assessment 1

Two differing schools of thought dominate current thinking about the stock market: One follows theteachings of University of Chicago professor Eugene Fama, who in the 1960s introduced the efficientmarket hypothesis—the idea that stock prices reflect all publicly available information. More recently, anew generation of scholars has begun to question the universality of Fama’s teachings, suggesting that,in the short term at least, stock markets indeed are less than fully rational and that analysis of pastbehaviors can help anticipate the future direction of the market—the premise fueling research in the field

of behavioral finance.

The assessment of risk has an important role in investment management, and plan sponsors remainkeenly aware of the risk level of their total portfolio, as well as the risk level for each individual managerwithin the total plan. Likewise, investment managers themselves consider the overall risk of the portfoliosthey manage for plan sponsors. And while it is important to know the portfolio-level risk, it is critical thatthis risk be assessed correctly in order to yield meaningful results. Herein lies one of the great challenges:are investors analyzing risk correctly or are they falling victim to incorrect risk assumptions that lead tosub-optimal (irrational) decisions? If the latter, what can be done to overcome this?

A great deal of research in recent years suggests that our risk assessment is flawed—that is, thathumans make errors in judgment—because we are biologically programmed to respond to some risks in

certain ways. Behavioral research has shown that most of the incorrect decisions made by investors stemfrom overconfidence. This paper will detail some of the most common errors in judgment, explore how thefield of neuroscience is working to explain why investors make these errors in assessing risk, the brain’sresponse to risk and then, ultimately, how a quantitative approach to investing can help overcome humanerrors in risk assessment.




Contents

I. Common Human Errors in Judgment

II. How the Brain Responds to Risk

III. Can Quantitative Investing Overcome Flawed Risk Assessment and Biology?

IV. Quantitative Investing at OFI Institutional – One Manager’s Experience

V. Conclusions

Notes






Whereas the odds are known in the gambler’s fallacy, the hot-hand fallacy applies to streaks where theodds are not known. Imagine a professional baseball player that has gotten a hit in his last three at-batsor a home run in each of the last ten games played. Observers are left to wonder how likely it is that theplayer will get a hit his next at-bat or hit a home run in his next game. Statistically speaking, the odds areno greater than if he had not had a hit or home run for some time. Hersh Shefrin writes in his 2007 work,Behavioral Finance: Biases, Mean-Variance Returns, and Risk Premiums , that the idea that streaks have

predictive power is an illusion: representativeness, the human tendency to discern a pattern (even wherenone may exist) is what leads us to extrapolate recent performance. 5

Gilovich, Vallone, and Tversky (1985) found that virtually everyone has some belief in the hot hand. 6 Theirresearch, which focused on professional basketball, found that 91 percent of fans thought that a playerhad “a better chance of making a shot after having just made his last two or three shots.” Moreover,68 percent thought the same for free throws. To test whether the hot hand actually exists, Gilovich et al .looked at team shooting percentages over an entire season. Their research showed that the proportion ofshots hit was in no way related to the number of previous shots in a row a player had either hit or missed.

Analysis of the data demonstrated that successful and unsuccessful runs for each player were notsignificantly different from the number of expected runs given the player’s overall shooting percentageand assuming that all shots were independent of each other. The conclusion: “[B]elief in the ‘hot hand’ is

not just erroneous, it could also be costly.”

Both the gambler’s and hot-hand fallacies can be observed in the investing public’s behavior. RachelCroson and James Sundali, in their 2005 piece, The Gambler’s Fallacy and the Hot Hand: Empirical Datafrom Casinos , wrote that “it has been argued that the disposition effect in finance (the tendency ofinvestors to sell stocks that have appreciated and hold stocks that have lost value, discussed in depthbelow) is caused by gambler’s fallacy beliefs… other evidence demonstrates that consumer’s mutual fundpurchases depend strongly on past performance of particular fund managers (Sirri and Tufano, 1998),even though the data suggests that performance of mutual fund managers is serially uncorrelated (e.g.,Cahart, 1997). Thus individuals are presumably making investment decisions based upon the belief thatparticular funds or fund managers are hot.” 7

Interestingly, the gambler’s fallacy and the hot-hand fallacy work in opposite directions: the first stipulatesthat streaks or deviations reverse quickly while the latter assumes that trends will extend. Thus is thesubtle and quixotic nature of our behavioral biases—we reach opposite conclusions about similar patternsdepending upon how they are framed. We discuss framing errors in greater detail later in this article.

Disposition Effect

“When an individual investor sells a stock in his portfolio, he has a greater propensity to sell astock that has gone up in value since purchase than one that has gone down.” 8

— Barberis and Xiong (2009)

Research has shown that investors tend to hold losing stocks too long and to sell winning stocks too

early. In assessing holdings within a portfolio, investors are emotionally affected by whether a stock waspurchased for more or less than its current price. Considering an investor’s reluctance to sell a stock thathas declined in value, Statman and Shefrin (1985) concluded that people avoid selling a stock that hasgone down in value in order to avoid the pain and regret of having made a bad investment choice; thisphenomenon has been termed the “disposition effect.” 9




Tversky and Kahneman’s prospect theory, which describes how people make choices in situations wherethey must decide between alternatives that involve risk, offers a simple understanding of the dispositioneffect: If an individual investor is risk-averse over gains, that investor should be inclined to sell a stockthat is trading at a gain. 10 If the investor is risk-seeking over losses, the expected course of action wouldbe to hold a stock that is trading at a loss. Given the relationship between risk-averse and risk-seekingbehaviors and trading activities, researchers have concluded that investors are more willing to assume

risk with a security that has an unrealized loss than one with an unrealized gain and thus are moreinclined to sell the latter. Said another way, investors often (and often incorrectly) assume that the stockthat has declined in value is more likely to revert back to (at least) its original purchase price rather thancontinue to decline in value.

In his 1998 paper “Are Investors Reluctant to Realize Their Losses,” Terrance Odean studied 10,000accounts at a large brokerage house and found that, overall, investors were more likely to realize gainsthan losses in their accounts. 11 While he found that some movement occurred in late December asinvestors sought to capture tax-related benefits, to rebalance portfolios, or to trade in consideration oftransaction costs, his research suggested that once the data were controlled for rebalancing and forshare price, the disposition effect remained. Further, the winning stocks that investors chose to sell, insubsequent months outperformed losing investments that remained in the portfolio by an average of 3.4

percent. In fact, Odean’s research showed that, on average, winning stocks that were sold tended tooutperform the losers that were not sold over the next six to twenty-four months.

The disposition effect—this tendency to hold on to losing stocks to a greater extent than to winningstocks—provides a rationale for why momentum factors often tend to be an effective tool in makinginvestment decisions. If investors are inclined to sell their winning stocks, without regard to the earningsor other fundamental considerations, their selling applies a downward bias to the stock’s price. That is,absent this emotion-driven selling, the stock’s price would be higher than its current market price.Conversely, if investors are reluctant to sell losing stocks, again without regard to underlyingfundamentals, then their lack of selling exerts an upward bias on the stock’s price. In other words, absentthe disposition effect, investors would be more likely to sell their losing stocks, which would have theeffect of pushing the stock price below the market price. So, whether a stock is rising or falling in price, a

strategy of following the momentum (buy the winners, sell the losers, sometimes summarized as “thetrend is your friend”) can be justified due to the disposition effect.

Interestingly, financial research has shown that the disposition effect is observable over horizons of six tofifteen months. For horizons shorter than six months, price momentum has a tendency to reverse, withthe reversal effect more powerful as the horizon shortens. This effect is likely due in part to investor over-reaction: when good (bad) news on a firm is released, investors rush to buy (sell) with insufficient regardto the change in the stock’s price; the result is that the price is ultimately pushed too high (low) and areversal occurs. For longer horizons, say, of two years or more, price momentum also has a tendency toreverse. Viewed in terms of behavioral finance, this phenomenon is the result of the buying pressure ofinvestors following the hot-hand fallacy outweighing the selling pressure of investors following thedisposition effect over a sufficiently long horizon. Eventually, the trend-following investors drive the stock

price too far, high or low, with a reversal ultimately following.

Quantitative investment managers often make use of multiple factors which try to capture these varyingaspects of momentum and reversal. OFI Institutional, for example, employs two Price Momentum factorsas well as two Price Pullback factors. The former are purely momentum factors, designed to capture thedisposition effect. The latter are multi-faceted, trying to capture reversal and momentum effects. Detaileddescriptions of these, and every other factor used by OFI Institutional, are available in the publication OFIILibrary of Factors .




Base-Rate Neglect

“The Base Rate Fallacy is the belief that probability rates are false. When presented withstatistics about the population as a whole, people tend to ignore them and think about themselvesas completely different entities.” 12

— Elliott (1996)

Base-rate neglect (or fallacy)—another example of flawed risk assessment—explains how in makinginferences regarding probability in the presence of specific scenario information, people tend to ignoregeneric information. In order to gain a better understanding of the principle, let us review a commonlycited example of base-rate neglect. Imagine that you visit your family physician because you areconcerned that you have contracted a disease found in one out of a thousand people in the generalpopulation. To better determine whether you have in fact contracted the disease, the doctor orders a testwith an accuracy rate of 95 percent. If the test result comes back positive, what are the chances that youreally have contracted the disease?

Most people (and physicians) would incorrectly assume that in the above scenario there is a 95 percentchance of having the disease, given the positive test result. However, this assessment fails to consider

the possibility that the test result is wrong , i.e., showing a false positive. This intuitive but erroneousconclusion fails to consider the rate of incidence, or “base rate,” when evaluating probability. In actuality,there is less than a two percent chance that you have contracted the disease.

To better understand this, we must briefly turn to Bayes’ theorem, which says that the conditionalprobability (i.e., the probability of event X given event Y) is the ratio of the probability of both events X andY occurring to the probability of event Y occurring for any reason. In our example, the probability of beingsick, given a positive test result, is the probability of being sick and receiving a positive result divided bythe probability of receiving a positive result for any reason—i.e., a true or false positive.

To calculate Bayes’ theorem, we must have the following information:• P(S): probability that you are actually sick (1 out of 1,000 or 0.001)• P(N): probability that you are not sick ([1-0.001] or 0.999)• P(A): probability that the test is positive and you are sick (95 percent accuracy or 0.95)• P(I): probability that the test is positive and you are not sick ([1-0.95] or 0.05)

Bayes’ theorem:[P(A) x P(S)]

[P(A) x P(S)] + [P(N) x P(I)]

[0.95 x 0.001][0.95 x 0.001] + [0.05 x 0.999]

In the above equation, the numerator represents the probability that the test is accurately identifying anindividual who has contracted the disease (a true positive), while the denominator represents the totalnumber of true and false positive results expected with the test. Carrying out the calculation reveals just a1.87 percent chance of having contracted the disease if the test result is positive. While the odds are low,this 2 percent chance is nevertheless much higher than the base rate, so the test has provided someinformation—just nothing close to the 95 percent certainty level that most people would assume.

Leonard Mlodinow writes of his own personal experience with this issue in his book The Drunkard’s Walk:How Randomness Rules Our Lives .13 Mlodinow recalled how both he and his wife were denied life




insurance in 1989 based on a test of Mlodinow’s blood which returned a positive result for HIV. The denialprompted Mlodinow to seek further testing, which also came back positive. His doctor was sorry to reporta 999 out of 1,000 chance that Mlodinow would be dead within a decade.

Mlodinow later learned that for the HIV test, 1 of every 1,000 samples can produce a positive result evenwhen the blood is not infected with the virus (i.e., a false positive). And while on the surface the odds may

seem as daunting as those quoted by his doctor, they are not. The doctor confused the chances thatMlodinow would test positive if he were not infected with the HIV virus with the chances that he would notbe HIV positive if he tested positive. (Read that again: the order, importantly, is reversed.) The latter takesinto account the possibility of a false positive, which, as in the prior example, has a significant effect ontrue probabilities.

Bayes’ theorem shows that the probability of event X occurring given that event Y occurred (theprobability of a positive test result given Mlodinow was not infected) will generally differ from theprobability of Y given X (the probability being uninfected given a positive test result). Studies have shownthat the failure to recognize this difference is a common occurrence not only in the medical community butin the population at large. Mlodinow explains the importance of how this related to his own experience: ifthe test had considered a narrower sample—heterosexual, monogamous, white, non-IV drug-using

Americans like himself—Mlodinow claims the doctor could have developed a more accurate assessmentof the test’s results. Mlodinow goes on to state that, per the Centers for Disease Control and Prevention,the prevalence of HIV in his particular subset of the population in 1989 was 1 in 10,000.

Assuming a false-negative rate near zero, one person in every10,000 will test positive due to the presence of the HIV virus. Inthis same sample, 10 people out of 10,000 will have a falsepositive for a total of 11 positive results. Instead of asserting thatMlodinow would almost surely be dead within a decade, it wouldhave been more accurate to state that there was greater than a90 percent chance that he was not infected at all. And, in fact, hewas not infected: the screening test was fooled by certain

markers that were present in his blood even though the virus forwhich the test was screening was not present.

Base-rate neglect is a “cognitive heuristic”: a rule of thumb formaking quick judgments. We will discuss this idea in greaterdetail as we examine the role that the brain plays in these errorsin risk assessment; but for now, consider that in base-rateneglect, individuals fail to seriously consider probabilities andbase rates—even when they know they are important. This isevident in the example above, where Mlodinow’s doctor failed toconsider the subset of the population that has a risk profileclosest to that of his patient.

How can the concept of base-rate neglect tie into the investment industry? In his work Behavioral Financeand Wealth Management: How to Build Optimal Portfolios That Account for Investor Bias, Michael M.Pompian suggests that investors, rather than perform diligent research on a company, tend to categorizea stock as either “value” or “growth” and draw conclusions about that stock’s risk and reward profilebased on that categorization. By doing so, investors can, and often do, miss important variables thatcould impact the success of their investment.

10,000 people

1: 10,000 (true positive) 10: 10,000

(false positive)

11 people withpositive results

1: 11 (true positive)

9.1% 10: 11

(false positive) 90.9%




Loss Aversion and Status Quo Bias

“The central assumption of the theory (of loss aversion) is that losses and disadvantages have agreater impact on preferences than gains and advantages.” 14

— Tversky and Kahneman (1991)

“One implication of loss aversion is that individuals have a strong tendency to remain at the statusquo, because the disadvantages of leaving it loom larger than the advantages…” 15

— Thaler (1992)

Another concept that comes up frequently when discussing investor behavior is the idea of loss aversion,the tendency for people to have a much stronger preference for avoiding losses than for acquiring gains.Through their research, Tversky and Kahneman found that individuals are much more distressed byprospective losses than they are happy about equivalent gains. 16 In general, tend to be more reluctant toaccept an uncertain payout over a more certain, albeit lower payout. Consider the following:

• In scenario A, there is an 80 percent probability of gaining $4,000, and a 20 percentprobability of gaining $0

• In scenario B, there is a 100 percent probability of gaining $3,000

Although scenario A has an expected value of $3,200 [(0.8 x $4,000) + (0.2 x $0)], in experiments mostpeople choose the guaranteed payout of scenario B, foregoing the risk of leaving empty-handed. Whenfacing a loss, however, people tend to be risk- seeking , meaning a scenario with a higher expected lossbut with some chance of no loss at all is preferable to a guaranteed loss, despite a lower expected value.

• Scenario C exhibits an 80 percent chance of losing $4,000; a 20 percent chance of losing $0• In scenario D, there is a 100 percent probability of losing $3,000

Given these two options, scenario C (expected value: -$3,200) and scenario D (expected value: -$3,000),people choose scenario C by a wide margin as there is some chance they will lose nothing at all.

As world stock markets plummeted in late 2008, investors sought the safety of the U.S. Treasury market,despite yields that were historically low—or even negative. December 2008, for example, saw one-monthT-bills selling at 0% for the first time ever, and the annualized yield on three-month T-bills fell just below0%—an indication that investors (mainly banks seeking to shore up their balance sheets as year-endapproached) preferred a slight but known loss to the risk of even larger losses that might have resultedfrom staying in the equity markets. At the December auction, demand for U.S. Treasury securities was sostrong that the government could have sold four times as much as it did.

Kahneman, Knetsch, and Thaler (1990) first proposed loss aversion as an explanation for somethingcalled the “endowment effect,” 17 that is, the fact that people place a higher value on a good that they ownthan on an identical good they do not own. Similarly, Samuelson and Zeckhauser (1988) introduced theconcept of a “status quo bias” 18 in which individuals prefer things to remain the same. When valuing agood, individuals demand a much higher price to sell it than they would be willing to pay to acquire it.

Can these theories and biases translate to the current economy, to market practices, and so forth? Theanswer is yes. The housing market, for example, provides strong evidence for the endowment effect.Shefrin states that in a normal housing market, homeowners tend to overvalue their homes by an averageof 12 percent. 19 During a downturn, such as we have been experiencing over the last several years,sellers list their home for an average nearly one-third above fair market value.




The status quo bias, meanwhile, plays a particularly important role in 401(k) planning, both in terms ofcontributions to a plan and to the asset allocation choices for those contributions. Overall, when U.S.employees must “opt in” to their employer’s 401(k) plan, roughly 25 percent of workers fail to sign up,despite the fact that in many cases the employer will match some portion of employee contributions(these non-participants effectively “leave money on the table”). However, participation increasesdramatically when employees must opt out of, rather than in to, the plan. Ed Ferrigno, vice president of

Washington Affairs for the Profit Sharing/401(k) Council of America, reports that when an employer’s planfeatures automated contributions, participation increases to more than 90 percent. 20 Similar patterns canbe observed globally: countries where employees must opt out of a retirement plan show markedly higherparticipation rates than in countries where employees must opt in. In the U.S., the Pension Protection Actof 2006 addressed this issue by allowing employers to automatically enroll workers in their 401(k) plan ata minimum default contribution of 3 percent (and allowing employees 90-days to opt out without penalty).

In another example of the status quo bias, research shows that many 401(k) plan participants enroll in thedefault fund rather than making a more active decision. Dana Muir, Professor of Business Law at theUniversity of Michigan, notes that in a 2002 study of 401(k) plans, up to 81 percent of plan assets wereinvested in a default option. 21 This means that plan providers should give careful consideration to thedefault set-up. All too often, the default is a money market fund or other extremely low-risk asset class.

While such a choice provides protection against the loss of nominal principal, loss of real principal isprobable—i.e., there is a good chance the low-risk choice will not keep pace with inflation, eroding thereal value of the retirement account. As directed by the Pension Protection Act, the U.S. Department ofLabor defined Qualified Default Investment Alternatives to include “life-cycle” funds, which help to providethe investor an age-appropriate blend of asset classes, along with target-date retirement funds, balancedfunds, and professionally managed accounts.

Framing Errors

“[P]resented with the same basic economic proposition, we make markedly different choicesdepending on whether the deal gets framed in terms of loss or gain.” 22

— Conniff (2008)

In the assessment of risk and reward, the manner in which choices are framed can play a significant rolein decision-making. Massimo Piattelli-Palmarini, in his 1994 book Inevitable Illusions: How Mistakes ofReason Rule Our Minds, presented the following example 23:

Imagine that the United States is preparing for the outbreak of an unusual disease that isexpected to kill 600 people. Two alternative programs to combat the disease have beenproposed. Assume that the exact scientific consequences of the program are as follows:

• If Program A is adopted, 200 people will be saved.• If Program B is adopted, there is a one-third probability that 600 people would be saved

and a two-thirds probability that no people would be saved.

In this same test, two additional possibilities were presented:• If Program C is adopted, 400 people would certainly die.• If Program D is adopted, there is a one-third probability that no one will die and a two-

thirds probability that 600 people would die.

In the above example, rational individuals should view each choice as being equal since in each scenario,the expectation is that 200 lives will be saved. However, the manner in which each choice is presented




evokes strong reactions that can affect decision-making. When given a choice between Program A andProgram B, respondents preferred Program A at a rate of nearly three to one, indicating risk-aversion:people typically consider it better to save some people than to risk everyone being lost. Contrarily, given achoice between Programs C and D, respondents chose Program D over Program C in similar numbers,indicative of risk-seeking behavior: framed in terms of certain death rather than of saving lives, peopletend to feel it is better to try and save everyone rather than to accept 400 certain deaths.

This effect has clear implications for investment managers—the response a manager has to corporatenews is likely influenced by how the news is framed and thus interpreted by the manager. However, for aquantitative manager, framing errors can be largely mitigated: By relying only on the result or expectedvalue, the change in investment opinion (if any) to the news will be the same irrespective of the manner inwhich the news is either framed or received.

Noah Myers, a senior portfolio manager with Smith Barney, spoke to Richard Conniff about framing errorsin the context of a market crash. He contends that investors suffering steep declines are only able to viewtheir portfolio through a “framework of loss.” 24 Myers claims that most investors reach a point during acrash where they give up and sell, at that point becoming much more rational and seeing the potential forgain as the markets begin to rise again. Selling low, only to reinvest as stocks begin moving higher, can

be especially costly. Consider this: No one knows for sure when a down-market has turned positive untilwell after the fact, and missing even a few days of an up-market can dramatically affect performance.

Looking at the first few trading days of the complete post-World War II market cycles underscores thispoint: The average absolute price-only return of the S&P 500 ® Index for the first 20 trading days of eachnew bull leg (including the apparent bull market that began on March 9, 2009) is 11.8%—significantlybetter than the annualized 9.6% total return of common stocks for the last 83 years, from 1926 through2008.

Recency Effect

“Given a list of items to remember, we will tend to remember the last few things more than those

things in the middle.” 25

— Changingminds.org (website)

From a psychological perspective, the recency effect is found when subjects are able to recall items fromthe end of a list more readily than from its beginning and is considered a reflection of short-term memory.In finance, the recency effect also relies on short-term memory and stands in direct opposition to thegambler’s fallacy (a false belief that the probability of a random event, in a sequence of events, isdependent on what happened in the preceding events). Also referred to as “availability,” it has been usedto explain distortions in frequency or probability judgments.

Simply put, the recency effect refers to a phenomenon in which recent events carry more weight. Forexample, people tend to buy earthquake insurance immediately after an earthquake; however, as time

passes, fewer people are inclined to purchase protection. This is in direct contrast to scientific reasoning:immediately after an earthquake, one is unlikely to experience another, but the odds increase the moretime has passed since the last one. Similarly, bear markets may push investors to safer alternatives eventhough much of the downside risk to equities may have already passed.

Keep in mind that late in a bear market is perhaps the time when a plan sponsor is most susceptible toholding a lower than “normal” equities exposure. Admittedly, it can be difficult to see beyond the tumult of




late; the recency of market events weighs heavily on investors, but it is critical to maintain a long-termperspective. The most recent bear market, which likely ended in March 2009, was just two months longerthan the post-World War II average of nearly 15 months. It was the most costly of the recent bearmarkets, with a total decline of -56.8% (the 2000-02 decline of -49.1% had previously rated most costly).

For investors that pulled out of the market, or delayed rebalancing their equity ratio (upward), there was

much to be lost. Consider this:• The average price-only return for the first 20 trading days of the latest bull leg was 24.5%

significantly higher than the annual total return of common stocks (9.6%) dating back to 1926.• Missing the first 20 trading days (on average only 2.5 percent of the total trading days for each

bull leg) means missing an average 12.4 percent of the total return for the entire bull leg.

In effect, each of the first 20 trading days of a bull market are, on average, nearly four times moreproductive than each of the remaining 1,100 days (nearly 4.25 years).

Mispricing of Risk

The efficient market hypothesis states that prices on traded assets such as stocks and bonds reflect allknown information; consistently outperforming the market using information that is already known isimpossible. Testing the efficiency of the markets is difficult, however, because a stock is considered to bea perpetual asset—its value depends not just on the present value of its future cash flows but also theprice someone would be willing to pay to acquire that stream of future cash flows. Richard Thaler arguesin The Winner’s Curse: Paradoxes and Anomalies of Economic Life that wagering markets, such ashorseracing, have a better chance of being efficient: these markets have a short time horizon, offerrepeated feedback, and have a well-defined termination point at which potential value becomes certain. 26 It is this type of market that provides an opportunity to examine the mispricing of risk in more detail.

For illustration, we will follow horseracing in our examination of mispriced riskand the wagering market. While there are both simple and complex ways inwhich to place bets (win, place, or show versus daily double, exacta, trifecta,and so on), what is important to know is that payoffs for all winning bets aredetermined in a “pari-mutuel” fashion. Simply put, all bets of a particular typeare placed together in a pool, transaction costs are deducted, and payoff oddsare calculated by sharing the pool among all winning bets.

The free, online encyclopedia Wikipedia provides a basic example of pari-mutuel betting, detailing a hypothetical event with eight possible outcomes,each with a certain amount of money wagered as shown to the left. Inhorseracing, the proportion of money in the “win pool” that is bet on eachhorse can be interpreted as the subjective probability (corresponding to apersonal belief) that each horse will win the race. James Surowiecki, author of

The Wisdom of Crowds , cites four qualities that make the crowd smarter: diversity, decentralization,independence, and a way to summarize people’s opinions into one collective verdict. 27 Horseracing offersa great laboratory to study this “wisdom of crowds”: while a good public handicapper will pick about 30percent of the winners each year, the public picks roughly 33 percent.

The expected payout in horseracing, or any event using pari-mutuel betting, is simply a function of thecollective opinion of the bettors. Returning to our Wikipedia example, the total pool for this event is $514.

Assume outcome #4 is the winner, with $55 wagered. To calculate the payout, transaction costs (in thiscase a commission of 14.25%) are first deducted, leaving $440.76.

OutcomeAmt.

Wagered

1 $30.00

2 $70.00

3 $12.00

4 $55.00

5 $110.00

6 $47.00

7 $150.00

8 $40.00




The amount remaining in the pool after transaction costs is distributed to thosewho wagered on outcome #4. $440.76/$55 = $8.00 per $1.00 wagered. With$7.00 in profit for each $1.00 bet, the odds are considered 7-to-1. Theexpected payout for each outcome is shown to the right. In this type of market,participants ultimately determine the risk level. But what happens whenparticipants misprice the inherent risk of the bet? Steven Crist, publisher andcolumnist for the Daily Racing Form spoke at Legg Mason CapitalManagement’s Thought Leader Forum in September 2007. During his lecture,he recalled the spring of 2004 when Smarty Jones emerged as winner of theKentucky Derby—and thus the only horse eligible to win that year’s TripleCrown. 28 The horse went on to win the Preakness by an astonishing twelvelengths. As Crist recalled, “… the world went absolutely crazy,” and anticipationwas high that Smarty Jones would win the first Triple Crown since 1978.

After taking the first two legs of the 2004 Triple Crown, handicappers considered Smarty Jones between30 and 50 percent likely to win the final leg—the Belmont Stakes. The betting public viewed thingsdifferently, assigning even higher odds of success. More often than not, the ‘wisdom of the crowds’ holdsand bettors tend to correctly value risk at the track. In this case, however, the betting public wasoverwhelmingly biased toward Smarty Jones. Five out of every six dollars wagered were for Smarty

Jones to win, meaning that the public gave the horse an 83 percent chance of winning. Crist called this“one of the most fabulous opportunities in the history of betting.” He said it did not matter which horsemight beat Smarty Jones—and he bet on all the other horses in the race. In the end, Smarty Jones lost toa late-charging long-shot. For those who recognized the opportunity, a lot of money was there for thetaking: when asked what his pay-out was, Crist replied “About a quarter of a year’s salary.”

Surowiecki writes that when a group of people are biased in the same direction, the group is not likely tomake a good decision. 29 Similarly, he notes it is also detrimental when people start paying too muchattention to what others in the group think. The technology bubble of the late 1990s is just one example ofhow a crowd’s judgment can be compromised, just as it was in the Smarty Jones case. Rather thanconsidering what a company was truly worth, many investors speculated on how much other investorsthought the company was worth. More recently, the run-up in housing prices from investor speculation in

the housing market is another example. In each instance, a self-reinforcing feedback loop resulted withrising prices justifying current prices and begetting ever-higher prices. Ultimately, these feedback loopsaccelerate, triggering pricing bubbles. The bubble inevitably pops, resulting in a costly correction in orderto return the markets to a more appropriate valuation point.

OutcomeExpected

Payout

1 $14.69

2 $6.30

3 $36.73

4 $8.00

5 $4.00

6 $9.37

7 $2.94

8 $11.02




Summary of Common Errors in Judgment

JudgmentError

SummaryExplanation

InvestmentImplications

Benefits ofQuantitative Investing

Gambler’s Fallacy

Belief that the outcome of arandom event, in a sequence ofsuch events, is at least partiallydependent upon precedingoutcomes.

Can lead to attempts totime markets; may becompounded byDisposition Effect.

Decisions made based onstock-level valuations(“bottom up”), rather thanon market trends (“topdown”).

Hot-Hand Fallacy

Assumes a person (or series) is“hot,” i.e., that a trend willcontinue; humans perceivepatterns where none may exist.

The axiom “pastperformance is noguarantee of futureresults” is ignored.

Decisions based onquantitative factors,typically uninfluenced byfalse patterns.

Disposition Effect

Reluctance to sell something(e.g. a house, a stock) that hasdeclined in value.

Prices can be artificiallysupported above orsuppressed below fairmarket value.

Momentum factors maybe quantitativelyexploitable in theinvestment process.

Base-Rate Neglect

Failure to properly account forconditional probabilities (e.g.,failure to consider falsepositives).

Failure to consider allrelevant information inmaking investmentdecisions.

Unbiased decisions basedon quantitative factors;potentially fullerassessment of risk.

Loss Aversion

The preference (often strong) foravoiding losses over achievinggains.

Can lead to unwillingnessto sell investments thathave fallen in value.

Sell decisions areunbiased.

Status Quo Bias

Tendency, absent incentives, not

to change established behavior(cf. Newton’s first law of motion).

Selection of a “safe” or

default investment optionthat may be unable tomeet financial objectives.

Can automatically

maintain portfoliodiversification.

Framing Errors

The manner in which choices arephrased (or “framed”) can play asignificant role in the decision-making process.

Response to corporatenews is likely influencedby how the news ispresented or framed.

By relying only on resultsor expected value of, e.g.,a news event, investmentrankings are not directlyaffected by framing.

Recency Effect

Tendency to best remember (orgive more weight to) the mostrecently presented data.

Investors can be moreheavily influenced byrecent market events.

Investment decisionsbased on quantitativerather than chronological

or perceptual inputs.

Mispricing of Risk

The over- or underestimation(often due to errors noted above)of the risk or probability of aparticular situation or outcome;also, to miscalculate the truecosts of possible outcomes.

When a group is biased inthe same direction, orwhen an individual isaffected by “the crowd,”investment decisions maysuffer.

Is intended to minimizespeculative, emotional, orrash buy/sell orders.




II. How the Brain Responds to Risk

It turns out that many of these errors may have a biological basis—our brains undergo changes in theface of uncertain risk and reward. Researchers can study how the brain responds to an estimate of riskand the probability of reward via magnetic resonance imaging (MRI). MRI technology offers a detailedlook inside the body and is especially useful in studying soft tissues, such as muscle and the brain. Whilesociety today may appear quite evolved, the brain seems to excel at the types of skills that likely wouldaid survival in more primitive times—solving short-term trends or eliciting emotional responses. Humansare less efficient when dealing with long-term patterns or multiple factors—areas of interest to investors.

So, what is happening inside our brains and how can the investment community use that information?Before answering that question, we must first examine the areas of the brain involved in decision-makingwhere uncertainty, risk, and reward are concerned.

The amygdala is part of the limbic system—a collection of brain structures that support such functions asemotion, behavior, and long-term memory. The amygdala performs primary roles in the formation andstorage of memories associated with emotional events which are not limited to just threats of physicaldanger, but also include areas such as financial gain and loss. A study led by Jordan Grafman, a

neuroscientist at the National Institute of Health, found that the more frequently research subjects weretold they were losing money, the more active this region of the brain became. 30 In 2001, Breiter et al. found that even the expectation of losses set off bursts of activity inside the amygdala. 31

Further, researchers found that activity inside the amygdala can trigger the release of adrenaline—thehormone most commonly associated with the body’s “fight or flight” response. This primitive response tostress is intended to prompt action in the face of an immediate physical threat. It also serves to solidifymemories of the event—a response that can be quite costly for investors. Panicked selling, for example,can cause investors to leave the stock market prematurely (such as at or near a market low), missing outon the significant returns that come in the early days of a rebounding market. Similarly, Raymond Dolanof England’s University College London has shown that financial losing streaks evoke a strong reaction inthe hippocampus, 32 another portion of the limbic system which plays a major role in short-term memory

and spatial navigation. This region of the brain helps to program our memories of fear and anxiety andmay help to explain why investors are reluctant to return to the markets following a crash, despite therelative cheapness of securities. At this writing, despite a recent number of record or near-record lossesfor the Dow Jones, S&P ®, and other major indexes, many investors still remain hesitant to resume equityinvesting, spurring further losses and making this among the most costly bear markets in history.

Not everything about the amygdala’s response is negative. Antoine Bechara, a neurologist with theUniversity of Iowa, conducted an experiment in which participants played a computer card gameconsisting of four decks of cards and a $2,000 pot. Players gained or lost money with each draw from thedeck. Money Magazine columnist Jason Zweig also participated. He quickly learned that there were lessrisky and more risky decks to draw from. He noted that “in a matter of minutes, my amygdala had alreadycreated an emotional memory that made my body tingle with apprehension at the very thought of taking a

big risk.” Among the findings of the study: Bechara noted that people with certain types of damage to theamygdala never learned to avoid picking cards from the riskier decks. Instead, their prefrontal cortexes,the thinking part of the brain, allowed them to sample from all decks until their money was gone. 33

The prefrontal cortex serves to carry out the executive functions of the brain. This is the area that controlsand manages other cognitive processes such as planning, abstract thinking, cognitive flexibility, ruleacquisition, and “social control” (social mechanisms that regulate individual and group behaviors). Thisarea also has the ability to store memories, draw conclusions, forecast consequences, and compare




current and past experiences. Among other things, this region is extremely important to the making ofsound financial judgments. In a study of Vietnam veterans with cortex damage, Grafman found thatresearch subjects were better able to focus on short-term financial goals than were people with normalbrain function, but less able to plan for long-term goals, such as college education for their children oreven their own retirement. 34 Bechara noted that many forms of dementia (a progressive decline incognitive function) can begin in the prefrontal cortex, 35 perhaps helping explain why the elderly are often

susceptible to financial scams. Damage to this region impairs the ability to recognize futureconsequences of financial decisions.

Other structures that play important roles in financial decision-making include the nucleus accumbensand anterior cingulate. The nucleus accumbens is thought to be significantly involved with reward,laughter, pleasure, addiction, and fear, while the anterior cingulate is involved with autonomic functions(e.g., heart rate, breathing), as well as rational cognitive functions, such as reward anticipation anddecision-making. These two areas specialize in pattern recognition—something considered to have beenparticularly important for our early ancestors but is deemed less important in today’s environment.Unfortunately, evolution has programmed our brains to look for patterns, even in places where truepatterns do not exist. Scott Huettel, a neuroscientist at Duke University, found that the anterior cingulateanticipates another repetition after a stimulus occurs successively only twice. 36 If that repeating pattern is

broken (think winning streaks and gambler’s fallacy), neurons in the insula, candate, and putamen—areasof the inner brain—can generate feelings of fear and anxiety. Furthermore, the longer the pattern hadrepeated previously, the more violently the brain responds when the pattern is finally broken.

What could this mean in terms of financial investing? With the prevalence of financial information ontelevision, in print, and on the web, even lay investors are keenly aware of both how a company’searnings look and how Wall Street analysts view each company. Two consecutive quarters of increasingearnings—or even simply meeting analyst expectations—is enough, in this context, to establish a pattern.Irene Kim, while at the University of Michigan, found that the more times a company beat analystexpectations, the further its share price dropped when earnings finally fell short. 37 A shortfall after threeconsecutive positive quarters led to a three percent pullback in price. Extend that outperformance to eightquarters and share price fell an average of eight percent. The longer a firm matched the expected

performance pattern, the greater the value lost when that pattern was finally broken.

In their 2008 article “Neural Antecedents of the Endowment Effect,” Knutson, Wimmer, Rick, Hollon,Prelec, and Loewenstein demonstrate that activation in the anterior insula predicts the strength of theendowment effect discussed earlier. 38 This area of the brain activates when someone is afraid of losingsomething, is in physical (or imagined) pain, or is experiencing something that person finds disgusting. Ifthe idea of giving something up (one’s home, for instance) is considered painful, the brain assigns ahigher value to it in order to avoid the pain of loss. While researchers have studied the role of emotionand anticipation in decision-making, little work had been done to study the influence of anticipatory neuralactivity (both the nucleus accumbens and the anterior insula) on financial risk-taking.

Kuhnen and Knutson (2005) designed tasks expected to elicit a range of investment behaviors, including

risk-seeking and risk-averse choices.39

Their study found that anticipatory neural activation may promoteirrational choice, which may help explain why investors have a difficult time selling a stock that is losingmoney or facing trouble. The 2001 collapse of Enron is one example of such a phenomenon. Harry Griffinwrote in his 2006 article “Did Investor Sentiment Foretell the Fall of Enron?” that an examination of optionopen interest (a measure of liquidity and, hence, fear) from January 2000 through December 2001showed that the market signaled the potential for loss a year ahead of the actual crash. 40 Yet despite this,investors remained committed to the firm’s stock, which after a year of steadily falling value, becamevirtually worthless as the company filed the largest corporate bankruptcy in U.S. history up to that time.




III. Can Quantitative Investing Overcome Flawed Risk Assessment and Biology?

In the last several years, a number of studies have focused on the emotional regions of the brain andtheir link to costly behaviors for investors. Studies by Lo and Repin (2002), Sanfey, Rilling, et al . (2003),McClure, Laibson et al. (2004) and Camerer, Loewenstein et al. (2005) could lead investors to concludethat if emotional involvement in investing is destructive, then non-emotional processes are more likely tolead to better performance in the long run. Indeed, if ever there were an environment likely to trigger anemotional and potentially devastating response, the market from late 2008 to early 2009 has surely beenit: continued fallout from the sub-prime mortgage meltdown has affected the global financial system;global debt markets suffered near-paralysis as financial institutions’ efforts to raise and hoard cash sentcredit costs skyrocketing; world markets have been among the most volatile on record. Countlessinvestors have retreated from the markets as widespread panic and selling has become commonplace.

So, just how can investors protect themselves from knee-jerk reactions in these historic times? Simplyput: a disciplined, dispassionate investment approach may well be in investors best interests.

Generally speaking, a disciplined investment process can avoid many of the pitfalls in risk assessmentand a human’s biological responses to risk. Not surprisingly, people have a tendency to become

overconfident in their own abilities, particularly in areas in which they have some knowledge. However,research has shown that increased levels of confidence show no correlation with greater success.Investment managers are not exempt: while they are consistently overconfident in their ability tooutperform the market, most fail to consistently do so. Baumeister (2003) found that under emotionaldistress, people shift toward high-risk, high-payoff options, even if those options are poor choices. 41 Oneplace this type of behavior is often evident is the race track. Bettors often view the last race of the day asan opportunity to recoup any losses incurred over the course of the day, leaving them more likely tounderbet the race favorite or, more importantly, overbet horses with odds that would leave a bettor aheadfor the day.

In their 1981 article “Efficiency of the Market for Racetrack Betting”, Hausch, Ziemba, and Rubinsteinfound that people paid too little for favorites and too much for long shots. 42 They also noted that the more

money a bettor had lost, the more likely was that bettor to wager on horses with longer odds. Afterexamining nearly six million races over a roughly 10-year period of time, Snowberg and Wolfers showedin their 2007 paper “Explaining the Favorite-Longshot Bias: Is it Risk-Love or Misperceptions?” that whilethe rate of return per dollar bet in the last race of the day is somewhat lower than for all races, it is notstatistically significant. The high-risk, high-payoff option has the lowest rate of return. 43

Now, consider this phenomenon in investment management. A manager that has been experiencing aperiod of underperformance likely feels increasingly pressured to produce results. In order to make up theperformance deficit, that manager may assume a higher level of risk than was initially agreed upon,leaving the investor more vulnerable to even greater underperformance. Baumeister (2003) also foundthat when self-esteem is threatened, people lose their capacity to self-regulate: attempts to provethemselves tend to override rational thinking. Research showed that when self-regulation fails, people

may become self-defeating in ways such as taking immediate pleasure rather than a delayed reward.

One of the tenets of quantitative investing is what is often referred to as a “dispassionate approach.” Aswe have discussed, individual investors—and even money managers—can fall victim to emotion and thedisposition effect. In essence, investors hold on to a stock too long despite the presence of sell signalssimply because they have invested a great deal of time and energy into that stock, are overconfident intheir abilities, or are reluctant to admit an investment decision was a poor one. Perhaps because of these






IV. Quantitative Investing at OFI Institutional – One Manager’s Experience

OFI Institutional’s quantitative investment style is based on roughly 30 years of performance data and acomprehensive research library of more than 80 valuation factors. Using a variety of factors to build asector-specific composite for each market sector, our Quantitative Equity team calculates a rank for eachstock in a particular sector relative to all other stocks in the same sector. Looking at the InformationTechnology sector, for example, the composite emphasizes factors that indicate sustainable earningsgrowth in the near to intermediate term. In contrast, the Utilities sector composite emphasizes value-oriented factors such as Price-to-Cashflow. This does not mean that valuation is not considered in theTechnology sector or that earnings growth sustainability is not considered in Utilities. We do considerthose factors, but, in trying to prospectively determine the relative winners and losers in each sector, ourresearch has shown that emphasizing different types of factors in different sectors leads to a morepowerful composite ranking.

Generally speaking, we know that quantitative managers have a dispassionate approach to investing: thatcomputer models and solid facts drive investment decisions. Investment decisions are the result of strongbalance sheets, not simply because a stock is “due” to have a strong run. We have seen evidence inrecent periods that investors have been flocking toward the most downtrodden names (the gambler’s

fallacy). Investors seemingly believe that these beleaguered stocks simply have nowhere to go but up. Inthe first quarter of 2009 for example, American Capital, Ltd. lost more than 80% of its share value prior tothe March 9 low. With an average daily volume below seven million shares, trading surged nearly seven-fold in early March when the stock was trading well below $1.00/share. From the market bottom throughthe first quarter of 2009, the stock gained nearly 200%. While impressive on the surface, stocks such as

American Capital, Ltd. are riddled with quality issues.

Many of these large gainers, in both the large- and small-cap segments, carry a heavy debt burden orface debt covenant issues moving forward. With an expectation that some firms will renegotiate theirdebt, leading to higher interest expenses, it is critical to identify firms that will continue to struggle in thelong-term. OFII’s Interest Coverage factor is just one screening tool that may help to identify thesetroubled securities: the factor measures a company’s ability to meet its interest payment obligations by

evaluating forecasted annual cash flow in the coming year divided by their annual interest expense overthe trailing year. The factor prefers stocks with higher ratios over stocks with lower ratios. OFII’s Debt-to-Equity Trend factor also evaluates a firm’s debt levels, calculating a regression of the trailing eightquarters debt to equity ratio. The trend is important here: a firm with a rising trend will score lower, even ifits actual Debt-to-Equity is considered low, as rising debt levels may signal problems later on.

As you will recall, the disposition effect deals with the propensity to sell a stock that has appreciated invalue rather than selling one that has declined in value and provides a rationale for why momentumfactors tend to be an effective tool in making investment decisions. If investors are inclined to sell theirwinning stocks, without regard to earnings or other fundamental considerations, their selling acts as adownward bias to the stock’s price. Thus, absent this emotion-driven selling, the stock’s price would behigher than the current market price.

Conversely, if investors are reluctant to sell losing stocks, again without regard to earnings or otherfundamentals, then their decision to hold the stock acts as an upward bias to the stock’s price. In otherwords, absent the disposition effect, more investors would willingly sell losing stocks, pushing the stockprice below market price. So, whether a stock is rising or falling in price, a strategy of following themomentum (buy the winners and sell the losers) is justified due to the disposition effect.




The OFII factor library offers a number of tools that evaluate momentum—Earnings-to-Price Momentum,Earnings Momentum, Internal-Growth Momentum, Long-Terms Earnings-Yield Momentum, PriceMomentum (over both a nine- and twelve-month horizon), Return-on-Equity Momentum, RevenueMomentum, and Yield Momentum. Looking more closely at Price Momentum, as an example of OFII’smomentum-measuring tools, our team is able to utilize this factor to measure relative strength orweakness within one economic sector. The idea behind this factor is that a stock’s prospects are not

completely defined by a company’s balance sheet and income statement fundamentals. A stock’s pricetrend may capture important non-quantifiable or qualitative factors such as corporate restructuring,management changes, pending patent or drug approvals, investor’s behavioral considerations, etc.Currently, we run two versions of Price Momentum: one is based on a company’s total return over theprior twelve months (lagged one month because of the short-term price reversal phenomenon); the otheruses a slightly shorter, nine-month horizon.

Earlier, we focused on the recency effect: that given a list of things to remember, we are most likely torecall the last few things rather than those at the beginning or middle of the list. From an investingperspective, individuals are most likely to recall the most recent market returns. In bear markets, this oftendrives investors to a lower-than-optimal allocation to equities. For a quantitative manager, stock selectionis model-driven, without the influence of human emotion. A quantitative manager will not act impulsively,

even under conditions such as we have been experiencing over much of 2008-2009. Instead, bymaintaining a full allocation to equities, each client will be in a position to benefit fully from a new bull leg.




V. Conclusions

Ongoing research and new techniques continue to uncover the particular strengths—and weaknesses—of the human brain. Driven to make sense of their environments, humans appear well wired to make snap

judgments in potentially life-threatening situations. But our synaptic shortcuts may leave us less able todeliberately assess the full array of information surrounding us; our biological programming may cause usto miss the forest for the trees. Overconfidence, loss aversion, false perceptions, and the like may allconspire to distort our judgment when it comes to long-term investment planning. In this brief paper, wediscussed some of the most common errors in judgment, their causes and effects, and how a quantitativeinvestment process may mitigate some of the more pernicious errors.

Humans look for patterns, rely on trends, even when it comes to investing. Quantitative managers relysimply on the available raw fundamental data for each security in their investable universe. That data cancome from any number of sources—including (but not limited to) Thomson/Baseline, Thomson/First Call,Gradient/Lancer, Compustat, and FactSet—and is used as inputs to proprietary investment processes.Financial models attempt to account for valuation, growth, risk, and management quality. Generallyspeaking, quantitative investment processes have less chance of falling victim to human errors in

judgment; they do not recognize trends such as the hot-hand, nor do they fall for the gambler’s fallacy.

They do not allow a portfolio to continue to hold a security simply because it has fallen in value(disposition effect). The negative effects of human emotion should have no place in a rigorous investmentprocess; quantitative processes offer a disciplined and dispassionate approach and an ability to controlrisk levels that, in the long run, may be just what is needed for long-term success.

Internally, quant managers such as OFI Institutional employ teams of skilled research analysts to screendata on a daily basis. Company data extracted from SEC filings, the ultimate source for outfits such asBaseline and Compustat, are dependent on the quality of the company’s internal procedures and auditreview. The OFI Institutional research team reviews not just the earnings estimates supplied by external(and internal) stock analysts, but the method by which those estimates were calculated.

Is there a way to avoid human errors in risk assessment, or are we all destined to fall into cognitive traps?

We contend that it is imperative to think through investment decisions rather than simply reacting—perhaps over-reacting—to perceived immediacies. The current economic crisis challenges us like noother time in recent history: Investor reaction to dire news regarding corporate earnings, unemployment,housing data, and so forth has come swiftly, and market volatility has been unprecedented, on the upsideas well as the down. A sound, controlled, long-term investment plan remains vital; asset allocationcontinues to play a crucial role in overall performance for any plan sponsor. As part of that allocationprocess, we believe a quantitative investment approach can eliminate much of the risk of emotionalreaction to current economic news. When investment decisions are based on a firm’s underlyingfundamentals rather than impulsiveness or crowd behavior, the potential for long-term stability andsuccess, we believe, is increased.




Notes

1,3 Judgment under Uncertainty: Heuristics and Biases, Amos Tversky; Daniel Kahneman, Science, New Series, Vol. 185, No.4157. (Sept. 27, 1974), pp. 1124-1131

2, 4 Biases in casino betting: The hot hand and the gambler’s fallacy, James Sundali (Managerial Sciences, University of Nevada,Reno), Rachel Croson (Operations and Information Management, Wharton School, U of Penn), Judgment and Decision Making, vol

1, no. 1, July 2006, pp. 1-12

5 Hersh Shefrin, Behavioral Finance: Biases, Mean-Variance Returns, and Risk Premiums, CFA Institute Conference ProceedingsQuarterly, June 2007, Vol. 24, No. 2:4-12, (doi 10.24698/cp.v24.n2.4700)

6 Thomas Gilovich (Cornell University) and Robert Vallone and Amos Tversky (Stanford University), The Hot Hand in Basketball: Onthe Misperception of Random Sequences, Cognitive Psychology 17, pp. 295-314 (1985)

7 Rachel Croson, James Sundali, The Gambler's Fallacy and the Hot Hand: Empirical Data from Casinos, Journal of Risk andUncertainty, Springer, vol. 30(3), pp. 195-209, May 2005

8 Nicholas Barberis and Wei Xiong, What Drives the Disposition Effect? An Analysis of a Long-Standing Preference-BasedExplanation, The Journal of Finance, Vol. LXIV, No. 2, April 2009

9 Meir Statman and Hersh Shefrin, The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence,

The Journal of Finance, Vol. XL, No. 3, July 1985

10 Amos Tversky and Daniel Kahneman, Prospect Theory: An Analysis of Decision Under Risk, Econometrica, 47(2), pp. 263-291,March 1979

11 Terrance Odean, Are Investors Reluctant to Realize Their Losses, The Journal of Finance, Vol., LIII, No. 5, October 1998

12 Mark Elliott, The Base Rate Fallacy, May 1996, http://users.ecs.soton.ac.uk/harnad/Hypermail/Explaining.Mind96/0220.html

13 Leonard Mlodinow, The Drunkard’s Walk: How Randomness Rules Our Lives, Pantheon Books, a division of Random House, Inc2008

14 Amos Tversky and Daniel Kahneman, Loss Aversion in Riskless Choice: A Reference-Dependent Model, Quarterly Journal ofEconomics, Vol. 106, No. 4 (Nov. 1991), pp. 1039-1061

15 Richard H. Thaler, The Winner’s Curse: Paradoxes and Anomalies of Economic Life, Princeton University Press, 1992

16 Amos Tversky and Daniel Kahneman, Prospect Theory: An Analysis of Decision Under Risk, Econometrica, 47(2), pp. 263-291,March 1979

17 Kahneman, D., Knetsch, J., & Thaler, R. (1990). Experimental Test of the Endowment Effect and the Coase Theorem. Journal ofPolitical Economy 98(6), pp. 1325-1348.

18 William Samuelson and Richard Zeckhauser, “Status Quo Bias in Decision Making,” Journal of Risk and Uncertainty, Springer,vol. 191, pp. 7-59, March 1988.

19 Hersh Shefrin on NPR, March 30, 2008

20 Testimony of Edward Ferrigno, The Drop in Retirement Savings: The Challenges Small Businesses Face Funding andMaintaining Retirement Plans in a Struggling Economy, House Committee on Small Business, February 25, 2009

21 Dana M. Muir, Professor of Business Law, University of Michigan, Ross School of Business, The Proliferation of IndividualPension Account Options-A Challenge for Institutional Oversight (ppt. presentation)

22, 24 Richard Conniff, MSN Money, For Investors, Excitement is an Enemy, Published Jan. 25, 2008http://articles.moneycentral.msn.com/Investing/StockInvestingTrading/ForInvestorsExcitementIsAnEnemy.aspx#pageTopAchor

23 Massimo Piattelli-Palmarini, Inevitable Illusions: How Mistakes of Reason Rule Our Minds, John Wiley & Sons, Inc., 1994




25 Definition of recency effect, http://changingminds.org/explanations/theories/recency_effect.htm

26 Richard H. Thaler, The Winner’s Curse: Paradoxes and Anomalies of Economic Life, Princeton University Press, 1992

27, 29 James Surowiecki, The Wisdom of Crowds, Random House, Inc., 2004

28 Steven Crist, Publisher and Columnist, Daily Racing Form, Transcript – Thought Leader Forum, Legg Mason CapitalManagement, September 26-27, 2007 – Baltimore

30, 33, 34 Jason Zweig, “Are You Wired for Wealth?” Money, pp. 74-83, October 2002. 31 Hans C. Breiter, Itzhak Aharon, Daniel Kahneman, Anders Dale and Peter Shizgal, Functional Imaging of Neural Responses toExpectancy and Experience of Monetary Gains and Losses, Neuron, Volume 30, Issue 2, May 2001, pp. 619-639.

32 Brain tour: Your hot spot. Part 1: The amygdala is the place where fear and anger dwell, Jason Zweig, MONEY Magazine StaffWriter, On CNN Money (http://money.cnn.com/2002/09/16/pf/investing/agenda_brain2/index.htm), September 27, 2002

35 Brain tour: Managing fear. Part 2: Your brain’s prefrontal cortex helps you monitor and manage your brain’s responses.Jason Zweig, MONEY Magazine Staff Writer, On CNN Money(http://money.cnn.com/2002/09/16/pf/investing/agenda_brain2/index.htm), September 27, 2002

36, 37 Brain tour: Looking for patterns. Part 3: Think you see a pattern? Maybe. Or maybe your brain’s just doing what brainsalways do. Jason Zweig, MONEY Magazine Staff Writer, On CNN Money(http://money.cnn.com/2002/09/16/pf/investing/agenda_brain2/index.htm). September 27, 2002

38 Neural Antecedents of the Endowment Effect, Brian Knutson, G. Elliott Wimmer, Scott Rick, Nick G. Hollon, Drazen Prelec, andGeorge Loewenstein, Neuron 58, pp. 814-822, June 12, 2008

39 The Neural Basis of Financial Risk Taking, Camelia M. Kuhnen and Brian Knutson, Neuron, Vol. 47, pp. 763-770, September 1,2005

40 Did Investor Sentiment Foretell the Fall of Enron?, Harry F. Griffin, The Journal of Behavioral Finance, volume 7, number 3, 2006

41 Baumeister (2003), The psychology of irrationality: Why people make foolish, self-defeating choices, in Crocas and Carrillo: ThePsychology of Economic Decision Volume 1: Rationality and Well-Being

42 Efficiency of the Market for Racetrack Betting, Donald B. Hausch, William T. Ziemba, Mark Rubinstein, Management ScienceVol. 27, No. 12, December 1981, pp. 1435-1452

43 Explaining the Favorite-Longshot Bias: Is it Risk-Love or Misperceptions?, Erik Snowberg, Justin Wolfers, 2007http://cbdr.cmu.edu/seminar/Wolfers.pdf

44 How quants stack up against fundaments, Karl C. Mergenthaler, Pensions & Investments, January 7, 2008http://www.pionline.com/article/20080107/PRINTSUB/811313000



Bi l gi l B i f E i Ri k A t

Disclosures

OFI Institutional Asset Management ("OFI Institutional") is the marketing name used by each of the members of the OFI InstitutionalGroup that consists of institutional investment advisers affiliated with OFI. The OFI Institutional Group includes the following SECregistered investment advisers: OFI Institutional Asset Management, Inc. and Trinity Investment Management Corporation.Professionals that provide portfolio management, analysis, trading and other services for OFI Institutional may be employed bymembers of the OFI Institutional Group, OFI or an affiliate of OFI.

OFII Research refers to a proprietary research database developed and maintained by OFI Institutional. For each year since 1968,the 3,000 U.S. equities with the largest market capitalization as of June 30 are included in the OFII 3000, which is categorized bysize and style. The size categories are: large-cap (largest 800), mid-cap (next 200), small-cap (bottom 2,000), and Institutional 1000(largest 1,000). Style categories are based on P/E, price/book, and projected growth rates, with 50 percent of the full OFII 3000universe being assigned to value and 50 percent to growth.

S&P 500 ® and S&P ® are trademarks of the McGraw-Hill Companies, Inc. The indexes are broad-based measures of domestic stockperformance that include the reinvestment of dividends. The indexes are unmanaged and cannot be purchased directly by investors.Index performance is shown for illustrative purposes only and does not predict or depict the performance of a particular product.

Past performance does not guarantee future results.

For Institutional Use Only. This material has been prepared for institutional investors only. It has not been filed with the NASD andmay not be reproduced, shown, quoted to, or used with members of the public.

©2009 OFI Institutional Asset Management. All rights reserved.

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Documents

Biological Basis for Errors in Risk Assessment