Secondary School inEast Harlem. Most of the students at Life Sciences are low achieving.Dweck divided eighth graders into two groups and entered each group intoan eight-session workshop. One group was taught study skills, and the sec-ond was taught both study skills and a module about how intelligence isnot innate. The second group saw slides of the brain and learned abouthow the brain grows new neurons when challenged.
The results for the students in the second group were surprising. Theirstudy habits and grades improved, and their math scores ended their longdecline. “The only difference between the control group and the test groupwas two lessons, a total of 50 minutes spent teaching not math but a sin-gle idea: that the brain is a muscle. Giving it a harder workout makes yousmarter. That alone improved their math scores.”25
Optimal goals should remain internal and in progress. Knowing thatone can improve leads to higher achievement. A focus on achievementsthemselves leads to lower returns. It’s a sort of positive placebo effect. Ifone understands that it takes time, and they have hope that their perfor-mance can improve, then by exercising that bulky mental muscle, greatersuccess will come. The beauty of the brain is its ability to learn and adaptand grow in response to any challenge. Only through humility, courage, cu-riousity, and exertion can unconscious biases be managed and mad mar-kets channeled into money ﬂow.
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C H A P T E R 23
BehavioralFinanceInvestingPlaying the Players
“The principles of successful stock speculation arebased on the supposition that people will continuein the future to make the mistakes that they havemade in the past.”
—Thomas F. Woodlock1
T he majority of this book is concerned with brain processes that bias
individual investment behavior. However, when individuals’ biasesalign, a diversity breakdown occurs, and market prices can suddenlybecome predictable. This chapter describes market patterns generated bycollectively biased investors.
My interest in this area arose while working as a quantitative analyst.From 1995 to 1997 I designed neural network-based stock prediction soft-ware. My software had a slight advantage over random prediction, but itrapidly lost its advantage over time. Because quantitative prediction sys-tems are easily replicable by others, leading to decreased proﬁtability overtime, I decided to model investor behavior. I assumed that mathematicalarbitrage would have difﬁculty eliminating powerful, biologically driven,market price patterns.
As I researched behavioral ﬁnance, it became apparent that one canimprove investment proﬁtability using psychology-informed strategies.Fortunately, these strategies are quantiﬁable, so they can be tested andstatistically conﬁrmed. In my research I found that most outperformingbehavioral strategies are long term and occasionally suffer large draw-downs. Their outperformance is not enormous, but in a world where
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most fund managers underperform the markets year after year, behavioralconsiderations provide a crucial key to beating one’s benchmark.
None of the strategies described here are “bullet-proof,” and since theyare all publicly known, there is a good chance that they will gradually dis-appear from the markets over time (perhaps not for a long time). In fact,many of the investing strategies that were originally considered “behav-ioral,” such as value and momentum investing, are now part of mainstreamﬁnance. First, this chapter examines how one pro, Jim Leitner, uses otherinvestors’ biased risk perceptions to his advantage. Later in this chapter,we’ll examine the outperformance of the value versus glamour effect, mo-mentum investing, and other psychology-based investment opportunities.
HARVESTING RISK PREMIA
Fear can best be translated into ﬁnancial language as “risk perceptions.”When risk perceptions are high, so is the risk premium. Risk perceptionsoften deviate from actual risk due to the biases described earlier in thisbook: emotion (fear leads to overestimation of low probability catastro-phes), time discounting (seeing an immediate danger), herding (investorsobserve each other for cues), uncertainty and mistrust (the veracity ofgovernment-published statistics is ambiguous), and loss framing (defaultis seen as a risk, not an opportunity). Over time, strategies that arbitragerisk perceptions can do very well.
Falcon Management principal Jim Leitner, proﬁled in Chapter 15,noted that in currencies, differences in risk perception are often reﬂectedin interest rate yields. Currencies that are at risk of devaluation typicallyhave higher-yielding bonds. Investors perceive risk of devaluation if theybelieve that the government may: (1) default on debt payments (bondyields); (2) signiﬁcantly increase its money supply, which sparks inﬂation;or (3) devalue its currency via other means, such as deﬁcit spending. Bondinterest rates will rise for debt denominated in currencies that have a highperceived risk of devaluation. The risk premium is a dynamic function ofinvestors’ fear and myopia.
The Brazilian government was believed to be on the verge of defaultingon its debt, denominated in the Brazilian real, in 2002. In the years follow-ing its narrow avoidance of default, investors continued to perceive a highrisk of devaluation in the Brazilian real due to representativeness. Brazilianbonds commanded relatively high interest rates, which were necessary inorder to attract investors.
However, risk perception does not always reﬂect the actual risk. In-vestors who bet that interest rates would drop in a country perceived as
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ﬁnancially unstable, such as Brazil, and rise in a ﬁnancially stable countrysuch as the United States, could have proﬁted as the interest rates partiallymoved toward convergence over the subsequent years.
Leitner himself carries such trades, exploiting the differences in riskpremia between high-yielding and low-yielding currencies. These tradesmust be held for years: “At times it doesn’t work, but over many, manyyears it does.” Leitner is taking advantage of other investors’ biased riskperceptions.
Option premiums contain an estimate of future price volatility—inshort, a measure of investors’ risk perceptions. Leitner has found that in-vestors’ fear of potential losses leads to option mispricing: “Short-datedvolatility is too high because of an insurance premium component in short-dated options.” That is, investors become excessively worried about theshort-term price performance of their investments, and thus they are will-ing to pay too much for the portfolio “insurance” provided by options. An-other investor who proﬁts from this effect, Richard Friesen, was proﬁledin Chapter 17.
A technique for taking advantage of both investors’ overconﬁdence(speciﬁcally, miscalibration) and fearful myopia involves Leitner’s obser-vation that “longer-dated options are priced expensively versus future dailyvolatility, but cheaply versus the drift in the future spot price.”2 Most peo-ple cannot accurately estimate their conﬁdence interval of future marketprice trends. As noted in Chapter 8, only 39 percent of CFOs correctly es-timated the one-year stock market close in their 80 percent conﬁdence in-terval. Investors are afraid of short-term volatility in their holdings, thusbidding prices for option insurance too high. But, over the long term, theyare unable to foresee the likelihood that the option price will trend to an-other level based on their intrinsic miscalibration. That is, when lookingout longer term, investors are overconﬁdent in their ability to estimate fu-ture price ranges. When buying options for the short term, they are exces-sively myopic and fearful.
RISK PREMIA AND EXPECTATIONS
The 2001 book Expectations Investing by Michael Mauboussin andBernard Rappaport, delineates a strategy for taking advantage of myopia ininvestors’ expectations. The authors argue that the disparity between long-term corporate cash ﬂow trends and investors’ short-term cash ﬂow expec-tations, as reﬂected in current share price, can be exploited as a source ofalpha. The authors assert that in order to generate a positive alpha, onemust develop superior forecasts of changes in others’ expectations.
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The Economist magazine alluded to the importance of expectationsin driving prices while Brazil threatened debt default in the summer of2002. Brazil was faced with increasing interest rates due to a contagion ofrisk perception after a meltdown in the ﬁnances of its neighbor, Argentina.Though Brazil was ﬁnancially stable at current interest rates, a signiﬁcantrise in rates and drop in the value of its currency, the real, would have cre-ated conditions in which Brazil was unable to meet debt payment obliga-tions. Understanding this risk, speculators and risk-averse investors beganto sell bonds. Their selling pressure gradually pushed interest rates higher.Interest rates were on the verge of surpassing Brazil’s ability to pay, andthe risk of a debt default suddenly became real.
Whether Brazil would default hinged on the conﬁdence of bond in-vestors. If the mass of investors expected default, then their bond sellingwould drive interest rates so high that the government would have beenunable to pay its interest obligations. If investors regained conﬁdence inthe Brazilian government’s ability to pay, then rates would drop, and de-fault would be averted. Default hinged on the risk perceptions, and ex-pectations, of market participants. The Economist magazine described theconundrum in terms of complexity theory and expectations:
More recently, a theory of “multiple equilibria” has gained support,in which there is more than one probable outcome, with market ex-pectations usually the deciding factor. Brazil’s total public debt, orits foreign debt, both public and private, could either be sustainableor could become unmanageably large, depending on what the mar-kets henceforth expect.3
Brazil ultimately did not default on its debt obligations. Soothing wordsfrom the world’s deep-pocketed bankers rapidly restored conﬁdence anddiminished risk perceptions.
In other cases, investors’ risk perceptions are too sanguine, even whenrisks are communicated clearly in ofﬁcial company reports. In a fascinat-ing study, Professor Li Feng at the University of Michigan counted the fre-quencies of the words risk, risks, risky, uncertainty, uncertain, and un-certainties in 10-K Securities and Exchange Commission (SEC) ﬁlings forover 34,000 ﬁrm-years of annual corporate reports. He found that compa-nies with large increases in risk-related word frequency underperformedcompanies with slight increases by a magnitude of 10 percent annually.Feng concludes, “I ﬁnd that a stronger emphasis on risk in the annual re-port is associated with lower future earnings. The risk sentiment of annualreports can also predict future returns.”4 It appears, in this context, thateven when companies explicitly remark upon their stock risk, investors
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underreact. Perhaps investors are focusing on the wrong information whendeveloping expectations.
VALUE VERSUS GLAMOUR
One of the ﬁrst identiﬁed market anomalies is the long-term outperfor-mance of “cheap” value stocks relative to more expensive “glamour”stocks. Because of their lengthy use and outperformance, value strategieshave been incorporated into modern ﬁnancial education. Warren Buffett isone notable value-investing practitioner. Value stocks’ shares sell inexpen-sively relative to the actual, underlying worth of their physical assets (suchas buildings, factories, equiptment, patents, brand, or market penetration),their projected earnings growth, or their cash ﬂow potential. Three widelyused ﬁnancial ratios for measuring a stock’s value are: (1) the price-to-bookratio, (2) the price-to-earnings ratio, and (3) the price-to-cash ﬂow ratio.
The price-to-book ratio measures the ratio of current share price toasset value. When the ratio is low, then a stock is relatively inexpensive,and when it is below 1, then a stock is selling for less than the value of allits assets. The price to earnings (P/E) ratio is another measure of value.When a company’s current earnings are high relative to the share price,then the P/E ratio is low and the stock is relatively cheap. Subpar pricinghappens for some companies, particularly if earnings expectations are lowor its business is out of favor.
Value-investing strategies were described in the early twentieth cen-tury by Benjamin Graham, and in the latter half they were promoted byDavid Dreman. Researchers who investigated value strategies found thatthey outperformed signiﬁcantly. In 1992, Professors Fama and French an-alyzed returns of U.S. stocks from 1963 to 1990.5 They divided stocks into10 tiers based on their price-to-book ratio. The average return of thosestocks with the lowest price-to-book ratio (“value” stocks) was 1.53 per-cent per month higher than the average return on the highest price-to-bookratio tier (“glamour” stocks). When they analyzed stocks by P/E ratio, theyfound a 0.68 percent per month outperformance by the lowest P/E stocks(value) versus the highest P/E stocks (glamour). Value outperformance hasbeen found in stock markets around the world.6
Using Factset software, in February 2005 I performed an analysis ofvalue stocks that had both low price-to-book and low P/E ratios. I rank or-dered all United States stocks over $250 million in market capitalizationaccording to each ratio independently. Then I summed their two indepen-dent rankings and reranked them. The subsequent ranked list was dividedinto hundredths. Each year for the prior 15 years (1990–2004) the list was
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reordered on January 1 and then run forward one year. The average returnsof each hundredth over the past 15 years are displayed in Figure 23.1. Youcan see that the annual returns of the top hundredths of inexpensive valuestocks (far left) was almost twice as high as the top hundredths of “glam-our” stocks (far right). The bar to the furthest right is the performance ofdelisted stocks.
MOMENTUM, SIZE, AND THEOPTIMAL PORTFOLIO
While value stocks tend to do well over the long term, ﬁnding the bestshort-term winners requires a different skill set. Researchers found thatstocks whose prices rose over the prior six months tended to continueoutperforming the market over the next two years. Momentum strategiesexploit the “price momentum” that carries rising stocks even higher.
Professors Jegadeesh and Titman identiﬁed the momentum anomalyby ranking stocks into 10 tiers according to their past performance.6 AllU.S. stocks with available data were grouped into prior six month returnsover the period 1963 to 1989. The researchers then computed the averagereturns of each tier over the next six months. The biggest prior winnersoutperformed the biggest prior losers by an average of 10 percent on anannual basis. In a later study, researchers demonstrated that the winnersoutperform the losers up to three years into the future.7
Other modiﬁcations of the momentum strategy have been examined.Professors Lee and Swaminathan report on a volume and momentuminvestment strategy in their 1999 paper “Price Momentum and TradingVolume.”8
Their results depict the returns for three momentum strategies thatconsider prior trading volume. The early-stage momentum strategy buyslow volume winners and sells high volume losers. The late-stage momen-tum strategy buys high-volume winners and sells low-volume losers. Allstrategies outperform for one year. The early-stage momentum strategysigniﬁcantly outperforms the late strategy and the market for four years(35 percent outperformance). The late strategy begins to underperform themarket after one year. The lesson is that low-volume winners are mostlikely to continue performing well. High-volume losers are likely to con-tinue downward.
In the short term, unsupported momentum reverses rapidly. Whenprices jump without being correlated with a news event, then they tendto reverse direction over the following month.9 In the case of unfoundedprice rises, there is no price momentum.
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Momentum strategies can theoretically outperform, but they requirehigher turnover (shorter holding periods) and thus higher transactioncosts. Furthermore, momentum strategies have been faltering in 2006. Onelarge Wall Street bank closed its momentum trading desks in mid-2006 as aresult of poor performance.
In an interesting reversal of the momentum effect over longer time pe-riods, three- to ﬁve-year stock underperformers will, on average, reversetrend over the subsequent three to ﬁve years. Professor Richard Thalermeasured the returns of all stocks traded on the New York Stock Exchange(NYSE) every three years from 1926 to 1982.10 A “winner” portfolio of the35 stocks with the best performance and a “loser” portfolio of the 35 worst-performing stocks was formed. Over the following three years, the averageannual return of the loser portfolio was about 8 percent per year greaterthan the winner portfolio.
On a different note, small stocks tend to do better than large ones overtime. Stock returns every year from 1963 to 1990 were examined for re-turns relative to the market capitalization of the companies. Stocks on theNYSE, American Exchange (Amex), and the NASDAQ were grouped intodeciles according to their size. The average return of the smallest stockdecile was 0.74 percent per month greater than the average return of thehighest decile. The difference in risk is not enough to explain the differencein returns.
So how can all of these market anomalies be combined into a market-beating portfolio? Emory University professors Shanken and Kothariattempted to ﬁnd the optimum asset allocation to take advantage of com-bined small stock, value, and momentum anomalies.11 The authors sam-pled stocks from 1963 to 2000. They allowed for the possibility that thepositive alphas of their three anomaly portfolios might not be as large in thefuture as they had appeared to be in the past. They found that even if port-folio alphas were expected to be just a quarter of the size they were in thepast, an optimal portfolio would invest more than half in value stocks andabout one-third in momentum stocks. This strategy increased the annualexpected return by about 0.6 percent, relative to the market index, holdingrisk constant. With alphas half of the historical level, the authors found thatthe increase in expected return is more substantial, almost 2 percent.
As we saw in Chapter 1, active investment management is a losingproposition for the vast majority of investors, both individual and profes-sional. As a result, academics recommend that individuals buy index fundsand avoid checking their prices for 10 years (for long-term investors).Index funds have low expenses, and they are likely to outperform bondssigniﬁcantly over the long term (taking advantage of the equity premiumpuzzle). Risk management is easier with index funds than with a portfolioof individual stocks. Different types of index funds—sector, international,
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currencies, and commodities—can be balanced to create diversiﬁedportfolios with lower overall risk proﬁles. Buying and holding indexfunds for the long term is the highest-returning investment plan for mostinvestors. However, if one checks their prices repeatedly, they will seemore downside risk and will thus be more likely to sell.
“BUY ON THE RUMOR AND SELLON THE NEWS”
The “Buy on the rumor and sell on the news” (BRSN) price pattern de-scribes a phenomenon in which a stock price rises in anticipation of a pos-itive event, and then falls immediately after the event happens. It appearsto be caused by several biases. Many traders buy shares in anticipation ofa positive event, such as a “better-than-expected” earnings report, a hypedproduct release, or good economic news. It is said that traders are “buy-ing on the rumor” because the good event has not yet occurred. When theexpected event does occur, prices often fall, which is the opposite of whatwas expected.
Investors’ positive expectations for an anticipated event trigger thenucleus accumbens (NAcc). The NAcc drives excessive risk-taking. Asthe event approaches in time, investors’ expectations for “better-than-expected” news increase due to the NAcc-induced effects of excitement,time discounting, and the exaggeration of expectations by desire. As a sideeffect of such overoptimism, the probability that investors will be disap-pointed also increases. One study that examined the BRSN effect duringthe Internet bubble found it to be strikingly consistent.
Professor Brett Trueman and colleagues at the University of Californiaat Berkeley identiﬁed the BRSN pattern around the earnings announce-ments of Internet stocks. They analyzed a large sample of earningsannouncements for 393 Internet ﬁrms over 1,875 ﬁrm-quarters betweenJanuary 1998 and August 2000. They found that purchasing an Internetstock ﬁve days prior to its earnings announcement and then selling thatstock at the open on the day immediately following the earnings releaseyielded an average market adjusted return of 4.9 percent. Shorting thesame stock at the open the day after its earnings release yielded an averagemarket-adjusted return of 6.4 percent when the short was covered at themarket close ﬁve days later.12 The optimism that was brewing aroundInternet stocks transformed into excitement around their earnings reports.As with most diversity breakdowns in the markets, an exclusive focuson sentiment or behavioral indicators is inadequate for designing a market-beating strategy. Multiple conditions facilitate the “buy on the rumor and
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sell on the news” pattern in prices. Speciﬁc psychological and neuroscien-tiﬁc factors all amplify the emergence of this pattern in a stock, including:
Ĺ Signiﬁcantly limited or ambiguous accounting information available.Ĺ Excess liquidity available to traders.Ĺ High vividness or imaginability of the concept (even a vivid name
helps).
Ĺ A virtually “unlimited” potential market to drive revenues.Ĺ Groupthink and herding (for example, unconﬁrmed rumors and media
mentions).
Ĺ Preexisting optimism about the company or market sectorĹ Large potential magnitude of potential payoff.Ĺ Perceived certainty of payoff.Ĺ Recent trending price movement.Ĺ Event is approaching in time.Ĺ A high level of impulsivity in traders chasing the potential gain.13
Each of these factors can be reversed so that traders can take advan-tage of the pattern in a “Sell on the rumor, buy on the news” scenario, butthe dynamics of such a strategy differs from BRSN.14
LIMITS TO ARBITRAGE
“Limits to arbitrage” prevent behavioral price distortions from being tradedfor proﬁt. Patterns are easy to ﬁnd in the markets, but ﬁnding patterns thatcan proﬁtably be exploited after transaction costs and structural barriers(such as short-sale constraints) are accounted for, is much more difﬁcult.The limits to arbitrage fall into three distinct categories: short-saleconstraints, size constraints, and transaction costs. Some preconditionsprevent the shorting of securities on certain markets, such as the over-the-counter (OTC) bulletin boards or some foreign markets. In some markets,only market makers can short. In others, regulations prevent any shorting.Many brokerages will not allow shorting of securities priced below acertain dollar amount, particularly for smaller clients. Additionally, it isoften the case that a broker cannot borrow the security for shorting.
Size constraints are represented by the inability to enter a substantialarbitrage position without moving the market into closing that arbitrageopportunity. In the worst-case scenario, unraveling large arbitrage po-sitions pushes prices back into the former pricing inefﬁciency. Such acase was seen as Long-Term Capital Management considered exiting itspositions.
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Transaction costs are one of the most overlooked aspects of invest-ment. Transaction costs are typically assumed to include commissions andslippage in securities and up-front and redemption fees in mutual funds.Transaction costs in large-cap stocks can consume about 0.1 percent ofcapital for each purchase or sale of shares. Small and micro-cap stockshave much steeper transaction costs, sometimes up to 10 percent whenthe bid-ask spread is wide. Futures often have the smallest transactioncosts—less than 0.05 percent. Active investors can lose a large portion oftheir proﬁts through transaction costs.
One example of an obvious arbitrage opportunity that could not be ex-ecuted involved the initial public offering (IPO) of Palm Inc. from 3ComInc. In March 2000, 3Com Inc. sold 5 percent of its wholly owned subsidaryPalm Inc. in an IPO, retaining ownership of the remaining 95 percent. Afterthe IPO, a shareholder of 3Com indirectly owned 1.5 shares of Palm (withfull spin-off planned in nine months). At the end of the ﬁrst IPO tradingday, Palm shares closed at $95, thus valuing 3Com at more than $142. But3Com was actually trading at $81, implying a market valuation of 3Com’ssigniﬁcant businesses outside of Palm at negative $60 per share. Arbitrageseemed like a no-brainer, as the prices would likely converge within ninemonths, but it was virtually impossible to execute due to short-sale con-straints (limited shares available to borrow). What should have been cer-tain proﬁts, gained through arbitrage, were unavailable to many attentivetraders.
BEHAVIORAL FINANCE FUND PERFORMANCE
Behavioral ﬁnance represents the application of principles of investor psy-chology to investing. Flagship funds using behavioral ﬁnance-based invest-ment strategies have outperformed their benchmarks signiﬁcantly overtime. One notable fund group is Fuller and Thaler Asset Management. Theyhave achieved an average alpha of almost 4 percent on their funds since in-ception. However, a recent ﬂood of money into behavioral strategies mayhave eliminated some of the advantages that Fuller and Thaler’s strategiesheld.
LGT Capital Management of Lichtenstein utilizes principles of behav-ioral ﬁnance, in combination with technical and fundamental market valua-tion approaches, to design portfolios. Some LGT strategies take advantageof “asymmetric risk perception as well as the various investment horizonsof investors.”15 According to an LGT brochure, “the ﬁnancial markets dealnot in realities, but in expectations, or more precisely changes in expecta-tions.”16 LGT’s funds have consistently delivered lower volatility than the
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markets and higher returns since inception. Their ﬂagship fund is up 65 per-cent since opening on December 31, 1998, while the Morgan Stanley GlobalIndex is up 43 percent.
Dimensional Fund Advisers manages $69 billion and was founded bytwo students of Professors Fama and French at the University of ChicagoBusiness School. Fama and French were two of the most adamant propo-nents of the efﬁcient markets hypothesis in the latter half of the twentiethcentury. However, Professor French currently posts excellent