Journalof Economic Perspectives—Volume 17,Number 1—Winter 2003— Pages 59 – 82

TheEfŽ cient Market Hypothesisand Its Critics

BurtonG. Malkiel

generationago, theefŽ cient market hypothesis was widelyaccepted by academicŽ nancial economists; forexample, see Eugene Fama’ s (1970) A inuential survey article, “ EfŽcient Capital Markets.” It was generallybe- lievedthat securitiesmarkets were extremely efŽ cient in re ecting information about individualstocks and about thestock market as awhole.The accepted view was that when informationarises, the news spreads veryquickly and isincorporated intothe prices of securitieswithout delay. Thus, neithertechnical analysis, which is thestudy ofpast stock pricesin an attemptto predict future prices, nor even fundamental analysis, which isthe analysis ofŽ nancial informationsuch as com- pany earningsand asset valuesto help investors select “ undervalued”stocks, would enablean investorto achieve returns greater than those that could beobtained by holdinga randomlyselected portfolio of individual stocks, at leastnot withcom- parablerisk. TheefŽ cient market hypothesis isassociated withthe idea of a“random walk,” which isatermloosely used intheŽ nance literatureto characterizea priceseries whereall subsequent pricechanges representrandom departuresfrom previous prices.The logic of the random walkidea is that ifthe  owof information is unimpededand informationis immediately re ected in stock prices,then tomor- row’s pricechange willre ect only tomorrow’ s newsand willbe independent of the pricechanges today. Butnews is by deŽ nition unpredictable, and, thus, resulting pricechanges must beunpredictable and random. Asaresult,prices fully re ect all known information,and evenuninformed investors buying a diversiŽed portfolio at thetableau of prices given by the market will obtain arateof return as generousas that achievedby the experts. y BurtonG. Malkielis Chemical Bank Chairman’ s Professorof Economics, Princeton University,Princeton, New Jersey.His e-mail addressis [email protected] . 60Journal of Economic Perspectives

Theway I put itinmybook, ARandomWalk Down Wall Street , Ž rstpublished in 1973,a blindfoldedchimpanzee throwing darts at the WallStreet Journal could select aportfoliothat woulddo as wellas theexperts. Of course, theadvice was not literallyto throw darts, but instead tothrow a towelover the stock pages —that is, tobuy abroad-based indexfund that bought and heldall the stocks inthemarket and that chargedvery low expenses. Bythe start of the twenty- Ž rstcentury, the intellectual dominance ofthe efŽ cientmarket hypothesis had becomefar less universal. Many Ž nancial econo- mistsand statisticians began tobelieve that stock pricesare at leastpartially predictable.A newbreed of economistsemphasized psychological and behavioral elementsof stock-price determination, and theycame to believe that futurestock pricesare somewhat predictable on thebasis ofpast stockprice patterns as wellas certain “fundamental ” valuation metrics.Moreover, many ofthese economists were evenmaking the far more controversial claim that thesepredictable patterns enableinvestors to earn excess risk adjusted ratesof return. This paperexamines the attacks on theef Ž cientmarket hypothesis and the beliefthat stock pricesare partially predictable. While I makeno attemptto present acompletesurvey of the purported regularities or anomalies in thestock market, Iwilldescribe the major statistical Ž ndings as wellas theirbehavioral underpin- nings, whererelevant, and also examinethe relationship between predictability and efŽ ciency.I willalso describethe major argumentsof those who believethat marketsare often irrational by analyzingthe “crash of1987, ” theInternet “bubble” of the Žn de siecle and otherspeci Ž cirrationalitiesoften mentioned by critics of efŽ ciency.I conclude that ourstock markets are far more ef Ž cientand farless predictablethan somerecent academic papers wouldhave us believe.Moreover, theevidence is overwhelming that whateveranomalous behaviorof stock prices mayexist, it does not createa portfoliotrading opportunity that enablesinvestors toearn extraordinary risk adjusted returns. Atthe outset, itis important to make clear what Imeanby the term “efŽ - ciency.” Iwilluse as ade Ž nition of efŽ cient Ž nancial marketsthat such marketsdo not allowinvestors to earn above-average returns without accepting above-average risks.A well-knownstory tells of a Ž nance professorand astudent who comeacross a$100bill lying on theground. As thestudent stops topickit up, theprofessor says, “Don’t bother—ifit were really a $100bill, it wouldn ’t be there.” Thestory well illustrateswhat Ž nancial economistsusually mean when theysay marketsare efŽ cient.Markets can beef Ž cientin this senseeven if theysometimes make errors invaluation, as was certainlytrue during the 1999 – early2000 Internet “bubble.” Marketscan beef Ž cienteven if many marketparticipants arequite irrational. Marketscan beef Ž cienteven if stock pricesexhibit greater volatility than can apparentlybe explained by fundamentals such as earningsand dividends. Many of us economistswho believein ef Ž ciencydo so because weviewmarkets as amazingly successful devicesfor re  ectingnew information rapidly and, forthe most part, accurately.Above all, we believe that Ž nancial marketsare ef Ž cientbecause they don’tallowinvestors to earn above-average risk adjusted returns.In short, we BurtonG. Malkiel61

believethat $100bills are not lyingaround forthe taking, either by the professional orthe amateur investor. What Ido not argueis that themarket pricing is always perfect.After the fact, weknowthat marketshave madeegregious mistakes, as Ithinkoccurred during the recentInternet “bubble.” Nordo Idenythat psychologicalfactors in  uence securitiesprices. But I am convinced that Benjamin Graham (1965)was correctin suggestingthat whilethe stock marketin the short run maybe a votingmechanism, inthe long run itis a weighingmechanism. Truevalue will win out inthe end. Beforethe fact, thereis no wayin which investorscan reliablyexploit any anomalies orpatterns that mightexist. I am skepticalthat any ofthe “predictablepatterns ” that have beendocumented in theliterature were ever suf Ž cientlyrobust so as to have createdpro Ž tableinvestment opportunities, and afterthey have beendiscov- eredand publicized,they will certainly not allowinvestors to earn excess returns.

ANonrandom Walk DownWall Street

In this section, Ireviewsome of the patterns ofpossible predictability sug- gestedby studies of the behavior of past stock prices.

Short-TermMomentum, Including Underreaction to New Information Theoriginal empirical work supporting thenotion ofrandomness instock priceslooked at measuresof short-run serialcorrelations between successive stock pricechanges. In general,this worksupported theview that thestock markethas no memory—that is, theway a stockprice behaved in the past isnot usefulin divininghow itwill behave in the future; for example, see the survey of articles contained inCootner (1964). More recent work by Lo and MacKinlay(1999) Ž nds that short-run serialcorrelations are not zeroand that theexistence of “too many” successivemoves in the same direction enable them to reject the hypothesis that stock pricesbehave as truerandom walks.There does seemto be some momentum inshort-run stock prices.Moreover, Lo, Mamaysky and Wang (2000)also Ž nd, through theuse of sophisticated nonparametricstatistical techniques that can recognizepatterns, someof the stock pricesignals used by “technicalanalysts, ” such as “head and shoulders ” formationsand “doublebottoms, ” mayactually have some modestpredictive power. Economistsand psychologists inthe Ž eldof behavioral Ž nance Ž nd such short-run momentumto be consistent withpsychological feedback mechanisms. Individuals seea stockprice rising and aredrawn intothe market in a kindof “bandwagon effect. ” Forexample, Shiller (2000) describes the rise in the U.S. stock marketduring the late 1990s as theresult of psychological contagion leadingto irrationalexuberance. The behavioralists offered another explanationfor patterns ofshort-run momentum —atendencyfor investors to underreact to new informa- tion. If thefull impact of an importantnews announcement isonlygrasped over a periodof time, stock prices will exhibit the positive serial correlation found by 62Journal of Economic Perspectives

investigators.As behavioral Ž nance becamemore prominent as abranch ofthe study of Ž nancial markets,momentum, as opposed torandomness, seemedrea- sonable tomany investigators. However,several factors should preventus frominterpreting the empirical resultsreported above as an indication that marketsare inef Ž cient.First, while the stockmarket may not bea mathematicallyperfect random walk,it is importantto distinguishstatistical signi Ž cance fromeconomic signi Ž cance. Thestatistical de- pendenciesgiving rise to momentum are extremely small and arenot likelyto permitinvestors to realize excess returns. Anyone who pays transactions costs is unlikelyto fashion atradingstrategy based on thekinds ofmomentum found in thesestudies that willbeat a buy-and-hold strategy.Indeed, Odean (1999)suggests that momentuminvestors do not realizeexcess returns. Quite the opposite —a sampleof such investorssuggests that such tradersdid far worse than buy-and-hold investorseven during a periodwhere there was clearstatistical evidence of positive momentum.This isbecause ofthe large transactions costs involvedin attempting toexploit whatever momentum exists. Similarly, Lesmond, Schilland Zhou (2001) Ž nd that standard “relativestrength ” strategiesare not pro Ž tablebecause ofthe tradingcosts involvedin their execution. Second, whilebehavioral hypotheses about bandwagon effectsand under- reactionto new information may sound plausibleenough, theevidence that such effectsoccur systematicallyin the stock marketis often rather thin. Forexample, EugeneFama (1998)surveys the considerable body ofempirical work on “event studies” that seeksto determine if stock pricesrespond ef Ž cientlyto information. The “events” includesuch announcements as earningssurprises, stock splits,divi- dend actions, mergers,new exchange listings and initialpublic offerings. Fama Ž nds that apparent underreactionto information is about as common as over- reaction,and posteventcontinuation ofabnormal returnsis as frequentas posteventreversals. He also shows that many ofthe return “anomalies” ariseonly in thecontext of some very particular model and that theresults tend todisappear when exposedto different models for expected “normal” returns,different meth- ods toadjust forrisk and when differentstatistical approaches areused tomeasure them.For example, a study that givesequal weight to postannouncement returnsof many stocks can produce differentresults from a study that weightsthe stocks according totheir value. Certainly, whatever momentum displayed by stock prices does not appear tooffer investors a dependableway to earn abnormal returns. Thekey factor is whetherany patterns ofserial correlation are consistent over time.Momentum strategies, which referto buyingstocks that displaypositive serial correlationand/ orpositiverelative strength, appeared to produce positive relative returnsduring some periods of thelate 1990s, but highlynegative relative returns during2000. It isfarfrom clear that any stock pricepatterns areuseful for investors infashioning an investmentstrategy that willdependably earn excess returns. Many predictablepatterns seemto disappear afterthey are published in the Ž nance literature.Schwert (2001) points out twopossible explanations forsuch a pattern. One explanationmay be that researchersare always sifting through The EfŽcientMarket Hypothesis and Its Critics 63

mountains of Ž nancial data. Theirnormal tendency is to focus on resultsthat challengeperceived wisdom, and everynow and again, acombination ofa certain sampleand acertaintechnique will produce a statisticallysigni Ž cant resultthat seemsto challenge the ef Ž cientmarkets hypothesis. Alternatively,perhaps practi- tionerslearn quickly about any truepredictable pattern and exploitit tothe extent that itbecomes no longerpro Ž table.My own viewis that such apparent patterns werenever suf Ž cientlylarge or stable to guarantee consistently superior investment results,and certainly,such patterns willnever be usefulfor investors after they have receivedconsiderable publicity. The so-called “January effect, ” forexample, in which stock pricesrose in early January, seemsto have disappearedsoon afterit was discovered.

Long-RunReturn Reversals In theshort-run, when stockreturns are measured over periods of days or weeks,the usual argumentagainst marketef Ž ciencyis that somepositive serial correlation exists.But many studieshave shown evidenceof negative serial correlation —that is, returnreversals — overlonger holding periods. For example, Fama and French(1988) found that 25to 40 percent of the variation in long holdingperiod returns can bepredicted in termsof a negativecorrelation with past returns.Similarly, Poterba and Summers(1988) found substantial meanreversion instock marketreturns at longerhorizons. Somestudies have attributedthis forecastabilityto the tendency of stock marketprices to “overreact.” DeBondtand Thaler(1985), for example, argue that investorsare subject towaves of optimism and pessimismthat cause pricesto deviatesystematically from their fundamental valuesand laterto exhibit mean reversion.They suggest that such overreactionto past eventsis consistent withthe behavioraldecision theory of Kahneman and Tversky(1979), where investors are systematicallyovercon Ž dent intheirability to forecasteither future stock prices or futurecorporate earnings. These Ž ndings givesome support toinvestment tech- niquesthat reston a “contrarian” strategy,that is, buyingthe stocks, orgroups of stocks, that have beenout offavor for long periods of time and avoidingthose stocks that have had largerun-ups overthe last several years. Thereis indeed considerable support forlong-run negativeserial correla- tionin stock returns.However, the Ž nding ofmean reversion is not uniform across studiesand isquitea bitweaker in someperiods than itisforother periods. Indeed, thestrongest empirical results come from periods including the Great Depression—which maybe a timewith patterns that do not generalizewell. Moreover,such returnreversals for the market as awholemay be quite consistent with the efŽ cientfunctioning ofthe market since they could result,in part, from thevolatility of interest rates and thetendency of interest rates to be mean reverting.Since stock returnsmust riseor fallto becompetitivewith bond returns, thereis atendencywhen interestrates go up forprices of both bond and stocks to godown, and as interestrates go down forprices of bonds and stocks togo up. If interestrates revert to the mean over time, this patternwill tend togeneratereturn 64Journal of Economic Perspectives

reversals,or mean reversion, in a waythat isquite consistent withthe ef Ž cient functioning ofmarkets. Moreover,it may not bepossible to pro Ž tfromthe tendency for individual stocks toexhibit return reversals. Fluck, Malkiel and Quandt (1997)simulated a strategyof buying stocks overa 13-yearperiod during the 1980s and early1990s that had particularlypoor returns over the past threeto Ž veyears. They found that stocks withvery low returns over the past threeto Ž veyears had higherreturns in thenext period and that stocks withvery high returnsover the past threeto Ž ve yearshad lowerreturns in the next period. Thus, theycon Ž rmedthe very strong statistical evidenceof return reversals. However, they also found that returnsin the nextperiod were similar for both groups, so theycould not con Ž rm that a contrarian approach wouldyield higher-than-average returns. There was astatisti- callystrong pattern of return reversal, but not onethat impliedan inef Ž ciency in themarket that wouldenable investors to make excess returns.

Seasonaland Day-of-the-WeekPatterns Anumberof researchers have found that January has beena veryunusual month forstock marketreturns. Returns from an equallyweighted stock indexhave tendedto be unusually high duringthe Ž rsttwo weeks of the year. The return premiumhas beenparticularly evident for stocks withrelatively small total capital- izations(Keim, 1983). Haugen and Lakonishok(1988) documented the high January returnsin abooktitled TheIncredible January Effect .Therealso appear tobe anumberof day-of-the-week effects. For example, French (1980) documents signiŽ cantly higherMonday returns.There appear tobe signi Ž cant differencesin averagedaily returns in countries other than theUnited States (Hawawini and Keim,1995). There also appear tobe some patterns inreturns around theturn of themonth (Lakonishokand Smidt,1988), as wellas around holidays (Ariel,1990). Thegeneral problem with these predictable patterns oranomalies, however,is that theyare not dependablefrom period to period. Wall Street traders now joke that the “January effect ” ismore likely to occur on theprevious Thanksgiving. Moreover,these nonrandom effects(even if theywere dependable) are very small relativeto the transactions costs involvedin trying to exploit them. They do not appear tooffer arbitrage opportunities that wouldenable investors to make excess riskadjusted returns.

Predictable Patterns Based on Valuation Parameters

Considerableempirical research has beenconducted todetermine if future stockreturns can bepredicted on thebasis ofinitial valuation parameters.It is claimedthat valuation ratios, such as theprice-earnings multiple or the dividend yieldof the stock marketas awhole,have considerablepredictive power. This sectionexamines the body ofwork based on timeseries analyses. BurtonG. Malkiel65

PredictingFuture Returns from Initial Dividend Yields Formalstatistical tests of the ability of dividend yields (that is, theratio of dividendto stock price)to forecast future returns have beenconducted byFama and French(1988) and Campbelland Shiller(1988). Depending on theforecast horizoninvolved, as much as 40percent of the variance of future returns for the stockmarket as awholecan bepredicted on thebasis ofthe initial dividend yield ofthe market index. Aninterestingway of presenting the results is shown inthetop panel ofExhibit 1. Theexhibit was produced bymeasuring the dividend yield of the broad U.S. stock marketStandard &Poor ’s500Stock Index each quartersince 1926 and then calculatingthe market ’ssubsequent ten-yeartotal return through theyear 2001. Theobservations were then dividedinto deciles depending upon thelevel of the initialdividend yield. In general,the exhibit shows that investorshave earneda higherrate of returnfrom the stock market when theypurchased amarketbasket ofequities with an initialdividend yield that was relativelyhigh and relativelylow futurerates of return when stocks werepurchased at lowdividend yields. These Ž ndings arenot necessarilyinconsistent withef Ž ciency.Dividend yields ofstocks tend tobe high when interestrates are high, and theytend tobelow when interestrates are low. Consequently, theability of initial yields to predict returns maysimply re  ectthe adjustment ofthestock market to generaleconomic condi- tions. Moreover,the use ofdividend yields to predict future returns has been ineffectivesince the mid-1980s. Dividend yields have beenat the3 percentlevel or belowcontinuously sincethe mid-1980s, indicating very low forecasted returns. In fact, forall ten-year periods from 1985 through 1992that endedJune 30,2002, realizedannual equityreturns from the market index have averagedover 15percent. One possibleexplanation is that thedividend behavior of U.S. corpo- rations mayhave changed overtime (Bagwell and Shoven, 1989;Fama and French, 2001).Companies inthetwenty- Ž rstcentury may be morelikely to institutea share repurchaseprogram rather than increasetheir dividends. Thus, dividendyield may not beas meaningfulas inthe past as ausefulpredictor of future equity returns. Finally,it isworthnoting that this phenomenon does not workconsistently with individualstocks (Fluck,Malkiel and Quandt, 1997).Investors who simplypurchase aportfolioof individual stocks withthe highest dividend yields in themarket will not earna particularlyhigh rateof return.One popular implementationof such a “high dividend ” strategyin the United States is the “Dogsof the Dow Strategy, ” which involvesbuying the ten stocks inthe Dow Jones Industrial Averagewith the highestdividend yields. For some past periods,this strategyhandily outpaced the overallaverage, and so several “Dogsof the Dow ” mutualfunds werebrought to marketand aggressivelysold toindividualinvestors. However, such funds generally underperformedthe market averages during the 1995 –1999period.

PredictingMarket Returns from Initial Price-Earnings Multiples Thesame kind of predictability for the market as awhole,as was demonstrated fordividends, has beenshown forprice-earnings ratios. Thedata areshown inthe 66Journal of Economic Perspectives

Exhibit 1 TheFuture 10-Year Rates of Return When Stocks are Purchased at Alternative InitialDividend Yields (D/ P)

Return (%) 18 16 14 12 10 8 6 4 2 0 .1 .7 .1 .7 .3 .9 .6 .3 .0 .9 6 5 5 4 4 3 3 3 3 2 . 5 5 5 5 5 5 5 5 , P P P P P P P P P P / / / / / / / / / / D D D D D D D D D D Stocks Cheap Stocks Expensive

TheFuture 10-Year Rates of Return When Stocks are Purchased at Alternative InitialPrice-to-Earnings (P/ E)Multiples

Return (%) 18 16 14 12 10 8 6 4 2 0 .9 .4 .5 .9 .4 .0 .4 .6 .1 .9 9 0 1 2 4 6 7 8 0 0 1 1 1 1 1 1 1 2 2 , E 5 5 5 5 5 5 5 5 . / E E E E E E E E E P / / / / / / / / / P P P P P P P P P Stocks Cheap Stocks Expensive

Source: TheLeuthold Group bottomhalf ofExhibit 1. Theexhibit presents a decileanalysis similarto that describedfor dividend yields above. Investorshave tendedto earn larger long- horizonreturns when purchasing themarket basket of stocks at relativelylow price-earningsmultiples. Campbell and Shiller(1998) report that initialP/ Eratios The EfŽcientMarket Hypothesis and Its Critics 67

explainedas much as 40percentof the variance of future returns. They conclude that equityreturns have beenpredictable in the past toa considerableextent. Consider, however,the recent experience of investors who have attemptedto undertakeinvestment strategies based eitheron thelevel of the price-earnings multipleor the dividend yield to predict future long-horizon returns. Price- earningsmultiples for the Standard &Poor ’s500stock indexrose into the low 20s on June 30,1987 (suggesting very low long-horizon returns). Dividend yields fell below3 percent.Price-earnings multiples rose into the low 20s. Theaverage annual totalreturn from the index over the next 10 years was an extraordinarilygenerous 16.7percent. Dividend yields, again, fellto 3 percentin June 1992.Price-earnings multiplesrose to the mid-20s. The subsequent returnthrough June 2002was 11.4percent. The yield of the index  uctuated between2 and 3percentfrom 1993 through 1995and earningsmultiples remained in the mid-20s, yet long-horizon returnsthrough June 30,2002,  uctuated between11 and 12percent. Even from earlyDecember 1996, the date of Campbell and Shiller ’spresentationto the FederalReserve suggesting near zero returns for the S&P500, the index provided almosta 7percentannual returnthrough mid-2002.Such resultssuggest to me a verycautious assessment ofthe extent to which stock marketreturns are predictable on this basis.

OtherPredictable Time Series Patterns Studieshave found someamount ofpredictability of stock returns based on various Ž nancial statistics. Forexample, Fama and Schwert(1977) found that short-terminterest rates were related to future stock returns.Campbell (1987) found that termstructure of interestrates spreads contained usefulinformation for forecastingstock returns.Keim and Stambaugh (1986)found that riskspreads betweenhigh-yield corporate bonds and short rateshad somepredictive power. Again, evenif some predictability exists, it mayre  ecttime varying risk premiums and requiredrates of return for stock investorsrather than an inef Ž ciency.More- over,it is farfrom clear that any ofthese results can beused togenerate pro Ž table tradingstrategies. Whether such historicalstatistical relations give investors reliable and usefulguides to appropriate asset allocationis farfrom clear.

Cross-Sectional Predictable Patterns Based on Firm Characteristics andValuation Parameters

Alargenumber of patterns that areclaimed to be predictable are based on Ž rmcharacteristics and differentvaluation parameters.

TheSize Effect One ofthe strongest effects investigators have found isthe tendency over long periodsof timefor smaller-company stocks togeneratelarger returns that those of 68Journal of Economic Perspectives

large-companystocks. Since1926, small-company stocks intheUnited States have produced annual ratesof return over 1 percentagepoint largerthan thereturns fromlarge stocks (Keim,1983). Fama and French(1993) examined data from1963 to1990 and dividedall stocks intodeciles according totheir size as measuredby totalcapitalization. Decile one contained thesmallest 10 percentof allstocks, while decileten contained thelargest stocks. Theresults, plotted in Exhibit 2, show a cleartendency for the deciles made up ofportfolios of smaller stocks togenerate higheraverage monthly returns than decilesmade up oflarger stocks. Thecrucial issue here is the extent to which thehigher returns of small companies representa predictablepattern that willallow investors to generate excessrisk-adjusted returns.According to the capital asset pricingmodel, the correctmeasure of risk for a stock isits “beta”—that is, theextent to which the returnof the stock varieswith the return for the market as awhole.If thebeta measureof systematic risk from the capital asset pricingmodel is accepted as the correctrisk measurement statistic, thesize effect can beinterpretedas indicatingan anomaly and amarketinef Ž ciency,because using this measureportfolios consisting ofsmaller stocks have excessrisk-adjusted returns.Fama and French(1993) point out, however,that theaverage relationship between beta and returnduring the 1963–1990period was  at—not upward slopingas thecapital asset pricingmodel predicts.Moreover, if stocks aredivided up bybeta deciles, ten portfolios con- structedby size display the same kind of positive relationship shown inExhibit 2. On theother hand, withinsize deciles, the relationship between beta and return continues tobe  at. Fama and Frenchsuggest that sizemay be a farbetter proxy forrisk than beta, and thereforethat their Ž ndings should not beinterpreted as indicatingthat marketsare inef Ž cient. Thedependability of thesize phenomenon isalso open toquestion. Fromthe mid-1980sthrough thedecade of the 1990s, there has beenno gainfrom holding smallerstocks. Indeed, inmostworld markets, larger capitalization stocks produced largerrates of return. It maybe that thegrowing institutionalization of themarket ledportfolio managers to prefer larger companies withmore liquidity to smaller companies whereit would be dif Ž cultto liquidate signi Ž cant blocksof stock. Finally,it is also possiblethat somestudies of the small- Ž rmeffect have been affectedby survivorshipbias. Today ’scomputerizeddatabases ofcompanies include only small Ž rmsthat have survived,not theones that laterwent bankrupt. Thus, a researcherwho examinedthe ten-year performance of today ’ssmallcompanies wouldbe measuring the performance of those companies that survived —not the ones that failed.

ValueStocks Severalstudies suggest that “value” stocks have higherreturns than so-called “growth” stocks. Themost common twomethods ofidentifying value stocks have beenprice-earnings ratios and price-to-book-valueratios. Stockswith low price-earnings multiples (often called “value” stocks) appear to providehigher rates of return than stocks withhigh price-to-earningsratios, as Ž rst BurtonG. Malkiel69

Exhibit 2 AverageMonthly Returns for Portfolios Formed on theBasis of Size: 1963 –1990

Return

1.6 1.5 1.4 1.3 1.2 1.1 1.0 .9 .8

1 2 3 4 5 6 7 8 9 10 Size Small Stocks Large Stocks

Source: Fama and French(1992). shown byNicholson (1960)and latercon Ž rmedby Ball (1978) and Basu (1983). This Ž nding isconsistent withthe views of behavioralists that investorstend tobe overconŽ dent oftheir ability to project high earningsgrowth and thus overpayfor “growth” stocks (forexample, Kahneman and Riepe,1998). The Ž nding isalso consistent withthe views of Graham and Dodd (1934), Ž rstexpounded intheir classic bookon securityanalysis and laterchampioned bythe legendary U.S. investorWarren Buffett. Similar results have beenshown forprice/ cash  ow multiples,where cash  ow is deŽ ned as earningsplus depreciationand amortiza- tion(Hawawini and Keim,1995). Theratio of stock priceto book value, de Ž ned as thevalue of a Ž rm’s assets minus itsliabilities divided by the number of shares outstanding, has also been found tobe a usefulpredictor of futurereturns. Low price-to-book is considered to beanother hallmarkof so-called “value” inequity securities and isalso consistent withthe view of behavioralists that investorstend tooverpayfor “growth” stocks that subsequentlyfail to live up toexpectations. Fama and French(1993) concluded that sizeand price-to-book-valuetogether provide considerable explanatory power forfuture returns, and once theyare accounted for,little additional in  uence can beattributed to P/ Emultiples.Fama and French(1997) also conclude that the P/BVeffectis importantin many worldstock marketsother than theUnited States. Such resultsraise questions about theef Ž ciencyof the market if one accepts thecapital asset pricingmodel, as Lakonishok, Shleiferand Vishny (1994)point out. Butthese Ž ndings do not necessarilyimply inef Ž ciency.They may simply indicatefailure of the capital asset pricingmodel to capture all the dimensions of 70Journal of Economic Perspectives

risk.For example, Fama and French(1993) suggest that theprice-to-book-value ratio may re ectanother riskfactor that ispricedinto the market and not captured bythe capital asset pricingmodel. Companies insome degree of Ž nancial distress, forexample, are likely to sellat lowprices relative to bookvalues. Fama and French (1993)argue that athree-factorasset-pricing model (including price-to-book-value and sizeas measuresof risk)is theappropriate benchmark against which anomalies should bemeasured. Wealso needto keep in mindthat theresults of publishedstudies — even those done overdecades —maystill be time-dependent and ask whetherthe return patterns ofacademicstudies can actuallybe generated with real money. Exhibit 3 presentsaverage actual returnsgenerated by mutual funds classi Ž edbyeither their “growth” or “value” objectives. “Value” funds areso classi Ž edifthey buy stocks with price-to-earningsand price-to-book-valuemultiples that arebelow the averages for thewhole stock market.Over a periodrunning back tothe 1930s, it does not appear that investorscould actuallyhave realizedhigher rates of return from mutualfunds specializingin “value” stocks. Indeed, theexhibit suggests that the Fama-French (1993)period from the early 1960s through 1990may have beena uniqueperiod in which valuestocks ratherconsistently produced higherrates of return. Schwert(2001) points out that theinvestment Ž rmof Dimensional Fund Advisorsactually began amutualfund that selectedvalue stocks quantitatively according tothe Fama and French(1993) criteria. The abnormal returnof such a portfolio(adjusting forbeta, thecapital asset pricingmodel measure of risk) was a negative0.2 percent per month overthe 1993 –1998period. The absence during that periodof an excessreturn to the “value” stocks isconsistent withthe results from “activelymanaged ” valuemutual funds shown inExhibit 3.

TheEquity Risk Premium Puzzle Anotherpuzzle that isoften used tosuggest that marketsare less than fully rationalis the existence of a verylarge historical equity risk premium that seems inconsistent withthe actual riskinessof common stocks as can bemeasured statistically.For example, using theIbbotson data on stock returnsfrom 1926 through 2001,common stocks have produced ratesof retain of approximately 10.5percent, while high-grade bonds have returnedonly about 5.5percent. I believethat this Ž nding istheresult of a combination ofperceived equity risk being considerablyhigher during the early years of the period and ofaverage equity returnsbeing much higherthan had beenforecast by investors. It iseasy to say 50to 75 years later that common stocks wereunderpriced duringthe 1930s and 1940s.But remember that theannual averageof almost 6percentgrowth in corporateearnings and dividendsthat has occurredsince 1926 was hardlya foregoneconclusion duringa periodof severe depression and world war. Indeed, theU.S. stock marketis almostunique in that itisoneof the few world marketsthat remainedin continuous operationduring the entire period and the measuredrisk premium results, in part, fromsurvivorship bias. One must bevery The EfŽcientMarket Hypothesis and Its Critics 71

Exhibit 3 Reversionto the Mean: Relative Performance of “Value” vs. “Growth” Mutual Funds, 1937–June 2002

1.1 Average Annual Return Growth: 10.61% Value: 10.57% 1.0 Value n r Outperforming u t 0.9 e R Growth e v i Outperforming t

a 0.8 l e R 0.7

0.6 7 0 3 6 9 2 5 8 1 4 7 0 3 6 9 2 5 8 1 4 7 0 3 4 4 4 4 5 5 5 6 6 6 7 7 7 7 8 8 8 9 9 9 0

Source: LipperAnalytic Servicesand BogleResearch Institute ValleyForge, Pennsylvania. Note: Theexhibit shows the cumulativevalue of onedollar invested in the average “value” fund dividedby the samestatistic calculatedfor the average “growth” fund. carefulto distinguish between expected risk premiums and such premiumsmea- suredafter the fact. Fama and French(2002) argue that thehigh averagerealized returnsresult in part fromlarge unexpected capitalgains. Economistssuch as Shiller have suggestedthat duringthe early 2000s, the expected equity risk premium was, ifanything, irrationallytoo low.

Summarizingthe “Anomalies” and PredictablePatterns Thepreceding sections have pointedout many “anomalies” and statistically signiŽ cant predictablepatterns inthe stock returnsthat have beenuncovered in theliterature. However, these patterns arenot robust and dependablein different sampleperiods, and someof the patterns based on fundamental valuation mea- suresof individual stocks maysimply re  ectbetter proxies for measuring risk. Moreover,many ofthesepatterns, evenif they did exist,could self-destructin thefuture, as many ofthem have alreadydone. Indeed, this isthe logical reason whyone should becautious not tooveremphasize these anomalies and predictable patterns. Suppose, forexample, one of the anomalies or predictable patterns appears tobe robust. Suppose thereis atrulydependable and exploitableJanuary effect,that thestock market — especiallystocks ofsmall companies —willgenerate extraordinaryreturns during the Ž rst Ž vedays ofJanuary. What willinvestors do? Theywill buy onthelast day ofDecemberand sellon January 5.Butthen investors Ž nd that themarket rallied on thelast day ofDecember,and so theywill need to beginto buy on thenext-to-last day ofDecember; and because thereis so much “proŽ t taking” on January 5, investorswill have tosell on January 4totake advantage ofthis effect.Thus, tobeat the gun, investorswill have tobe buying 72Journal of Economic Perspectives

earlierand earlierin December and sellingearlier and earlierin January so that eventuallythe pattern will self-destruct. Any trulyrepetitive and exploitablepattern that can bediscovered in the stock marketand can bearbitraged away will self-destruct.Indeed, theJanuary effectbecame undependable afterit received considerablepublicity. Similarly,suppose thereis a generaltendency for stock pricesto underreact to certainnew events, leading to abnormal returnsto investorswho exploitthe lack of fullimmediate adjustment (DeBondtand Thaler,1995; Campbell, Lo and Mac- Kinlay,1977). “Quantitative ” investmentmanagers will then developtrading strat- egiesto exploit the pattern. Indeed, themore potentially pro Ž tablea discoverable patternis, theless likely it is to survive. Many ofthe predictable patterns that have beendiscovered may simply be the resultof data mining.The ease of experimenting with Ž nancial databanks ofalmost everyconceivable dimension makes it quite likely that investigatorswill Ž nd some seeminglysigni Ž cant but whollyspurious correlationbetween Ž nancial variablesor among Ž nancial and non Ž nancial data sets. Givenenough timeand massaging of data series,it ispossibleto tease almost any patternout ofmost data sets. Moreover, thepublished literature is likely to be biased in favor of reporting such results. SigniŽ cant effectsare likely to be published in professionaljournals whilenegative results,or boring con Ž rmationsof previous Ž ndings, arerelegated to the Ž le draweror discarded. Data-miningproblems are unique to nonexperimental sci- ences, such as economics, which relyon statisticalanalysis fortheir insights and cannot testhypotheses byrunning repeatedcontrolled experiments. An exchangeat asymposiumabout adecadeago betweenRobert Shiller, an economistwho issympathetic to the argument that stock pricesare partially predictableand skepticalabout marketef Ž ciency,and Richard Roll,an academic Ž nancial economistwho also isa portfoliomanager, is quite revealing (Roll and Shiller,1992). After Shiller stressed the importance of inef Ž cienciesin thepricing ofstocks, Rollresponded as follows:

Ihave personallytried to invest money, my client ’smoneyand myown, in everysingle anomaly and predictivedevice that academics have dreamed up. ...Ihave attemptedto exploit the so-called year-end anomalies and a wholevariety of strategiessupposedly documentedby academic research. And Ihaveyet tomake anickel onany of these supposed marketinef Žciencies . . . a true market inefŽciency ought tobe an exploitableopportunity. If there ’s nothing investorscan exploitin asystematicway, timein and timeout, then it ’s very hard tosay that informationis not beingproperly incorporated into stock prices.

Seemingly Irrefutable Cases ofInef Ž ciency

Criticsof ef Ž ciencyargue that thereare several instances ofrecent market historywhere market prices could not plausiblyhave beenset by rational investors BurtonG. Malkiel73

and that psychologicalconsiderations must have playedthe dominant role.It is alleged,for example, that thestock marketlost about one-thirdof its value from earlyto mid-October 1987 with essentially no change inthe general economic environment.How could marketprices be ef Ž cientboth at thestart of October and duringthe middle of the month? Similarly,it iswidely believed that thepricing of Internetstocks inearly 2000 could onlybe explainedby the behavior of irrational investors.Do such eventsmake a beliefin ef Ž cientmarkets untenable?

TheMarket Crash of October 1987 Can theOctober 1987 market crash beexplainedby rational considerations, or does such arapidand signi Ž cant change inmarket valuations provethe dominance ofpsychological rather than logicalfactors inunderstanding thestock market? Behavioristswould say that theone-third drop inmarket prices, which occurred earlyin October 1987, can onlybe explained by relying on psychologicalconsid- erations,since the basic elementsof the valuation equationdid not change rapidly overthat period.It is, ofcourse, impossibleto rule out theexistence of behavioral orpsychological in  uences on stock marketpricing. But logical considerations can explaina sharp change inmarketvaluations such as occurredduring the Ž rst weeks ofOctober 1987. Anumberof factors could rationallyhave changed investors ’ viewsabout the propervalue of the stock marketin October 1987. For one thing, yieldson long-termTreasury bonds increasedfrom about 9percentto almost 10.5 percent in thetwo months priorto mid-October. Moreover, a numberof eventsmay rationally have increasedrisk perceptions during the Ž rsttwo weeks of October. Early in the month, Congressthreatened to impose a “merger tax” that wouldhave made mergeractivity prohibitively expensive and could wellhave endedthe merger boom. Therisk that mergeractivity might be curtailed increased risks throughout thestock marketby weakening the discipline over corporate management that potentialtakeovers provide. Also, inearly October 1987, then Secretaryof the TreasuryJames Baker had threatenedto encourage a furtherfall in theexchange valueof the dollar, increasing risks for foreign investors and frighteningdomestic investorsas well.While it is impossible to correlate each day ’smovementin stock pricesto speci Ž cnewsevents, it isnot unreasonable toascribethe sharp declinein mid-Octoberto the cumulative effect of a numberof unfavorable “fundamental ” events.As MertonMiller (1991) has written, “...on October19, some weeks of externalevents, minor in themselves. ..cumulativelysignaled a possiblechange in what had beenup tothen averyfavorable political and economicclimate for equities. ..and ...many investorssimultaneously came to believe they were hold- ingtoo large a share oftheir wealth in risky equities. ” Shareprices can behighly sensitive as aresultof rational responses tosmall changes ininterest rates and riskperceptions. Suppose stocks arepriced as the presentvalue of the expected future stream of dividends. Fora long-termholder of stocks, this rationalprinciple of valuation translatesto a formula: 74Journal of Economic Perspectives

r 5 D/P 1 g, where r isthe rate of return, D/P isthe (expected) dividend yield, and g is the long-termgrowth rate. For present purposes, consider r tobethe required rate of returnfor the market as awhole.Suppose initiallythat the “riskless” rateof interest on governmentbonds is9percentand that therequired additional riskpremium forequity investors is 2percentagepoints. In this case, r willbe 11 percent(0.09 1 0.02 5 0.11).If atypicalstock ’sexpectedgrowth rate, g, is7 percentand ifthe dividendis $4 pershare, wecan solvefor the appropriate price of the stock index (P),obtaining

$4 0.11 5 1 0.07 P

P 5 $100.

Nowassume that yieldson governmentbonds risefrom 9 to10.5percent, with no increasein expected in  ation, and that riskperceptions increase so that stock-marketinvestors now demand apremiumof 2.5 percentage points instead of the2 points inthe previous example. The appropriate rate of return or discount ratefor stocks, r,risesthen from11 percentto 13percent (0.105 1 0.025),and the priceof our stock indexfalls from $100 to $66.67:

$4 0.13 5 1 0.07 P

P 5 $66.67.

Theprice must fallto raise the dividend yield from 4 to6 percentso as toraise thetotal return by the required 2 percentagepoints. Clearly,no irrationalityis requiredfor share pricesto sufferquite dramatic declines with the sorts ofchanges ininterest rates and riskperceptions that occurredin October 1987. Of course, evena verysmall decline in anticipated growth would have magni Ž ed these declinesin warranted share valuations. This isnot tosay that psychologicalfactors wereirrelevant in explaining the sharp drop inpricesduring October 1987 —theyundoubtedly playeda role.But it wouldbe a mistaketo dismissthe signi Ž cant change intheexternal environment, which can providean entirelyrational explanation for a signi Ž cant declinein the appropriatevalues for common stocks.

TheInternet Bubble of the Late 1990s Anotherstock marketevent often cited by behavioralists as clearevidence of theirrationality of markets is the Internet “bubble” ofthe late 1990s. Surely, the The EfŽcientMarket Hypothesis and Its Critics 75

remarkablemarket values assigned toInternet and relatedhigh-tech companies seeminconsistent withrational valuation. Ihave somesympathy withbehavioralists inthis instance, and inreviewing Robert Shiller ’s (2000) IrrationalExuberance, I agreedthat itwas inthe high-tech sectorof the market that his thesiscould be supported. Buteven here, when weknow after the fact that major errorswere made, therewere certainly no arbitrageopportunities available to rational investors before the “bubble” popped. Equityvaluations reston uncertain futureforecasts. Evenif allmarket partic- ipants rationallyprice common stocks as thepresent value of all future cash  ows expected,it is still possible for excesses to develop.We knownow, withthe bene Ž t ofhindsight, that outlandish and unsupportable claimswere being made regarding thegrowth of the Internet (and therelated telecommunications structure needed tosupport it).We know now that projections forthe rates and duration ofgrowth of these “new economy” companies wereunsustainable. Butremember, sharp- penciledprofessional investors argued that thevaluations ofhigh-tech companies wereproper. Many ofWall Street ’smostrespected security analysts, includingthose independentof investment banking Ž rms,were recommending Internet stocks to the Ž rm’sinstitutionaland individualclients as beingfairly valued. Professional pension fund and mutualfund managersoverweighted their portfolios with high- techstocks. Whileit is now clearin retrospect that such professionalswere egregiously wrong,there was certainlyno obviousarbitrage opportunity available. One could disagreewith the projected growth rates of security analysts. Butwho could besure, withthe use of the Internet for a timedoubling every several months, that the extraordinarygrowth rates that could justifystock valuations wereimpossible? After all,even Alan Greenspanwas singingthe praises of the new economy. Nothingis everas clearin prospectas itisinretrospect.The extent of the “bubble” was only clearin retrospect. Notonly is it almost impossible to judge with con Ž dencewhat theproper fundamental valueis for any security,but potentialarbitrageurs face additional risks.Shleifer (2000) has arguedthat “noisetrader risk ”—therisk from traders who areattempting to buy intorising markets and sellinto declining markets —limits theextent to which oneshould expectarbitrage to bring prices quickly back to rationalvalues even in the presence of an apparent bubble.Professional arbitra- geurswill be loath tosell short astock theybelieve is trading at twotimes its “fundamental ” valuewhen itis always possible that somegreater fools may be willingto pay threetimes the stock ’svalue.Arbitrageurs are quite likely to have short horizons, sinceeven temporary losses may induce theirclients to withdraw theirmoney. Whilethere were no pro Ž tableand predictablearbitrage opportunities avail- ableduring the Internet “bubble,” and whilestock priceseventually did adjust to levelsthat morereasonably re  ectedthe likely present value of theircash  ows, an argumentcan bemaintained that theasset pricesdid remain “incorrect” for a periodof time. The result was that too much newcapital  owedto Internet and 76Journal of Economic Perspectives

relatedtelecommunications companies. Thus, thestock marketmay well have temporarilyfailed in its role as an ef Ž cientallocator of equity capital. Fortunately, “bubble” periodsare the exception rather than therule, and acceptance ofsuch occasional mistakesis the necessary price of a  exiblemarket system that usually does averyeffective job ofallocating capital to its most productive uses.

OtherIllustrations of Irrational Pricing Arethere not someillustrations of irrational pricing that can beclearly ascertainedas theyarise, not simplyafter a “bubble” has burst?My favorite illus- trationconcerns thespinoff ofPalm Pilot from its parent 3Com Corporation duringthe height of the Internet boom in early2000. Initially, only 5 percentof the PalmPilot shares weredistributed to thepublic; theother 95 percent remained on 3Com’sbalance sheet.As PalmPilot began trading, enthusiasm forthe shares was so greatthat the95 percent of its shares stillowned by 3Com had amarketvalue considerablymore than theentire market capitalization of 3Com, implying that all therest of its business had anegativevalue. Other illustrations involve ticker symbol confusion. Rasches (2001) Ž nds clearevidence of comovement of stocks with similarticker symbols; forexample, the stock ofMCI Corporation(ticker symbol MCIC) movesin tandem withan unrelatedclosed-end bond investmentfund Mass Mutual CorporateInvestors (ticker symbol MCI). In acharmingarticle entitled “A Rose.com byAny OtherName, ” Cooper,Dimitrov and Rau (2001)found positive stock pricereactions during 1998 and 1999on corporatename changes when “dot com” was added tothe corporate title. Finally, it has beenargued that closed-end funds sellat irrationaldiscounts fromtheir net asset values(for example, Shleifer, 2000). Butnone oftheseillustrations should shake ourfaith that exploitablearbitrage opportunitiesshould not existin an ef Ž cientmarket. The apparent arbitragein the PalmPilot case (sellPalm Pilot short and buy 3Com)could not beundertaken becausenot enough Palmstock was outstanding tomake borrowing the stock possibleto effectuate a short sale.The “anomaly” disappearedonce 3Comspun off moreof Palm stock. Moreover,the potential pro Ž ts fromname or ticker symbol confusion areextremely small relative to the transactions costs that wouldbe requiredto exploitthem. Finally, the “closed-end fund puzzle ” isnot reallya puzzle today. Discounts have narrowedfrom historical averages for funds withassets tradedin liquid markets, and researcherssuch as Ross (forthcoming)have sug- gestedthat theycan largelybe explained by fund managementfees. Perhaps the moreimportant puzzle today iswhy so many investorsbuy high-expense,actively managed mutualfunds instead oflow-cost index funds.

ThePerformance of Professional Investors

Forme, the most direct and mostconvincing testsof market ef Ž ciency are directtests of the ability of professional fund managersto outperform the market BurtonG. Malkiel77

Exhibit 4 TheRecords of Surviving Funds Overstates the Success of Active Management

50

40 Surviving funds n r u t 30 e r

f o

e 20 t a R 10 All funds*

0 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 e 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 0 0 n ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ 9 u J 1

2 0 ’

*Includesthose liquidated or merged out of existence. Source: LipperAnalytic Services,as compiledby TheVanguard Group. as awhole.Surely, if market prices were determined by irrational investors and systematicallydeviated from rational estimates of thepresent value of corporations, and ifit were easy to spot predictablepatterns insecurity returns or anomalous securityprices, then professionalfund managersshould beable to beat the market. Directtests of theactual performanceof professionals, who oftenare compensated withstrong incentives to outperform the market, should representthe most com- pellingevidence of market ef Ž ciency. Aremarkablylarge body ofevidence suggests that professionalinvestment managersare not ableto outperform index funds that buy and hold thebroad stock marketportfolio. The Ž rststudy ofmutual fund performancewas undertaken byJensen (1968).He found that activemutual fund managerswere unable toadd valueand, infact, tendedto underperform the market by approximately the amount oftheir added expenses.I repeatedJensen ’sstudy withdata froma subsequent periodand con Ž rmedthe earlier results (Malkiel, 1995). Moreover, I found that thedegree of “survivorshipbias ” inthe data was substantial; that is, poorlyperforming funds tend tobe merged into other funds inthemutual fund ’s familycomplex, thus buryingthe records of many ofthe underperformers. Exhibit 4updates thestudy Iperformedthrough mid-2002,showing that thereturn for survivingfunds isquite a bitbetter than theactual returnfor all funds, including funds liquidatedor mergedout ofexistence. Survivorship bias makesthe interpre- tation oflong-run mutualfund data setsvery dif Ž cult. Buteven using data setswith somedegree of survivorship bias, onecannot sustain theargument that profes- sional investorscan beatthe market. Exhibit5 presentsthe percentage of activelymanaged mutualfunds that have beenoutperformed by the Standard &Poor ’s500and theWilshire stock indexes. Throughout thepast decade, about three-quartersof activelymanaged funds have 78Journal of Economic Perspectives

Exhibit 5 Percentageof Large Capitalization Equity Funds Outperformed by Index Ending 6/30/2002

1 year 3 years 5 years 10 years

S&P 500 vs. Large Cap Equity Funds 63% 56% 70% 79% Wilshire 5000 vs. Large Cap Equity Funds 72% 64% 69% 74%

Source: LipperAnalytic Services. Note: Alllarge capitalization mutual funds inexistence are covered with the exceptionof “sector” funds and funds investingin foreign securities.

Exhibit 6 MedianTotal Returns Ending 12/ 31/2001

10 years 15 years 20 years Large Cap Equity Funds 10.98% 11.95% 13.42% S&P 500 Index 12.94% 13.74% 15.24%

Source: LipperAnalytic Services,Wilshire Associates, Standard &Poor ’sand TheVanguard Group. failedto beat the index. Similar results obtain forearlier decades. Exhibit6 shows that themedian large-capitalization professionally managed equityfund has un- derperformedthe S&P 500index by almost 2 percentagepoints overthe past 10-, 15-and 20-yearperiods. Exhibit 7 shows similarresults in different markets and against differentbenchmarks. Managed funds areregularly outperformed by broad indexfunds, withequiv- alentrisk. Moreover, those funds that produceexcess returns in oneperiod are not likelyto do so inthe next. There is no dependablepersistence in performance. Duringthe 1970s, the top 20mutual funds enjoyedalmost double the performance ofthe index. During the 1980s, those samefunds underperformedthe index. The bestperforming funds ofthe 1980s similarly underperformed during the 1990s. A moredramatic example of the lack of persistence in performance is shown in Exhibit8. Thesemutual funds during1998 and 1999enjoyed three times the performanceof the index. During 2000 and 2001,they did threetimes worse than theindex. Over the long run, theresults are even more devastating to active managers. One can count on the Ž ngersof one hand thenumber of professional portfoliomanagers who have managed tobeat the market by any signi Ž cant amount. Exhibit9 shows thedistribution of return. Of theoriginal 355 funds, only Ž veof them outperformed the market by 2 percentper year or more. Therecord of professionals does not suggestthat suf Ž cientpredictability exists inthe stock marketor that thereare recognizable and exploitableirrationalities sufŽ cientto produce excess returns. The EfŽcientMarket Hypothesis and Its Critics 79

Exhibit 7 Percentageof Various Actively Managed Funds Outperformed by Benchmark Index 10Years to 12/ 31/01

100

80

60

40

20

0 U S Large- U S Large- European Japanese Emerging Global Bond Cap Equity Cap Equity Funds vs. Funds vs. M arket Funds Funds vs. vs. S&P 500 vs. W ilshire MSCI M SCI vs. M SCI Salomon 5000 Europe Japan EMF Index Source: LipperAnalytic Servicesand Micropal. Exhibit 8 GettingBurned by Hot Funds

1998–1999 2000–2001

Fund Average Average Annual Annual Name Rank Return Rank Return Van Wagoner:Emrg Growth 1 105.52 1106 243.54 Rydex:OTC Fund;Inv 2 93.43 1103 236.31 TCW Galileo:AGr Eq;Instl 3 92.78 1098 234.00 RS Inv:Emerg Growth 4 90.19 1055 226.17 PBHG:Large Cap 20 5 84.56 1078 229.03 Janus Olympus Fund 6 77.24 1061 227.03 Van Kampen Aggr Gro;A 7 76.70 1067 228.04 Janus Mercury 8 76.31 1057 226.35 PBHG:Sel Equity 9 76.21 1097 233.19 WM:Growth;A 10 74.77 1046 225.82 Berger New Generatn;Inv 11 73.31 1107 245.96 Janus Enterprise 12 72.28 1101 235.40 Janus Venture 13 72.22 1091 230.89 Fidelity Aggr Growth 14 70.56 1105 238.02 Janus Twenty 15 69.09 1090 230.83 Amer Cent:New Oppty. 16 67.64 1033 224.11 Morg Stan Sm Cap Gro;B 17 66.59 1102 235.96 Van Kampen Emerg Gro;A 18 65.67 1021 222.70 TCW Galileo:SC Gro;Instl. 19 64.87 1099 234.77 BlackRock:MdCp Gro;Instl. 20 64.44 1009 222.18 Average Fund Return 76.72 231.52

S&P 500 Return 24.75 210.50

Source: LipperAnalytic Servicesand BogleResearch Institute, ValleyForge, PA. 80Journal of Economic Perspectives

Exhibit 9 TheOdds of Success: Returns of Surviving Mutual Funds vs. S&P 500,1970 –2001

50 Number of Equity Funds 40 1970: 355 34 2001: 158 30 28 29 Nonsurvivors: 197 21 20 17 13 11 10 3 1 1 0 24% 23% 22% 21% 0 to 0 to 1% 2% 3% 4% or or less 21% 11% more

86 Losers 50 Market 22 Winners Equivalent

Source: Data fromLipper Analytic Servicesand TheVanguard Group.

Conclusion

Aslongas stock marketsexist, the collective judgment ofinvestors will some- timesmake mistakes. Undoubtedly, somemarket participants aredemonstrably less than rational.As aresult,pricing irregularities and evenpredictable patterns in stock returnscan appear overtime and evenpersist for short periods.Moreover, themarket cannot beperfectly ef Ž cient,or there would be no incentivefor professionalsto uncover the information that getsso quicklyre  ectedin market prices,a point stressedby Grossman and Stiglitz(1980). Undoubtedly, withthe passage oftimeand withthe increasing sophistication ofourdatabases and empir- icaltechniques, wewilldocument furtherapparent departuresfrom ef Ž ciency and furtherpatterns inthe development of stock returns. ButI suspect that theend resultwill not bean abandonment ofthe belief of many intheprofession that thestock marketis remarkably ef Ž cientin its utilization ofinformation. Periods such as 1999where “bubbles” seemto have existed,at least incertainsectors of the market, are fortunately the exception rather than therule. Moreover,whatever patterns orirrationalities in the pricing of individual stocks that have beendiscovered in a search ofhistoricalexperience are unlikely to persist and willnot provideinvestors with a methodto obtain extraordinaryreturns. If any $100bills are lying around thestock exchangesof theworld, they will not bethere for long. y Iwishto thank J. BradfordDe Long,Timothy Taylor and Michael Waldman for their extremely helpful observations.While they may notagree with all ofthe conclusions inthis paper,they have strengthened my argumentsin important ways. BurtonG. Malkiel81

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