Effect that the stock market especially stocks of

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effect, that the stock marketespecially stocks of small companieswill generateextraordinary returns during thefirstfive days of January. What will investors do?They will buy on the last day of December and sell on January 5. But then investorsfind that the market rallied on the last day of December, and so they will need tobegin to buy on the next-to-last day of December; and because there is so muchprofit takingon January 5, investors will have to sell on January 4 to takeadvantage of this effect. Thus, to beat the gun, investors will have to be buyingExhibit 3Reversion to the Mean: Relative Performance ofValuevs.GrowthMutualFunds, 1937June 20020.70.80.9GrowthOutperformingValueOutperformingAverage Annual ReturnGrowth:Value:10.61%10.57%1.01.10.637404346495255586164677073767982858891949700Relative ReturnSource:Lipper Analytic Services and Bogle Research Institute Valley Forge, Pennsylvania.Note:The exhibit shows the cumulative value of one dollar invested in the averagevaluefunddivided by the same statistic calculated for the averagegrowthfund.The Efficient Market Hypothesis and Its Critics71
earlier and earlier in December and selling earlier and earlier in January so thateventually the pattern will self-destruct. Any truly repetitive and exploitable patternthat can be discovered in the stock market and can be arbitraged away willself-destruct. Indeed, the January effect became undependable after it receivedconsiderable publicity.Similarly, suppose there is a general tendency for stock prices to underreact tocertain new events, leading to abnormal returns to investors who exploit the lack offull immediate adjustment (DeBondt and Thaler, 1995; Campbell, Lo and Mac-Kinlay, 1977).Quantitativeinvestment managers will then develop trading strat-egies to exploit the pattern. Indeed, the more potentially profitable a discoverablepattern is, the less likely it is to survive.Many of the predictable patterns that have been discovered may simply be theresult of data mining. The ease of experimenting withfinancial databanks of almostevery conceivable dimension makes it quite likely that investigators willfind someseemingly significant but wholly spurious correlation betweenfinancial variables oramongfinancial and nonfinancial data sets. Given enough time and massaging ofdata series, it is possible to tease almost any pattern out of most data sets. Moreover,the published literature is likely to be biased in favor of reporting such results.Significant effects are likely to be published in professional journals while negativeresults, or boring confirmations of previousfindings, are relegated to thefiledrawer or discarded. Data-mining problems are unique to nonexperimental sci-ences, such as economics, which rely on statistical analysis for their insights andcannot test hypotheses by running repeated controlled experiments.

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