A number of investors read that paper and decide to go long stocks with high levels of Factor X and short stocks with low levels of Factor X. For example, let’s say I publish an academic paper that touts a new factor, Factor X, that nobody has used before. Once a factor has been published, investors are going to try to use that factor, thus arbitraging away its effect. Because of the statistical law of regression to the mean, factors that work best over one period are unlikely to work best over another period. A researcher will backtest several factors and publish the results for those that work best. In both cases, betting on a factor will prove unprofitable. One must start with the assumption of zero alpha - the assumption that the market is either quite efficient or quite random. The authors don’t explain why, though, so I thought I would. ![]() Why Out-of-Sample Results are Rarely Higher than In-Sample ResultsĪs anyone who does any backtesting can attest, in-sample alpha is always higher than out-of-sample alpha, and this is confirmed by the authors’ testing. Their conclusion: academic research into factors is totally valid. In addition, they find that higher in-sample alphas correspond to higher out-of-sample alphas. This, together with considering all factors simultaneously, naturally lowers the p-value threshold for factor success.Īs a result, their overall out-of-sample success rate for factors tested in academic papers is a massive 85%. They use a Bayesian approach to factor evaluation based on the prior assumption that alpha is zero.(This is, in my opinion, exactly the way it should be measured.) They measure success for a factor by looking at its alpha rather than its raw return.They exclude factors that the original researchers found insignificant (or at least they say they do, but they end up including a few anyway).(This makes sense especially if you're designing a system that works for large caps.) They use value-weighted (cap-weighted) results, but winsorize at the 80th percentile of the NYSE, so that massive firms don’t overwhelm the rest.(This is one of the signal merits of this study.) They use terciles rather than deciles (earlier papers claimed that a factor didn’t work if the top tenth of stocks ranked by the factor failed to beat the bottom tenth these authors say that if the top third beats the bottom third, it works this is a somewhat more forgiving and broader measure).(This makes sense to me, as it provides an even playing ground for factors that have short and long look-back periods.) They use one-month holding periods rather than six- or twelve-month periods.What do these researchers do differently than the authors of the you-can’t-replicate-this papers? The Difference Between This Paper and Previous Ones In this article I’m going to summarize their paper and talk about the various factors that they tested, what factors they didn’t test, their factor classification system, some things about their research that remain vague, and some useful conclusions about the results. At the same time, a non-trivial minority of factors fail to replicate in our data, but the overall evidence is much less disastrous than some people suggest. We find that the majority of factors do replicate, do survive joint modeling of all factors, do hold up out-of-sample, are strengthened (not weakened) by the large number of observed factors, are further strengthened by global evidence, and the number of factors can be understood as multiple versions of a smaller number of themes. Our findings challenge the dire view of finance research. This new paper appears to prove that factor-based investing actually works. ![]() A few months ago three researchers published an astonishingly ambitious and compendious paper called “Is There a Replication Crisis in Finance?” (Their names are Theis Jensen, Bryan Kelly, and Lasse Pedersen two are at the Copenhagen Business School, one is at Yale, and two also work for AQR Capital Management.) It attempts to refute several recent papers that have said that there is indeed a replication crisis in finance: that researchers today are unable to replicate the findings of earlier researchers who claimed that going long and/or short certain factors results in improved returns.
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