We study the out-of-sample and post-publication return-predictability of 97 variables that
academic studies show to predict cross-sectional stock returns. Portfolio returns are 26% lower
out-of-sample and 58% lower post-publication. The out-of-sample decline is an upper bound
estimate of data mining effects. We estimate a 32% (58% - 26%) lower return from publication informed trading. Post-publication declines are greater for predictors with higher in-sample
returns, and returns are higher for portfolios concentrated in stocks with high idiosyncratic risk
and low liquidity. Predictor portfolios exhibit post-publication increases in correlations with
other published-predictor portfolios. Our findings suggest investors learn about mispricing from
academic publications.
Finance research has uncovered many cross-sectional relations between predetermined variables
and future stock returns. Beyond historical curiosity, these relations are relevant to the extent
they provide insight into the future. Whether or not the typical relation continues outside of a
study’s original sample is an open question, the answer to which can shed light on why crosssectional return predictability is observed in the first place.1 Although several papers note
whether a specific cross-sectional relation continues, no study compares in-sample returns, postsample returns, and post-publication returns among a large sample of predictors. Moreover,
previous studies produce contradictory messages. As examples, Jegadeesh and Titman (2001)
show that the relative returns to high-momentum stocks increased after the publication of their
1993 paper, while Schwert (2003) argues that since the publication of the value and size effects,
index funds based on these variables fail to generate alpha.2
In this paper, we synthesize information from 97 predictors that have been shown to
explain cross-sectional stock returns in peer-reviewed finance, accounting, and economics
journals. Our goal is to better understand what happens to return-predictability outside of a
study’s sample period. We compare each predictor’s returns over three distinct periods: (i) the
original study’s sample; (ii) after the original sample but before publication; and (iii) postpublication. Previous studies attribute cross-sectional return predictability to statistical biases,
rational pricing, and mispricing. By comparing return-predictability between these three periods,
we can better differentiate between these explanations.
Statistical Bias. If return-predictability in published studies is solely the result of statistical
biases, then predictability should disappear out of sample. We use the term “statistical biases” to
describe a broad array of biases that are inherent to research. Fama (1991) addresses this issue
when he notes that: “With clever researchers on both sides of the efficiency fence, rummaging for forecasting variables, we are sure to find instances of ‘reliable’ return predictability that are
in fact spurious.” To the extent that the results of the studies in our sample are caused by such
biases, we should observe a decline in return-predictability out-of-sample.
Rational Expectations versus Mispricing. Differences between in-sample and postpublication returns are determined by both statistical biases and the extent to which investors
learn from the publication. Cochrane (1999) explains that if predictability reflects risk it is likely
to persist: “Even if the opportunity is widely publicized, investors will not change their portfolio
decisions, and the relatively high average return will remain.” Cochrane’s logic follows Muth’s
(1961) rational expectations hypothesis, and thus can be broadened to non-risk models such as
Amihud and Mendelson’s (1986) transaction-based model and Brennan’s (1970) tax-based
model. If return predictability entirely reflects rational expectations, then publication will not
convey information that causes a rational agent to behave differently. Thus, once the impact of
statistical bias is removed, pre- and post-publication return-predictability should equate.
If return-predictability reflects mispricing and publication causes sophisticated investors to
learn about and trade against the mispricing, then we expect the returns associated with a
predictor to disappear or at least decay after the paper is published.4 Decay, as opposed to
disappearance, will occur if impediments prevent arbitrage from fully eliminating mispricing.
Examples of such impediments include systematic noise trader risk (Delong, Shleifer, Summers,
and Waldman (1990)) and idiosyncratic risk and transaction costs (Pontiff (1996, 2006)). These
effects can be worsened by the principal-agent relations that exist between investors and
investment professionals, Shleifer and Vishny (1997))
Findings. We conduct our analysis with 97 different characteristics from 80 different
studies, using long-short portfolio strategies that buy and sell extreme quintiles that are based on each predictor. The average predictor’s long-short return declines by 26% out-of-sample. This
26% estimate is an upper bound on the effect of statistical biases, since some traders are likely to
learn about the predictor before publication, and their trading will cause the return decay to be
greater than the pure decay from statistical bias.
The average predictor’s long-short return shrinks 58% post-publication. Combining this
finding with an estimated statistical bias of 26% implies a lower bound on the publication effect
of about 32%. We can reject the hypothesis that return-predictability disappears entirely, and we
can also reject the hypothesis that post-publication return-predictability does not change. This
post-publication decline is robust to a general time trend, to time indicators used by other
authors, and to time fixed effects.
The decay in portfolio returns is larger for predictor portfolios with higher in-sample
returns and higher in-sample t-statistics. We also find evidence that decay is larger for predictors
that can be constructed with only price and trading data, and therefore likely to represent
violations of weak form market efficiency. Post-publication returns are lower for predictors that
are less costly to arbitrage; i.e., predictor portfolios concentrated in liquid stocks and low
idiosyncratic risk stocks. Our findings are consistent with mispricing accounting for some or all
of the original return predictability, and investors learning about this mispricing.
We further investigate the effects of publication by studying traits that reflect trading
activity. We find that stocks within the predictor portfolios have post-publication increases in
trading volume, and that the difference in short interest between stocks in the short and long
sides of each portfolio increases after publication. These findings are consistent with the idea that
academic research draws attention to predictors.
Publication has an effect on correlations between predictor portfolio returns. Yet-to-be published predictor portfolios returns are correlated, and after a predictor is featured in a
publication its portfolio return correlation with other yet-to-be-published predictor portfolios
decreases, while its correlation with other already-published predictor portfolio returns increases.
One interpretation of this finding is that some portion of predictor portfolio returns is the result
of mispricing and mispricing has a common source; this is why in-sample predictor portfolios
returns are correlated. This interpretation is consistent with the irrational comovement models
proposed in Lee, Shleifer, and Thaler (1991) and Barberis and Shleifer (2003). Publication could
then cause more arbitrageurs to trade on the predictor, which causes predictor portfolios to
become more correlated with already-published predictor portfolios that are also p