Does Academic Research Destroy Stock Return Predictability?

被引:642
作者
McLean, R. David [1 ]
Pontiff, Jeffrey [2 ]
机构
[1] De Paul Univ, Chicago, IL 60614 USA
[2] Boston Coll, Chestnut Hill, MA 02167 USA
关键词
CROSS-SECTION; COSTLY ARBITRAGE; RISK; VOLATILITY; GROWTH; LIMITS;
D O I
10.1111/jofi.12365
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
We study the out-of-sample and post-publication return predictability of 97 variables shown 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 that investors learn about mispricing from academic publications.
引用
收藏
页码:5 / 32
页数:28
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