THE JOURNAL OF FINANCE· VOL. LXXI, NO. 1 · FEBRUARY 2016
学术研究摧毁了股票收益的可预测性吗？
作者🙅🏿♀️：R. David McLean, Jeffrey Pontiff
摘要：我们选取了97个被证明可以预测横截面股票收益的变量，研究它们的样本外和发表后收益的可预测性。样本外和发表后组合收益分别（比样本）低26%和58%。样本外收益的下降是对数据挖掘效应上限的刻画。我们估计，发表令（投资者）获知预测因子，然后进行交易🧑🏼🔬，此时收益下降了32%（58%–26%）。对于样本内收益更高的预测因子⏳，其发表后收益下降得更多📰，且高异质风险和低流动性的股票组合拥有更高的回报🚶♂️➡️。预测因子组合在发表后与其他已发表预测因子组合的相关性上升🚶🏻♀️➡️。我们的发现指出，投资者从学术发表中了解到了错误定价信息。
关键词：学术研究📅，股票收益，可预测性
Does Academic Research Destroy Stock Return Predictability?
R. David McLean, Jeffrey Pontiff
ABSTRACT
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.
Keywords: academic research, stock return, predictability
原文链接🚶🏻♂️➡️：http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2156623
翻译：任兆月
