THE JOURNAL OF FINANCE· VOL. LXXI, NO. 1 · FEBRUARY 2016

學術研究摧毀了股票收益的可預測性嗎？

作者：R. David McLean, Jeffrey Pontiff

摘要：我們選取了97個被證明可以預測橫截面股票收益的變量，研究它們的樣本外和發表後收益的可預測性。樣本外和發表後組合收益分别（比樣本）低26%和58%。樣本外收益的下降是對數據挖掘效應上限的刻畫。我們估計，發表令（投資者）獲知預測因子，然後進行交易，此時收益下降了32%（58%–26%）。對于樣本内收益更高的預測因子，其發表後收益下降得更多，且高異質風險和低流動性的股票組合擁有更高的回報。預測因子組合在發表後與其他已發表預測因子組合的相關性上升。我們的發現指出，投資者從學術發表中了解到了錯誤定價信息。

關鍵詞：學術研究，股票收益，可預測性

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

原文鍊接：http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2156623

翻譯：任兆月