Does Academic Research Destroy Stock Return Predictability?

• 成果类型: Article
• 署名作者: McLean, R. David; Pontiff, Jeffrey
• 署名单位: DePaul University; Boston College
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.12365
• 发表日期: 2016
• 页码: 5-32
• 关键词: cross-section; COSTLY ARBITRAGE; RISK; volatility; GROWTH; LIMITS

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.