.png){kind=icon}
返回
Does Academic Research Destroy Stock Return Predictability?
.png){kind=icon}
OA
AI
DOI：10.1111/jofi.12365.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
CROSS-SECTION
COSTLY ARBITRAGE
RISK
VOLATILITY
GROWTH
LIMITS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.5
论文数:
4.0K
被引数:
5.0W
机构
引用论文
Should Investors Follow the Prophets or the Bears? Evidence on the Use of Public Information by Analysts and Short Sellers.png)
ACCOUNTING REVIEW
ACCOUNTING REVIEWIF4.4
PREAI
分享收藏
查看更多内容