REVIEW OF FINANCIAL STUDIES·DOI: https://doi.org/10.1093/rfs/hhx001·Published: 06 March 2017

基本面分析和股票的横截面收益：数据挖掘方法

作者：Xuemin (Sterling) Yan (University of Missouri), Lingling Zheng (Renmin University of China)

摘要：我们从财务报表中构建了一个超过18000个基本信号的“宇宙”，并使用bootstrap方法来评估数据挖掘对基于基本面的异象的影响。我们发现，即使在考虑数据挖掘之后，许多基本信号也是股票横截面收益的重要预测指标。这种预测能力在高情绪时期和在套利限制较大的股票中更加明显。我们的证据表明，基于基本面的异象，包括在这项研究中新发现的异常，不能归因于随机机会，并且能由错误定价更好地解释。我们的方法是通用的，我们也将其应用于过去基于回报的异象。

Fundamental Analysis and the Cross-Section of Stock Returns: A Data-Mining Approach

Xuemin (Sterling) Yan (University of Missouri), Lingling Zheng (Renmin University of China)

ABSTRACT

We construct a “universe” of over 18,000 fundamental signals from financial statements and use a bootstrap approach to evaluate the impact of data mining on fundamental-based anomalies. We find that many fundamental signals are significant predictors of cross-sectional stock returns even after accounting for data mining. This predictive ability is more pronounced following high-sentiment periods and among stocks with greater limits to arbitrage. Our evidence suggests that fundamental-based anomalies, including those newly discovered in this study, cannot be attributed to random chance, and they are better explained by mispricing. Our approach is general and we also apply it to past return–based anomalies.

原文链接：

https://academic.oup.com/rfs/article-abstract/doi/10.1093/rfs/hhx001/2908895/Fundamental-Analysis-and-the-Cross-Section-of?redirectedFrom=fulltext

翻译：何杉