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

• 成果类型: Article
• 署名作者: Yan, Xuemin (Sterling); Zheng, Lingling
• 署名单位: University of Missouri System; University of Missouri Columbia; Renmin University of China
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhx001
• 发表日期: 2017
• 页码: 1382
• 关键词: momentum; anomalies; INFORMATION; winners

摘要

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.