作者：Yan, Xuemin (Sterling); Zheng, Lingling
作者单位：University of Missouri System; University of Missouri Columbia; Renmin University of China
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 anoma...
