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

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 crosssectional
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.