41 Pages Posted: 30 Jun 2016 Last revised: 16 Mar 2017
Date Written: March 15, 2017
We propose an estimate of expected returns that accounts for selective anomaly submission and publication, and apply our adjustment to a broad cross-section of anomaly hedge portfolio returns. Selection bias accounts for a modest 10 to 15% of the typical in-sample return. This small bias is due to fact that the dispersion of in-sample returns is nearly twice as large as the typical standard error, indicating a significant amount of variation in true returns. The bias is much smaller than the out-of-sample decline in anomaly returns, showing that that investors learn about mispricing from academic research. Estimations on simulated data show that our bias adjustment is robust.
Keywords: Stock return anomalies, selection bias, data mining, mispricing
JEL Classification: G10, G12
Suggested Citation: Suggested Citation
Chen, Andrew Y. and Zimmermann, Tom, Selection Bias and the Cross-Section of Expected Returns (March 15, 2017). Available at SSRN: https://ssrn.com/abstract=2802357