Real-Time Profitability of Published Anomalies: An Out-of-Sample Test

Empirical evidence on the out-of-sample performance of asset-pricing anomalies is mixed so far and arguably is often subject to data-snooping bias. This paper proposes a method that can significantly reduce this bias. Specifically, we consider a long-only strategy that involves only published anomalies and non-forward-looking filters and that each year recursively picks the best past-performer among such anomalies over a given training period. We find that this strategy can outperform the equity market even after transaction costs. Overall, our results suggest that published anomalies persist even after controlling for data-snooping bias.

Keywords: Data-snooping Bias, Asset-pricing Anomalies, Out-of-sample Test, Published Anomalies

JEL Classification: G11, G14, D83

Suggested Citation: Suggested Citation

Huang, Jing-Zhi Jay and Huang, Zhijian, Real-Time Profitability of Published Anomalies: An Out-of-Sample Test (March 12, 2014). Quarterly Journal of Finance, Volume 3, Issue 3n4, 2013, Available at SSRN: https://ssrn.com/abstract=1571706 or http://dx.doi.org/10.2139/ssrn.1571706