Real-Time Profitability of Published Anomalies: An Out-of-Sample Test
Pennsylvania State University - University Park - Department of Finance
Zhijian (James) Huang
University of Wisconsin - Milwaukee - Department of Finance
May 30, 2013
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.
Number of Pages in PDF File: 39
Keywords: Data-snooping Bias, Asset-pricing Anomalies, Out-of-sample Test, Published Anomalies
JEL Classification: G11, G14, D83working papers series
Date posted: March 17, 2010 ; Last revised: May 31, 2013
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