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

Jing-Zhi Huang () and Zhijian Huang ()
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Jing-Zhi Huang: Smeal College of Business, Pennsylvania State University, University Park, PA 16802, USA
Zhijian Huang: Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
Authors registered in the RePEc Author Service: Jingzhi Huang

Quarterly Journal of Finance (QJF), 2013, vol. 03, issue 03n04, 1-33

Abstract: 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: Published anomalies; data-snooping bias; asset-pricing anomalies; out-of-sample test; JEL Classification: G11; JEL Classification: G14; JEL Classification: D83 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S201013921350016X

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