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

Jing Zhi Huang, Zhijian Huang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Article number1350016
JournalQuarterly Journal of Finance
Issue number3-4
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics
  • Strategy and Management


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