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A Model of Anomaly Discovery

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  • Qi Liu
  • Lei Lu
  • Bo Sun
  • Hongjun Yan

Abstract

We analyze a model of anomaly discovery. Consistent with existing evidence, we show that the discovery of an anomaly reduces its magnitude and increases its correlation with existing anomalies. One new prediction is that the discovery of an anomaly reduces the correlation between deciles 1 and 10 for that anomaly. Using data for 12 well-known anomalies, we find strong evidence consistent with this prediction. Moreover, the correlation between deciles 1 and 10 of an anomaly becomes correlated with the aggregate hedge-fund wealth volatility after the anomaly is discovered. Our model also sheds light on how to distinguish between risk- and mispricing-based anomalies.

Suggested Citation

  • Qi Liu & Lei Lu & Bo Sun & Hongjun Yan, 2015. "A Model of Anomaly Discovery," International Finance Discussion Papers 1128, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgif:1128
    DOI: 10.17016/IFDP.2015.1128
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    Cited by:

    1. Andrew Y Chen & Tom Zimmermann & Jeffrey Pontiff, 2020. "Publication Bias and the Cross-Section of Stock Returns," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(2), pages 249-289.

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    More about this item

    Keywords

    Anomaly; Arbitrage; Discovery; Arbitrageur-based asset pricing;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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    This paper has been announced in the following NEP Reports:

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