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Does it pay to follow anomalies research? Machine learning approach with international evidence

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  • Tobek, Ondrej
  • Hronec, Martin

Abstract

We study out-of-sample returns on 153 anomalies in equities documented in the academic literature. We show that machine learning techniques that aggregate all the anomalies into one mispricing signal are profitable around the globe and survive on a liquid universe of stocks. We investigate the value of international evidence for selection of quantitative strategies that outperform out-of-sample. Past performance of quantitative strategies in regions other than the United States does not help to pick out-of-sample winning strategies in the U.S. Past evidence from the U.S., however, captures most of the return predictability outside the U.S.

Suggested Citation

  • Tobek, Ondrej & Hronec, Martin, 2021. "Does it pay to follow anomalies research? Machine learning approach with international evidence," Journal of Financial Markets, Elsevier, vol. 56(C).
  • Handle: RePEc:eee:finmar:v:56:y:2021:i:c:s1386418120300574
    DOI: 10.1016/j.finmar.2020.100588
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    Keywords

    Anomalies; Machine learning; International finance;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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