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Enhancing stock market anomalies with machine learning

Author

Listed:
  • Vitor Azevedo

    (Technical University Kaiserslautern)

  • Christopher Hoegner

    (McKinsey & Company)

Abstract

We examine the predictability of 299 capital market anomalies enhanced by 30 machine learning approaches and over 250 models in a dataset with more than 500 million firm-month anomaly observations. We find significant monthly (out-of-sample) returns of around 1.8–2.0%, and over 80% of the models yield returns equal to or larger than our linearly constructed baseline factor. For the best performing models, the risk-adjusted returns are significant across alternative asset pricing models, considering transaction costs with round-trip costs of up to 2% and including only anomalies after publication. Our results indicate that non-linear models can reveal market inefficiencies (mispricing) that are hard to conciliate with risk-based explanations.

Suggested Citation

  • Vitor Azevedo & Christopher Hoegner, 2023. "Enhancing stock market anomalies with machine learning," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 195-230, January.
    • Handle: RePEc:kap:rqfnac:v:60:y:2023:i:1:d:10.1007_s11156-022-01099-z
    DOI: 10.1007/s11156-022-01099-z
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    Cited by:

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    2. Vitor Azevedo & Georg Sebastian Kaiser & Sebastian Mueller, 2023. "Stock market anomalies and machine learning across the globe," Journal of Asset Management, Palgrave Macmillan, vol. 24(5), pages 419-441, September.

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    More about this item

    Keywords

    Anomalies; Machine learning models; Efficient market hypothesis; Asset pricing models;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G29 - Financial Economics - - Financial Institutions and Services - - - Other
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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