Enhancing stock market anomalies with machine learning

Vitor Azevedo () and Christopher Hoegner ()
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Vitor Azevedo: Technical University Kaiserslautern
Christopher Hoegner: McKinsey & Company

Review of Quantitative Finance and Accounting, 2023, vol. 60, issue 1, No 6, 195-230

Abstract: 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.

Keywords: Anomalies; Machine learning models; Efficient market hypothesis; Asset pricing models (search for similar items in EconPapers)
JEL-codes: G12 G29 M41 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11156-022-01099-z

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