Stock market anomalies and machine learning across the globe

Vitor Azevedo (), Georg Sebastian Kaiser () and Sebastian Mueller ()
Additional contact information
Vitor Azevedo: Department of Financial Management
Georg Sebastian Kaiser: Roland Berger
Sebastian Mueller: Technical University of Munich, TUM School of Management, Center for Digital Transformation, Campus Heilbronn

Journal of Asset Management, 2023, vol. 24, issue 5, No 6, 419-441

Abstract: Abstract We identify the characteristics and specifications that drive the out-of-sample performance of machine-learning models across an international data sample of nearly 1.9 billion stock-month-anomaly observations from 1980 to 2019. We demonstrate significant monthly value-weighted (long-short) returns of around 1.8–2.2%, and a vast majority of tested models outperform a linear combination of predictors (our baseline factor benchmark) by a substantial margin. Composite predictors based on machine learning have long-short portfolio returns that remain significant even with transaction costs up to 300 basis points. By comparing 46 variations of machine-learning models, we find that the models with the highest return predictability apply a feed-forward neural network or composite predictors, with extending rolling windows, including elastic net as a feature reduction, and using percent ranked returns as a target. The results of our nonlinear models are significant across several classical asset pricing models and uncover market inefficiencies that challenge current asset pricing theories in international markets.

Keywords: International stock market; Anomalies; Machines learning models; Market efficiency; Publication impact (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 (3)

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DOI: 10.1057/s41260-023-00318-z

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