Empirical asset pricing via machine learning: evidence from the European stock market

Wolfgang Drobetz and Tizian Otto ()
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Tizian Otto: University of Hamburg

Journal of Asset Management, 2021, vol. 22, issue 7, No 1, 507-538

Abstract: Abstract This paper evaluates the predictive performance of machine learning methods in forecasting European stock returns. Compared to a linear benchmark model, interactions and nonlinear effects help improve the predictive performance. But machine learning models must be adequately trained and tuned to overcome the high dimensionality problem and to avoid overfitting. Across all machine learning methods, the most important predictors are based on price trends and fundamental signals from valuation ratios. However, the models exhibit substantial variation in statistical predictive performance that translate into pronounced differences in economic profitability. The return and risk measures of long-only trading strategies indicate that machine learning models produce sizeable gains relative to our benchmark. Neural networks perform best, also after accounting for transaction costs. A classification-based portfolio formation, utilizing a support vector machine that avoids estimating stock-level expected returns, performs even better than the neural network architecture.

Keywords: Stock return prediction; Machine learning; Active trading strategy (search for similar items in EconPapers)
JEL-codes: G11 G12 G14 G17 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (15)

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DOI: 10.1057/s41260-021-00237-x

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