Empirical Asset Pricing via Machine Learning: Evidence from the European Stock Market

60 Pages Posted: 27 Jul 2020 Last revised: 17 Aug 2021

See all articles by Wolfgang Drobetz

Wolfgang Drobetz

University of Hamburg

Tizian Otto

University of Hamburg

Date Written: July 1, 2020

Abstract

This paper evaluates the performance of machine learning methods in forecasting stock returns. Compared to a linear benchmark model, interactions and non-linear effects help improve predictive performance. But machine learning models must be adequately trained and tuned to overcome the high dimensionality issue and to avoid over-fitting. Across all machine learning methods, the most important predictors are based on price trends and fundamental signals from valuation ratios. However, the models exhibit disparities in statistical 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 size-able gains relative to our benchmark. Neural networks perform best, even after adjusting for risk and accounting for transaction costs. However, 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

JEL Classification: G11, G12, G14, G17

Suggested Citation

Drobetz, Wolfgang and Otto, Tizian, Empirical Asset Pricing via Machine Learning: Evidence from the European Stock Market (July 1, 2020). Available at SSRN: https://ssrn.com/abstract=3640631 or http://dx.doi.org/10.2139/ssrn.3640631

Wolfgang Drobetz (Contact Author)

University of Hamburg ( email )

Moorweidenstrasse 18
Hamburg, 20148
Germany

Tizian Otto

University of Hamburg ( email )

MoorweidenstraƟe 18
Hamburg, 20148
Germany

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