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

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Asset Management ; ISSN: 1479-179X ; Volume: 22 ; Year: 2021 ; Issue: 7 ; Pages: 507-538 ; London: Palgrave Macmillan UK

Classification and Topics

• Classification: Wirtschaft ; Portfolio Choice; Investment Decisions ; Asset Pricing; Trading Volume; Bond Interest Rates ; Information and Market Efficiency; Event Studies; Insider Trading ; Financial Forecasting and Simulation
• Subject: Stock return prediction ; Machine learning ; Active trading strategy

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Drobetz, Wolfgang ; Otto, Tizian
• Event: Veröffentlichung
• (who): Palgrave Macmillan UK
• (where): London
• (when): 2021

Further information

• DOI: doi:10.1057/s41260-021-00237-x
• Last update: 07.03.2025, 7:41 PM CET