Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287592 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Asset Management [ISSN:] 1479-179X [Volume:] 22 [Issue:] 7 [Publisher:] Palgrave Macmillan UK [Place:] London [Year:] 2021 [Pages:] 507-538
Publisher: 
Palgrave Macmillan UK, London
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.
Subjects: 
Stock return prediction
Machine learning
Active trading strategy
JEL: 
G11
G12
G14
G17
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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