Machine learning techniques for cross-sectional equity returns’ prediction

We compare the performance of the linear regression model, which is the current standard in science and practice for cross-sectional stock return forecasting, with that of machine learning methods, i.e., penalized linear models, support vector regression, random forests, gradient boosted trees and n...

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Bibliographic Details
Published in:OR Spectrum 2023-03, Vol.45 (1), p.289-323
Main Authors: Fieberg, Christian, Metko, Daniel, Poddig, Thorsten, Loy, Thomas
Format: Article
Language:English
Subjects:
Benchmarks
Business and Management
Calculus of Variations and Optimal Control
Optimization
Cash flow statements
Cross-sections
Equity
Machine learning
Macroeconomics
Missing data
Neural networks
Operations Research/Decision Theory
Original Article
Rates of return
Regression analysis
Regression models
Research design
Securities trading
Support vector machines
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Summary:We compare the performance of the linear regression model, which is the current standard in science and practice for cross-sectional stock return forecasting, with that of machine learning methods, i.e., penalized linear models, support vector regression, random forests, gradient boosted trees and neural networks. Our analysis is based on monthly data on nearly 12,000 individual stocks from 16 European economies over almost 30 years from 1990 to 2019. We find that the prediction of stock returns can be decisively improved through machine learning methods. The outperformance of individual (combined) machine learning models over the benchmark model is approximately 0.6% (0.7%) per month for the full cross-section of stocks. Furthermore, we find no model breakdowns, which suggests that investors do not incur additional risk from using machine learning methods compared to the traditional benchmark approach. Additionally, the superior performance of machine learning models is not due to substantially higher portfolio turnover. Further analyses suggest that machine learning models generate their added value particularly in bear markets when the average investor tends to lose money. Our results indicate that future research and practice should make more intensive use of machine learning techniques with respect to stock return prediction.
ISSN:0171-6468
1436-6304
DOI:10.1007/s00291-022-00693-w