Machine Learning for Stock Selection

Keywan Christian Rasekhschaffe and Robert C. Jones

Financial Analysts Journal, 2019, vol. 75, issue 3, 70-88

Abstract: Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can uncover subtle, contextual, and nonlinear relationships, overfitting poses a major challenge when one is trying to extract signals from noisy historical data. We describe some of the basic concepts of machine learning and provide a simple example of how investors can use machine learning techniques to forecast the cross-section of stock returns while limiting the risk of overfitting. Disclosure: The authors report no conflicts of interest. Editor’s Note Submitted 19 July 2018Accepted 30 January 2019 by Stephen J. Brown

Date: 2019
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DOI: 10.1080/0015198X.2019.1596678

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