Aurora Borealis
13 May 2019 Financial Analysts Journal Volume 75, Issue 3

Machine Learning for Stock Selection

  1. Keywan Christian Rasekhschaffe
  2. Robert C. Jones
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.
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Publisher Information
Routledge doi.org/10.1080/0015198X.2019.1596678 ISSN/ISBN: 0015-198X

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