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.
About the Author(s)
Keywan Christian Rasekhschaffe is a senior quantitative strategist at Gresham Investment Management, LLC, in New York City.
Robert C. Jones, CFA, is chair and chief investment officer of System Two Advisors, LP, in Summit, New Jersey.