Machine Learning for Stock Selection

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.