Machine Learning for Stock Selection

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.

# Stock Market Forecasting Methods # Decision Sciences # Financial Markets and Investment Strategies # Economics, Econometrics and Finance # Financial Analysis and Corporate Governance # Business, Management and Accounting

Keywan Christian Rasekhschaffe, Robert C. Jones (2019). "Machine Learning for Stock Selection". Financial Analysts Journal. https://doi.org/10.1080/0015198x.2019.1596678