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 non-linear relationships, overfitting poses a major challenge when trying to extract signals from noisy historical data. In this article, we describe some of the basic concepts surrounding machine leaning 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.

Keywords: Machine Learning, Return Prediction, Cross-Section of Returns, Gradient Boosting, SVM, AdaBoost, (Deep) Neural Networks, Feature Engineering, Fintech

JEL Classification: G10, G11, G14, C14, C21, C22, C23, C58