Machine Learning for Stock Selection
Financial Analysts Journal, vol. 75, no. 3 (Third Quarter 2019)
35 Pages Posted: 4 Mar 2019 Last revised: 9 Aug 2019
Date Written: February 8, 2019
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
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