Machine Learning for Stock Selection. Issue 3 (1st July 2019)

• Record Type: Journal Article
• Authors: Rasekhschaffe, Keywan Christian; Jones, Robert C.
• Is Part Of: Financial analysts journal. Volume 75:Issue 3(2019)
• Page Start: 70
• Page End: 88
• Publication Date: 2019-07-01
• Subjects: Investment analysis -- Periodicals; Investment analysis; HW_FM; Periodicals; Electronic journals; 332.6
• DOI: 10.1080/0015198X.2019.1596678
• Languages: English
• ISSNs: 0015-198X

Abstract

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. Abstract : Disclosure: The authors report no conflicts of interest. Editor's Note Submitted 19 July 2018 Accepted 30 January 2019 by Stephen J. Brown