Can machine learning be helpful to asset management – and if so, how? Asset markets fundamentally differ from many of the environments in which machine learning has enjoyed success, and research into machine learning for asset management is just beginning.
In this Alternative Thinking, we discuss the crucial points for understanding the current state of machine learning in the practice of asset management:
- How machine learning attempts to solve difficult problems and how it differs from traditional computer programming.
- Why finance poses unique challenges even for the most powerful machine learning programs
- Early research evidence hints that machine learning tools can potentially improve investment portfolios
The application of machine learning techniques is a natural evolution for investment research, and one that will continue to be explored.
About the Portfolio Solutions Group
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We thank Bryan Kelly, Ronen Israel and Tobias Moskowitz for their work on this paper. We also thank Gregor Andrade, Pete Hecht, Antti Ilmanen, Michael Katz, Lasse Pedersen and Dan Villalon for their helpful comments.
The information contained herein is only as current as of the date indicated, and may be superseded by subsequent market events or for other reasons. The views and opinions expressed herein are those of the author and do not necessarily reflect the views of AQR Capital Management, LLC, its affiliates or its employees. This information is not intended to, and does not relate specifically to any investment strategy or product that AQR offers. It is being provided merely to provide a framework to assist in the implementation of an investor’s own analysis and an investor’s own view on the topic discussed herein. Past performance is not a guarantee of future results.