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Financial Markets and Portfolio Management

, Volume 33, Issue 1, pp 93–104 | Cite as

Machine learning in empirical asset pricing

  • Alois WeigandEmail author
Article

Abstract

The tremendous speedup in computing in recent years, the low data storage costs of today, the availability of “big data” as well as the broad range of free open-source software, have created a renaissance in the application of machine learning techniques in science. However, this new wave of research is not limited to computer science or software engineering anymore. Among others, machine learning tools are now used in financial problem settings as well. Therefore, this paper mentions a specific definition of machine learning in an asset pricing context and elaborates on the usefulness of machine learning in this context. Most importantly, the literature review gives the reader a theoretical overview of the most recent academic studies in empirical asset pricing that employ machine learning techniques. Overall, the paper concludes that machine learning can offer benefits for future research. However, researchers should be critical about these methodologies as machine learning has its pitfalls and is relatively new to asset pricing.

Keywords

Machine learning Big data Empirical asset pricing 

JEL Classifications

G12 G13 

Notes

Acknowledgements

The author thanks Prof. Dr. Manuel Ammann as well as Prof. Dr. Markus Schmid for their constructive and insightful comments.

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Copyright information

© Swiss Society for Financial Market Research 2019

Authors and Affiliations

  1. 1.Swiss Institute of Banking and Finance (s/bf)University of St. GallenSt. GallenSwitzerland

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