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.
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Notes
The author refers the interested reader to Gu et al. (2018) who provide a detailed description of machine learning tools for empirical asset pricing. These explanations start from scratch and cover the statistical model specification as well as programming guidelines of different methods. Furthermore, Dey (2016) reviews and explains different machine learning algorithms in detail.
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The author thanks Prof. Dr. Manuel Ammann as well as Prof. Dr. Markus Schmid for their constructive and insightful comments.
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Weigand, A. Machine learning in empirical asset pricing. Financ Mark Portf Manag 33, 93–104 (2019). https://doi.org/10.1007/s11408-019-00326-3
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DOI: https://doi.org/10.1007/s11408-019-00326-3