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Published in: Financial Markets and Portfolio Management 1/2019

26-02-2019

Machine learning in empirical asset pricing

Author: Alois Weigand

Published in: Financial Markets and Portfolio Management | Issue 1/2019

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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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Footnotes
1
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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Metadata
Title
Machine learning in empirical asset pricing
Author
Alois Weigand
Publication date
26-02-2019
Publisher
Springer US
Published in
Financial Markets and Portfolio Management / Issue 1/2019
Print ISSN: 1934-4554
Electronic ISSN: 2373-8529
DOI
https://doi.org/10.1007/s11408-019-00326-3

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