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Machine Learning in Empirical Asset Pricing
Journal
Financial Markets and Portfolio Management
ISSN
1934-4554
ISSN-Digital
2373-8529
Type
journal article
Date Issued
2019-03
Author(s)
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.
Language
English
Keywords
Machine learning
Big data
Empirical asset pricing JEL Classifications G12
G13
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Quantitative Economic Methods
Refereed
Yes
Publisher
Springer Nature
Volume
33
Number
1
Start page
93
End page
104
Pages
12
Subject(s)
Division(s)
Eprints ID
257046