Machine Learning Methods in Finance: Recent Applications and Prospects

81 Pages Posted: 13 Dec 2022 Last revised: 24 Jan 2023

See all articles by Daniel Hoang

Daniel Hoang

University of Hohenheim

Kevin Wiegratz

Karlsruhe Institute of Technology

Date Written: January 24, 2023

Abstract

We study how researchers can apply machine learning (ML) methods in finance. We first establish that the two major categories of ML (supervised and unsupervised learning) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of research on ML in finance and identify three archetypes of applications: (i) the construction of superior and novel measures, (ii) the reduction of prediction error, and (iii) the extension of the standard econometric toolset. With this taxonomy, we give an outlook on potential future directions for both researchers and practitioners. Our results suggest many benefits of ML methods compared to traditional approaches and indicate that ML holds great potential for future research in finance.

Keywords: Machine Learning, Artificial Intelligence, Big Data

JEL Classification: C45, G00

Suggested Citation

Hoang, Daniel and Wiegratz, Kevin, Machine Learning Methods in Finance: Recent Applications and Prospects (January 24, 2023). Available at SSRN: https://ssrn.com/abstract=4293977 or http://dx.doi.org/10.2139/ssrn.4293977

Daniel Hoang (Contact Author)

University of Hohenheim

Fruwirthstr. 49
Stuttgart, 70599
Germany

Kevin Wiegratz

Karlsruhe Institute of Technology ( email )

Kaiserstraße 12
Karlsruhe, Baden Württemberg 76131
Germany

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