Machine learning methods in finance: Recent applications and prospects

Daniel Hoang and Kevin Wiegratz

No 158, Working Paper Series in Economics from Karlsruhe Institute of Technology (KIT), Department of Economics and Management

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 large 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 (search for similar items in EconPapers)
JEL-codes: C45 G00 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-fmk, nep-isf and nep-pay
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:kitwps:158

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