Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288215 
Year of Publication: 
2023
Citation: 
[Journal:] European Financial Management [ISSN:] 1468-036X [Volume:] 29 [Issue:] 5 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2023 [Pages:] 1657-1701
Publisher: 
Wiley, Hoboken, NJ
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.
Subjects: 
artificial intelligence
big data
machine learning
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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