Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/267689 
Year of Publication: 
2022
Series/Report no.: 
KIT Working Paper Series in Economics No. 158
Publisher: 
Karlsruher Institut für Technologie (KIT), Institut für Volkswirtschaftslehre (ECON), Karlsruhe
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.
Subjects: 
Machine Learning
Artificial Intelligence
Big Data
JEL: 
C45
G00
Document Type: 
Working Paper

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