Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288177 
Authors: 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Economic Surveys [ISSN:] 1467-6419 [Volume:] 38 [Issue:] 1 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2022 [Pages:] 27-56
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
The latest development in empirical Asset Pricing is the use of Machine Learning methods to address the problem of the factor zoo. These techniques offer great flexibility and prediction accuracy but require special care as they strongly depart from traditional Econometrics. We review and critically assess the most recent and relevant contributions in the literature grouping them into five categories defined by the Machine Learning (ML) approach they employ: regularization, dimension reduction, regression trees/random forest (RF), neural networks (NNs), and comparative analyses. We summarize the empirical findings with particular attention to their economic interpretation providing hints for futureĀ developments.
Subjects: 
empirical Asset Pricing
Machine Learning
risk premium
stochastic discount factor
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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