Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/275012 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 15 [Issue:] 11 [Article No.:] 535 [Year:] 2022 [Pages:] 1-17
Publisher: 
MDPI, Basel
Abstract: 
Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better suited for, problems of prediction. While some ML methods have been applied in econometrics for decades, their success in prediction has been limited, and examples of this abound in the asset pricing literature. In recent years, the ML literature has advanced new, more efficient, computation methods for regularization, modeling nonlinearity, and improved out-of-sample prediction. This article conducted a comprehensive, objective, and quantitative bibliometric analysis of this growing literature using Web of Science (WoS) data. We identified trends in the literature over the past decade, the geographical distribution of articles, authorship, and institutional contributions worldwide. The paper also identifies the dominant literature using citations in WoS and discusses computational algorithms that are expanding the econometric frontiers in asset pricing. The top cited papers were reviewed, highlighting their contribution. The limitations of ML learning methods and recent advances in ML were used to provide a conic view to future ML econometric practice.
Subjects: 
machine learning
artificial intelligence
autoencoder
asset pricing
anomalies
asset returns
options
big data
neural networks
textual analysis
Gaussian process
Bayesian inference
global optimization
JEL: 
C44
C45
C52
C55
C58
C61
G0
G01
G02
G12
G14
G17
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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