A bibliometric analysis of machine learning econometrics in asset pricing

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 15 ; Year: 2022 ; Issue: 11 ; Pages: 1-17

Classification and Topics

• Classification: Management ; Operations Research; Statistical Decision Theory ; Neural Networks and Related Topics ; Model Evaluation, Validation, and Selection ; Large Data Sets: Modeling and Analysis ; Financial Econometrics ; Optimization Techniques; Programming Models; Dynamic Analysis ; Financial Crises ; Asset Pricing; Trading Volume; Bond Interest Rates ; Information and Market Efficiency; Event Studies; Insider Trading ; Financial Forecasting and Simulation
• Subject: machine learning ; artificial intelligence ; autoencoder ; asset pricing ; anomalies ; asset returns ; options ; big data ; neural networks ; textual analysis ; Gaussian process ; Bayesian inference ; global optimization

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Zapata, Hector O. ; Mukhopadhyay, Supratik
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2022

Further information

• DOI: doi:10.3390/jrfm15110535
• Handle: http://hdl.handle.net/10419/275012
• Last update: 10.03.2025, 11:44 AM CET