Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.8
Journals
JRFM
Special Issues
Machine Learning Econometrics in Asset Pricing
share announcement

Machine Learning Econometrics in Asset Pricing

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Technology and Innovation".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 2617

Special Issue Editor

Prof. Dr. Hector O. Zapata

E-Mail Website
Guest Editor
Department of Agricultural Economics & Agribusiness, Louisiana State University-LSU AgCenter, Baton Rouge, LA 70803, USA
Interests: econometrics; financial econometrics; risk management; financial analysis; international trade & development; agricultural marketing; prices

Special Issue Information

Dear Colleagues,

The prediction of stock returns has been a complex problem of frequent research interest in quantitative finance. Complexity is enhanced by the vast number of determinants (predictors) of stock returns and the strength of noise-to-signal ratio of predictors across firms. Financial models of stock returns use a small number of predictors, leaving out vast amounts of financial information that contributes to explaining variations in stock returns. The explosive growth in financial data has allowed analysts, investors, and researchers to use methods such as machine learning (ML) to capture features in high-dimensional data. Capital asset pricing models (CAPM) and machine learning cointegrate well, with CAPM providing a theory-consistent structure to predict asset returns while machine learning captures data features that improve model specification and predictability.

Prof. Dr. Hector O. Zapata
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • econometrics
  • machine learning
  • asset pricing
  • computational methods
  • financial shocks
  • time series

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 994 KiB  
Article
A Bibliometric Analysis of Machine Learning Econometrics in Asset Pricing
by Hector O. Zapata and Supratik Mukhopadhyay
J. Risk Financial Manag. 2022, 15(11), 535; https://doi.org/10.3390/jrfm15110535 - 17 Nov 2022
Cited by 2 | Viewed by 2243
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Machine Learning Econometrics in Asset Pricing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop