A Worldwide Assessment of Quantitative Finance Research through Bibliometric Analysis

Feng Yu, Lianqian Yin, Guizhou Wang


The field of quantitative finance has been rapidly growing in both academics and practice. This article applies bibliometric analysis to investigate the current state of quantitative finance research. A comprehensive dataset of 2,723 publications from the Web of Science Core Collection database, between 1992 to 2022, is collected and analyzed. CiteSpace and VOSViewer are adopted to visualize the bibliometric analysis. The article identifies the most relevant research in quantitative finance according to journals, articles, research areas, authors, institutions, and countries. The study further identifies emerging research topics in quantitative finance, e.g. deep learning, neural networks, quantitative trading, and reinforcement learning. This article contributes to the literature by providing a systematic overview of the developments, trajectories, objectives, and potential future research topics in the field of quantitative finance.

Full Text:


DOI: https://doi.org/10.11114/aef.v10i2.5949


  • There are currently no refbacks.

Paper Submission E-mail: aef@redfame.com

Applied Economics and Finance    ISSN 2332-7294 (Print)   ISSN 2332-7308 (Online)

Copyright © Redfame Publishing Inc.

To make sure that you can receive messages from us, please add the 'redfame.com' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders. If you have any questions, please contact: aef@redfame.com