Finance research over 40 years: What can we learn from machine learning?

Po‐Yu Liu and Zigan Wang

International Studies of Economics, 2024, vol. 19, issue 4, 472-507

Abstract: We apply machine learning models to a universe of 20,185 finance articles published between 1976 and 2015 on 17 finance journals, and objectively identify 38 research topics. The financial crisis, hedge/mutual fund, social network, and culture were the fastest growing topics, while market microstructure, initial public offering, and option pricing shrank most from 2006 to 2015. We also list each topic's most cited papers, and present the fastest‐growing topics among the universe of 130,547 SSRN working papers. Moreover, we find a bibliometric regularity: the number of researchers covering n topics is about twice the number of researchers covering n + 1 topics.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/ise3.95

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:intsec:v:19:y:2024:i:4:p:472-507

Access Statistics for this article

More articles in International Studies of Economics from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().