Machine learning in finance: A topic modeling approach

Saqib Aziz, Michael Dowling, Helmi Hammami and Anke Piepenbrink
Additional contact information
Saqib Aziz: ESC [Rennes] - ESC Rennes School of Business
Michael Dowling: DCU - Dublin City University [Dublin]
Helmi Hammami: ESC [Rennes] - ESC Rennes School of Business
Anke Piepenbrink: ESC [Rennes] - ESC Rennes School of Business

Post-Print from HAL

Abstract: We identify the core topics of research applying machine learning to finance. We use a probabilistic topic modeling approach to make sense of this diverse body of research spanning across multiple disciplines. Through a latent Dirichlet allocation topic modeling technique, we extract 15 coherent research topics that are the focus of 5942 academic studies from 1990 to 2020. We find that these topics can be grouped into four categories: Price-forecasting techniques, financial markets analysis, risk forecasting and financial perspectives. We first describe and structure these topics and then further show how the topic focus has evolved over the last three decades. A notable trend we find is the emergence of text-based machine learning, for example, for sentiment analysis, in recent years. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic modeling of topics for deep comprehension of a body of literature.

Keywords: finance; latent dirichlet allocation; machine learning; textual analysis; topic modeling (search for similar items in EconPapers)
Date: 2022-06
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Published in European Financial Management, 2022, 28 (3), pp.744-770. ⟨10.1111/eufm.12326⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:hal:journl:hal-03700508

DOI: 10.1111/eufm.12326

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().