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Machine learning in finance: A topic modeling approach

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.
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https://hal-rennes-sb.archives-ouvertes.fr/hal-03700508
Contributor : Steven Gouin Connect in order to contact the contributor
Submitted on : Tuesday, June 21, 2022 - 11:21:06 AM
Last modification on : Wednesday, June 22, 2022 - 3:09:49 AM

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Saqib Aziz, Michael Dowling, Helmi Hammami, Anke Piepenbrink. Machine learning in finance: A topic modeling approach. European Financial Management, 2022, 28 (3), pp.744-770. ⟨10.1111/eufm.12326⟩. ⟨hal-03700508⟩

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