The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis
Open accessAbstract
Abstract Artificial intelligence (AI) and machine learning (ML) are two related technologies that are emergent in financial scholarship. However, no review, to date, has offered a wholistic retrospection of this research. To address this gap, we provide an overview of AI and ML research in finance. Using both co-citation and bibliometric-coupling analyses, we infer the thematic structure of AI and ML research in finance for 1986–April 2021. By uncovering nine (co-citation) and eight (bibliometric coupling) specific clusters of finance that apply AI and ML, we further identify three overarching groups of finance scholarship that are roughly equivalent for both forms of analysis: (1) portfolio construction, valuation, and investor behavior; (2) financial fraud and distress; and (3) sentiment inference, forecasting, and planning. Additionally, using co-occurrence and confluence analyses, we highlight trends and research directions regarding AI and ML in finance research. Our results provide assessment of AI and ML in finance research.
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Cites
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How to conduct a bibliometric analysis: An overview and guidelines
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Self-citationEfficient capital markets: A review of theory and empirical work
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Cited by
Alexa, what do we know about conversational commerce? Insights from a systematic literature review.
Lim, W. M., Kumar, S., Verma, S., & Chaturvedi, R. (2022) · Psychology & Marketing · doi:10.1002/mar.21654 · Cited by 293
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“Although there is a plethora of AI research (e.g., Abdel-Karim et al. 2021; Cao 2020, 2022; Goodell et al. 2021; Martin 2019; Rai et al. 2019; Zheng et al. 2019), practical implementation lags due to the so-called black-box nature of most AI-based systems, thus missing to unlock the full potential…”
InfluentialSocial companionship with artificial intelligence: Recent trends and future avenues
Chaturvedi, R., Verma, S., Das, R., & Dwivedi, Y. K. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122634 · Cited by 244
Rane, N. L., Choudhary, S. P., & Rane, J. (2024) · Studies in Economics and Business Relations · doi:10.48185/sebr.v5i2.1050 · Cited by 81
Ben Jabeur, S., Ballouk, H., Ben Arfi, W., & Sahut, J.-M. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2022.113631 · Cited by 70
Metadata
- Authors
- Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D.
- Venue
- Journal of Behavioral and Experimental Finance
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 32
- Pages
- 100577
- ISSN
- 2214-6350 (ISSN-L)
- OpenAlex
- W3198357836
- Cited by
- 715
- References
- 14
- FWCI
- 68.75
- Type
- journal article
- Language
- en
- Open access
- green
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- Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance. https://doi.org/10.1016/j.jbef.2021.100577
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
