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Artificial intelligence and machine learning in finance: A bibliometric review

Author

Listed:
  • Shamima Ahmed
  • Muneer Alshater

    (Emirates College of Technology)

  • Anis El Ammari

    (UM - Université de Monastir - University of Monastir)

  • Helmi Hammami

    (ESC [Rennes] - ESC Rennes School of Business)

Abstract

This study reviewed the artificial intelligence (AI) and machine learning (ML) literature in the finance field. Using a bibliometric approach, we collected 348 articles published in 2011–2021 from journals indexed in the Scopus database. Multiple software (RStudio, VOSviewer, and Excel) were employed to analyze the data and depict the most active scientific actors in terms of countries, institutions, sources, documents, and authors. Our review revealed an upward trajectory in the publication trend starting from 2015 and found the application of AI and ML in bankruptcy prediction, stock price prediction, portfolio management, oil price prediction, anti-money laundering, behavioral finance, big data analytics, and blockchain. Moreover, the United States, China, and the United Kingdom were the top three contributors to the literature. Our results provide practical guidance to market participants, especially, fintech and finance companies, on how AI and ML can be used in their decision-making.

Suggested Citation

  • Shamima Ahmed & Muneer Alshater & Anis El Ammari & Helmi Hammami, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Post-Print hal-03697290, HAL.
    • Handle: RePEc:hal:journl:hal-03697290
    DOI: 10.1016/j.ribaf.2022.101646
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    Citations

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    Cited by:

    1. Goodell, John W. & Oriani, Marco Ercole & Paltrinieri, Andrea & Patel, Ritesh, 2023. "The importance of ABS 2 journals in finance scholarship: Evidence from a bibliometric case study," Finance Research Letters, Elsevier, vol. 55(PA).
    2. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).
    4. Oliveira, Alexandre Silva de & Ceretta, Paulo Sergio & Albrecht, Peter, 2023. "Performance comparison of multifractal techniques and artificial neural networks in the construction of investment portfolios," Finance Research Letters, Elsevier, vol. 55(PA).
    5. Gupta, Somya & Ghardallou, Wafa & Pandey, Dharen Kumar & Sahu, Ganesh P., 2022. "Artificial intelligence adoption in the insurance industry: Evidence using the technology–organization–environment framework," Research in International Business and Finance, Elsevier, vol. 63(C).
    6. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
    7. Costola, Michele & Hinz, Oliver & Nofer, Michael & Pelizzon, Loriana, 2023. "Machine learning sentiment analysis, COVID-19 news and stock market reactions," Research in International Business and Finance, Elsevier, vol. 64(C).
    8. HUO, Peng & WANG, Luxin, 2022. "Digital economy and business investment efficiency: Inhibiting or facilitating?," Research in International Business and Finance, Elsevier, vol. 63(C).
    9. Wang, Dan & Chen, Zhi & Florescu, Ionuţ & Wen, Bingyang, 2023. "A sparsity algorithm for finding optimal counterfactual explanations: Application to corporate credit rating," Research in International Business and Finance, Elsevier, vol. 64(C).
    10. González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023. "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    11. Yasmeen Ansari & Mansour Saleh Albarrak & Noorjahan Sherfudeen & Arfia Aman, 2022. "A Study of Financial Literacy of Investors—A Bibliometric Analysis," IJFS, MDPI, vol. 10(2), pages 1-16, May.

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