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Uncovering the Dynamics in the Application of Machine learning in Computational Finance: A Bibliometric and Social Network Analysis

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  • Samuel-Soma M. Ajibade

    (1Department of Computer Engineering, Istanbul Ticaret University, Istanbul, Turkey; & Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia)

  • Muhammed Basheer Jasser

    (Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia,)

  • David Olayemi Alebiosu

    (Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia,)

  • Ismail Ahmed Al- Qasem Al-Hadi

    (Institute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur Malaysia,)

  • Ghassan Saleh Al-Dharhani

    (Institute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur Malaysia; & Faculty of Information Science, and Technology, UKM University, Bangi, Selangor, Malaysia,)

  • Farrukh Hassan

    (Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia,)

  • Bright Akwasi Gyamfi

    (School of Management, Sir Padampat Singhania University, Bhatewar, Udaipur 313601, India.)

Abstract

This paper examined the research landscape on the applications of machine learning in finance (MLF) research based on the published documents on the topic indexed in the Scopus database from 2007 to 2021. Consequently, the publication trends on the published documents data were examined to determine the most prolific authors, institutions, countries, and funding bodies on the topic. Next, bibliometric analysis (BA) was employed to analyse and map co-authorship networks, keywords occurrences, and citations. Lastly, a systematic literature review was carried out to examine the scientific and technological developments in the field. The results showed that the number of published documents on MLF research has soared tremendously from 5 to 398 between 2007 and 2021, which signifies an enormous increase (~7,900%) in the subject area. The high productivity is partly ascribed to the research activities of the most research-active academic stakeholders namely Chihfong Tsai (National Central University in Taiwan) and Stanford University (United States). However, the National Natural Science Foundation of China (NSFC) is the most active funder in the United States and has the largest number of published documents. BA analysis revealed high collaboration rates, published documents, and citations among the stakeholders. Keywords occurrence analysis revealed that MLF research is a highly inter- and multidisciplinary area with numerous hotspots and themes ranging from systems, algorithms and techniques to the security and crime prevention in Finance using ML. Citation analysis, the most prominent (and by extension the most prestigious) source titles on MLF are IEEE Access, Expert Systems with Applications and ACM International Conference Proceedings Series (ACM-ICPS). The systematic literature review revealed the various areas and applications of MLF research, particularly in the areas of predictive/forecasting analytics, credit assessment and management, as well as supply chain, carbon trading, neural networks, and artificial intelligence, among others. It is expected that MLF research activities and their impact on the wider global society will continue to increase in the coming years

Suggested Citation

  • Samuel-Soma M. Ajibade & Muhammed Basheer Jasser & David Olayemi Alebiosu & Ismail Ahmed Al- Qasem Al-Hadi & Ghassan Saleh Al-Dharhani & Farrukh Hassan & Bright Akwasi Gyamfi, 2024. "Uncovering the Dynamics in the Application of Machine learning in Computational Finance: A Bibliometric and Social Network Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 14(4), pages 299-315, July.
    • Handle: RePEc:eco:journ1:2024-04-32
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    More about this item

    Keywords

    Machine Learning; Financial Industry; Publication Trends; Financial Access; Industrial Growth; Bibliometric Analysis;
    All these keywords.

    JEL classification:

    • F3 - International Economics - - International Finance
    • G0 - Financial Economics - - General
    • G1 - Financial Economics - - General Financial Markets
    • G4 - Financial Economics - - Behavioral Finance
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance

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