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

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dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorJasser, Muhammed Basheer
dc.contributor.authorAlebiosu, David Olayemi
dc.contributor.authorAl-Hadi, Ismail Ahmed Al-Qasem
dc.contributor.authorAl-Dharhani, Ghassan Saleh
dc.contributor.authorHassan, Farrukh
dc.contributor.authorGyamfi, Bright Akwasi
dc.date.accessioned2024-10-12T19:47:13Z
dc.date.available2024-10-12T19:47:13Z
dc.date.issued2024
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractThis 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. © 2024, Econjournals. All rights reserved.en_US
dc.description.sponsorshipNational Science Foundation, NSF; Horizon 2020 Framework Programme, H2020; Vietnam National University HoChiMinh City, (C2023-54-01/H?-KHCN)en_US
dc.identifier.doi10.32479/ijefi.16399
dc.identifier.endpage315en_US
dc.identifier.issn2146-4138
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85199774678en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage299en_US
dc.identifier.urihttps://doi.org/10.32479/ijefi.16399
dc.identifier.urihttps://hdl.handle.net/11467/8829
dc.identifier.volume14en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherEconjournalsen_US
dc.relation.ispartofInternational Journal of Economics and Financial Issuesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzScopus_20241012en_US
dc.subjectBibliometric Analysisen_US
dc.subjectFinancial Accessen_US
dc.subjectFinancial Industryen_US
dc.subjectIndustrial Growthen_US
dc.subjectMachine Learningen_US
dc.subjectPublication Trendsen_US
dc.titleUncovering the Dynamics in the Application of Machine learning in Computational Finance: A Bibliometric and Social Network Analysisen_US
dc.typeArticleen_US

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