IDEAS home Printed from https://ideas.repec.org/a/eco/journ1/2024-04-32.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijefi/article/download/16399/8087
    Download Restriction: no
    File URL: https://www.econjournals.com/index.php/ijefi/article/view/16399
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. Donthu, Naveen & Kumar, Satish & Mukherjee, Debmalya & Pandey, Nitesh & Lim, Weng Marc, 2021. "How to conduct a bibliometric analysis: An overview and guidelines," Journal of Business Research, Elsevier, vol. 133(C), pages 285-296.
    3. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
    4. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    5. Gordon Rogers & Martin Szomszor & Jonathan Adams, 2020. "Sample size in bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 777-794, October.
    6. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    7. Abdelhamid Zaidi & Samuel-Soma M. Ajibade & Majd Musa & Festus Victor Bekun, 2023. "New Insights into the Research Landscape on the Application of Artificial Intelligence in Sustainable Smart Cities: A Bibliometric Mapping and Network Analysis Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 287-299, July.
    8. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    9. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    10. Kim, Soon-Ho & Kim, Dongcheol, 2014. "Investor sentiment from internet message postings and the predictability of stock returns," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 708-729.
    11. Zhu, You & Zhou, Li & Xie, Chi & Wang, Gang-Jin & Nguyen, Truong V., 2019. "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 22-33.
    12. Cynthia W. Cai & Martina K. Linnenluecke & Mauricio Marrone & Abhay K. Singh, 2019. "Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific," Abacus, Accounting Foundation, University of Sydney, vol. 55(4), pages 709-733, December.
    13. Wout Scholten & Thomas P Franssen & Leonie van Drooge & Sarah de Rijcke & Laurens K Hessels, 2021. "Funding for few, anticipation among all: Effects of excellence funding on academic research groups," Science and Public Policy, Oxford University Press, vol. 48(2), pages 265-275.
    14. Ashkan Ebadi & Andrea Schiffauerova, 2013. "Impact of Funding on Scientific Output and Collaboration: A Survey of Literature," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 12(04), pages 1-16.
    15. Grit Laudel, 2006. "The art of getting funded: How scientists adapt to their funding conditions," Science and Public Policy, Oxford University Press, vol. 33(7), pages 489-504, August.
    16. Jonas Lundberg & Göran Tomson & Inger Lundkvist & John Sk?r & Mats Brommels, 2006. "Collaboration uncovered: Exploring the adequacy of measuring university-industry collaboration through co-authorship and funding," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(3), pages 575-589, December.
    17. Liang, Deron & Lu, Chia-Chi & Tsai, Chih-Fong & Shih, Guan-An, 2016. "Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study," European Journal of Operational Research, Elsevier, vol. 252(2), pages 561-572.
    18. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    19. Habiba Halepoto & Tao Gong & Hafeezullah Memon, 2022. "A Bibliometric Analysis of Antibacterial Textiles," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    20. Jan De Spiegeleer & Dilip B. Madan & Sofie Reyners & Wim Schoutens, 2018. "Machine learning for quantitative finance: fast derivative pricing, hedging and fitting," Quantitative Finance, Taylor & Francis Journals, vol. 18(10), pages 1635-1643, October.
    21. Ubfal, Diego & Maffioli, Alessandro, 2011. "The impact of funding on research collaboration: Evidence from a developing country," Research Policy, Elsevier, vol. 40(9), pages 1269-1279.
    22. Basem Al-Omari & Tauseef Ahmad & Rami H. Al-Rifai, 2022. "SARS-CoV-2 and COVID-19 Research Trend during the First Two Years of the Pandemic in the United Arab Emirates: A PRISMA-Compliant Bibliometric Analysis," IJERPH, MDPI, vol. 19(13), pages 1-13, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.
    2. Huichen Gao & Shijuan Wang, 2022. "The Intellectual Structure of Research on Rural-to-Urban Migrants: A Bibliometric Analysis," IJERPH, MDPI, vol. 19(15), pages 1-19, August.
    3. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
    4. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    5. Bhaskar, Ratikant & Hunjra, Ahmed Imran & Bansal, Shashank & Pandey, Dharen Kumar, 2022. "Central Bank Digital Currencies: Agendas for future research," Research in International Business and Finance, Elsevier, vol. 62(C).
    6. Hashemi, Hossein & Rajabi, Reza & Brashear-Alejandro, Thomas G., 2022. "COVID-19 research in management: An updated bibliometric analysis," Journal of Business Research, Elsevier, vol. 149(C), pages 795-810.
    7. Boubaker, Sabri & Goodell, John W. & Kumar, Satish & Sureka, Riya, 2023. "COVID-19 and finance scholarship: A systematic and bibliometric analysis," International Review of Financial Analysis, Elsevier, vol. 85(C).
    8. Gour Gobinda Goswami & Tahmid Labib, 2022. "Modeling COVID-19 Transmission Dynamics: A Bibliometric Review," IJERPH, MDPI, vol. 19(21), pages 1-19, October.
    9. Jin Su & Mo Wang & Mohd Adib Mohammad Razi & Norlida Mohd Dom & Noralfishah Sulaiman & Lai-Wai Tan, 2023. "A Bibliometric Review of Nature-Based Solutions on Urban Stormwater Management," Sustainability, MDPI, vol. 15(9), pages 1-23, April.
    10. Khan, Ashraf & Goodell, John W. & Hassan, M. Kabir & Paltrinieri, Andrea, 2022. "A bibliometric review of finance bibliometric papers," Finance Research Letters, Elsevier, vol. 47(PA).
    11. Zamani, Mehdi & Yalcin, Haydar & Naeini, Ali Bonyadi & Zeba, Gordana & Daim, Tugrul U, 2022. "Developing metrics for emerging technologies: identification and assessment," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    12. Abidin Kemeç & Ayşenur Tarakcıoglu Altınay, 2023. "Sustainable Energy Research Trend: A Bibliometric Analysis Using VOSviewer, RStudio Bibliometrix, and CiteSpace Software Tools," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    13. Kumar, Satish & Chavan, Meena & Pandey, Nitesh, 2023. "Journal of International Management: A 25-year review using bibliometric analysis," Journal of International Management, Elsevier, vol. 29(1).
    14. Weisheng Chiu & Thomas Chun Man Fan & Sang-Back Nam & Ping-Hung Sun, 2021. "Knowledge Mapping and Sustainable Development of eSports Research: A Bibliometric and Visualized Analysis," Sustainability, MDPI, vol. 13(18), pages 1-17, September.
    15. Mbarki, Imen & Khan, Muhammad Arif & Karim, Sitara & Paltrinieri, Andrea & Lucey, Brian M., 2023. "Unveiling commodities-financial markets intersections from a bibliometric perspective," Resources Policy, Elsevier, vol. 83(C).
    16. Alaminos, David & Guillén-Pujadas, Miguel & Vizuete-Luciano, Emili & Merigó, José María, 2024. "What is going on with studies on financial speculation? Evidence from a bibliometric analysis," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 429-445.
    17. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    18. Huixin Wang & Jing Xie & Shixian Luo & Duy Thong Ta & Qian Wang & Jiao Zhang & Daer Su & Katsunori Furuya, 2023. "Exploring the Interplay between Landscape Planning and Human Well-Being: A Scientometric Review," Land, MDPI, vol. 12(7), pages 1-24, June.
    19. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    20. Faisal Bin Sulaiman, 2023. "Compact City: What Is the Extent of Our Exploration for Its Meanings? A Systematic Review," Sustainability, MDPI, vol. 15(13), pages 1-22, June.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eco:journ1:2024-04-32. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.