New Insight Into The Research Of Deep Learning Applications In Finance: A Scientometric Approach

  • Opeoluwa Kofoworola Oluwabiyi
  • Samuel-Soma M. Ajibade
  • Joshlen Lim
  • Sarah Jane Colina
  • Ivy Batican
  • Lislee Valle
  • Edralin General
Keywords: Deep Learning, Applications, Bibliometric Analysis, Systematic Literature Review, Artificial Intelligence

Abstract

This study analyzed the research environment for the applications of deep learning in finance (DLAF) based on published papers indexed in the Scopus database from 2007 to 2021. As a result, the publication trends of the published documents were analyzed to identify the most prolific writers, institutions, nations, and funding organizations on the subject. Subsequently, bibliometric analysis (BA) was utilized to examine and delineate co-authorship networks, keyword occurrences, and citations. A systematic literature review was conducted to analyze the scientific and technological advancements in the subject. The findings indicated that the quantity of published documents on DLAF study escalated significantly from 5 to 398 between 2007 and 2021, representing an extraordinary increase of around 7,900% in this field. The elevated production is somewhat attributed to the research endeavors of the foremost research-active academic entities, specifically Chihfong Tsai (National Central University, Taiwan) and Stanford University (United States). The National Natural Science Foundation of China (NSFC) is the most prolific funder in the United States and has the highest volume of published documents. Business study indicated elevated collaboration rates, published documents, and citations among the stakeholders. Keyword occurrence analysis indicated that DLAF research is a highly interdisciplinary field with several focal points and themes, encompassing systems, algorithms, methodologies, as well as security and crime prevention in finance through deep learning applications. According to citation analysis, the most distinguished and prestigious source titles on DLAF are IEEE Access, Expert Systems with Applications, and ACM International Conference Proceedings Series (ACM-ICPS). The comprehensive literature review identified several domains and applications of DLAF research, notably in predictive analytics, credit assessment and management, supply chain, carbon trading, neural networks, and artificial intelligence, among others. DLAF research activities and their influence on the broader global community are anticipated to escalate in the forthcoming years.

Author Biographies

Opeoluwa Kofoworola Oluwabiyi

Doosan Bobcat, U Kodetky 1810, 263 01, Dobris. Czech Republic

Samuel-Soma M. Ajibade

Department of Computer Engineering, Istanbul Ticaret Universitesi, Istanbul, Turkiye

Joshlen Lim

College of Education, Arts and Sciences, Cebu Technological University-Danao

Sarah Jane Colina

College of Education, Arts and Sciences, Cebu Technological University-Danao

Ivy Batican

College of Education, Arts and Sciences, Cebu Technological University-Danao

Lislee Valle

College of Education, Arts and Sciences, Cebu Technological University-Danao

Edralin General

College of Education, Arts and Sciences, Cebu Technological University-Danao

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Published
2024-09-09
How to Cite
Opeoluwa Kofoworola Oluwabiyi, Samuel-Soma M. Ajibade, Joshlen Lim, Sarah Jane Colina, Ivy Batican, Lislee Valle, & Edralin General. (2024). New Insight Into The Research Of Deep Learning Applications In Finance: A Scientometric Approach. Revista Electronica De Veterinaria, 25(1), 1657-1671. https://doi.org/10.69980/redvet.v25i1.986
Section
Articles