Artificial Intelligence and Deep Learning in Stock Prediction: A Bibliometric Review

Authors

  • Chin Yang Lin Laboratory of Applied Neurosciences, Faculty of Business and Law, University of Saint Joseph, Macau, China https://orcid.org/0000-0001-6325-2174
  • João Alexandre Lobo Marques Laboratory of Applied Neurosciences, Faculty of Business and Law, University of Saint Joseph, Macau, China https://orcid.org/0000-0002-6472-8784
  • Lin Kun Chan School of Business, Macau University of Science and Technology, Taipa, Macao SAR, China https://orcid.org/0000-0001-5764-1106

DOI:

https://doi.org/10.34190/ecmlg.20.1.3003

Keywords:

Bibliometric Analysis, Artificial Intelligence (AI), Deep Learning (DL), Stock Prediction, VOSviewer, Scientific Mapping Knowledge

Abstract

Artificial intelligence (AI) and deep learning (DL) are advancing in stock market prediction, attracting the attention of researchers in computer science and finance. This bibliometric review analyzes 525 articles published from 1991 to 2024 in Scopus-indexed journals, utilizing VOSviewer software to identify key research trends, influential contributors, and burgeoning themes. The bibliometric analysis encompasses a performance analysis of the most prominent scientific contributors and a network analysis of scientific mapping, which includes co-authorship, co-occurrence, citation, bibliographical coupling, and co-citation analyses enabled by the VOSviewer software. Among the 693 countries, significant hubs of knowledge production include China, the US, India, and the UK, highlighting the global relevance of the field. Various AI and DL technologies are increasingly employed in stock price predictions, with artificial neural networks (ANN) and other methods such as long short-term memory (LSTM), Random Forest, Sentiment Analysis, Support Vector Machine/Regression (SVM/SVR), among the 1399 keyword counts in publications. Influential studies such as LeBaron (1999) and Moghaddam (2016) have shaped foundational research in 8159 citations. This review offers original insights into the bibliometric landscape of AI and DL applications in finance by mapping global knowledge production and identifying critical AI methods advancing stock market prediction. It enables finance professionals to learn about technological developments and trends to enhance decision-making and gain market advantage.

Author Biographies

Chin Yang Lin, Laboratory of Applied Neurosciences, Faculty of Business and Law, University of Saint Joseph, Macau, China

Chin Yang Lin (Coka)

Ph.D. candidate in Business Administration at the University of Saint Joseph. His research focuses on Artificial Intelligence, finance analytics, and stock market prediction. As a university lecturer, he has over five years of teaching experience in finance, marketing, management, accounting, and e-commerce.

João Alexandre Lobo Marques, Laboratory of Applied Neurosciences, Faculty of Business and Law, University of Saint Joseph, Macau, China

Associate Professor, Head of the Department of Business Administration, Research Coordinator at the Faculty of Business and Law, University of Saint Joseph, Macau, China. Founder of the Laboratory of Applied Neurosciences. Post Doctorate and Honorary Research Fellow at the University of Leicester, UK. PhD in Engineering Federal University of Ceará.

Lin Kun Chan, School of Business, Macau University of Science and Technology, Taipa, Macao SAR, China

Lin Kun Chan majored in finance and has taught at the Macau University of Science and Technology. She had four published SSCI papers with coauthors. Her research focuses on Market Microstructure, Asset pricing, and Corporate Finance. The course fields she taught include Corporate Governance, Accounting, Finance, and Financial Marketing.

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Published

2024-11-13

Issue

Vol. 20 No. 1 (2024): Proceedings of the 20th European Conference on Management, Leadership and Governance

Section

Academic Papers