Artificial intelligence (AI) has revolutionized stock market research, offering innovative solutions for predictive modeling, algorithmic trading, and financial decision-making. Despite significant advancements, the integration of AI in stock markets remains underexplored in terms of evolving trends, collaborative research dynamics, and practical applications. Existing studies often focus on isolated techniques or case-specific applications, leaving a gap in understanding the broader landscape of AI’s role in reshaping financial systems. This study addresses these gaps through a bibliometric analysis of publications indexed in Web of Science (WoS) and Scopus from 1980 to early 2025, with a detailed focus on contributions between 2022 and 2023. The research seeks to answer three key questions: What are the emerging trends in AI-driven stock market research? Which authors, institutions, and countries lead in this domain? What are the most cited studies, and how do they influence the field? By using tools like VOSviewer, this study examines keyword co-occurrences, citation networks, and publication trends. The findings reveal a rapid shift towards hybrid models, leveraging AI techniques like machine learning and deep learning, coupled with sentiment analysis and real-time adaptive systems. While challenges such as data quality and ethical considerations persist, the study underscores AI’s transformative potential, highlighting the need for interdisciplinary collaboration and innovative approaches to address these challenges.
Mohamad et al. (Mon,) studied this question.
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