Emergence of Artificial Intelligence and Machine Learning in Equity Security Prediction: A Bibliometric Analysis

This bibliometric study explores the development of Artificial Intelligence (AI) and Machine Learning (ML) in equity market prediction research over the period from 2014 to 2024. Using a dataset of 234 articles from leading international journals, the analysis applies techniques such as co-citation analysis, bibliographic coupling, keyword co-occurrence, and co-authorship mapping to uncover thematic patterns and the intellectual structure of the field. The findings highlight four major research clusters: portfolio management, valuation techniques, investor behavior, and sentiment analysis. The increasing use of advanced ML approaches, including deep learning and neural networks, marks a shift toward more sophisticated predictive models. The study also notes a consistent rise in scholarly output, with research activity peaking in 2023 and 2024. Key authors, impactful publications, and collaborative networks are identified, showcasing the interdisciplinary nature of equity market forecasting. This paper offers a comprehensive overview of how AI and ML are being applied in financial research, providing valuable insights for both scholars and industry professionals.