Bibliometric Insights into Machine Learning for Market Forecasting: Advances in Predictive Financial Analytics

This study examines the role of machine learning in financial market forecasting through a bibliometric analysis of scholarly publications from 2015 to 2024. The research identifies key trends, leading contributors, and emerging themes at the intersection of machine learning and predictive financial analytics. Drawing on concepts from financial theory, machine learning, and behavioral finance, the study emphasizes the development of advanced algorithms and sentiment analysis to improve predictive accuracy. Using data from the Dimensions database and visualized through VOSviewer, the analysis highlights a significant rise in research activity post-2019, underscoring machine learning’s growing importance in financial forecasting. Influential authors and key journals are also identified. Despite progress, the study notes gaps in empirical validation and ethical considerations, suggesting the need for further research to enhance applications in financial markets.