The rise of artificial intelligence in algorithmic trading: a bibliometric study

Purpose This paper aims to provide a comprehensive bibliometric mapping of research on artificial intelligence in algorithmic trading by analyzing 417 publications from 1993 to 2025. It seeks to identify publication trends, influential journals and authors, collaboration patterns, and the field's main thematic structures, in order to clarify the intellectual foundations, emerging research fronts, and future directions of this rapidly evolving interdisciplinary domain. Design/methodology/approach The study employs a bibliometric research design based on 417 publications retrieved from the Web of Science Core Collection (1993–2025). Descriptive indicators are used to analyze publication trends and source productivity. Network-based techniques, implemented through VOSviewer, are applied to conduct citation analysis, co-authorship mapping, and term co-occurrence analysis. These methods enable the identification of influential contributions, collaboration structures, thematic clusters, and emerging research fronts within artificial intelligence–driven algorithmic trading. Findings The results show a sharp growth in publications since 2018, indicating the rapid consolidation of AI-driven algorithmic trading as a research field. A small number of highly cited studies act as intellectual anchors, while most authors and institutions remain weakly connected, revealing fragmented collaboration networks. Thematic analysis identifies coherent clusters cantered on methodological innovation, trading strategy applications, time-series forecasting, and ethical and governance issues. Emerging research fronts, particularly transformer-based models and explainable AI, signal a shift toward more interpretable and robust trading systems. Originality/value This study provides the first large-scale, longitudinal bibliometric mapping of artificial intelligence research in algorithmic trading, covering more than three decades of scientific production. It uniquely integrates publication trends, citation impact, collaboration networks, and thematic clustering within a single analytical framework. By revealing both the intellectual consolidation of dominant AI paradigms and the fragmentation of collaboration structures, the paper offers novel insights into the field's evolution. It delivers strategic value for scholars and practitioners by identifying emerging research fronts and underexplored areas that shape future theoretical development and practical innovation.