Artificial Intelligence and Stock Trading: Systematic Review Using TCCM and Bibliometric Analysis

This study presents a comprehensive systematic literature review of artificial intelligence (AI) applications in stock trading, mapping the field’s evolution from 1987 to 2025. In spite of the exponential growth of automated technologies in financial markets, existing research remains highly fragmented across domains such as predictive analytics, algorithmic trading, behavioural finance, and AI governance. To synthesize this diverse literature and identify emerging trends, this study integrates a large-scale bibliometric analysis initially evaluating 2,507 documents with the TCCM (Theory, Context, Characteristics, and Methodology) framework to critically examine a final curated sample of 36 core studies. The findings reveal a transformational shift within the domain: while early literature primarily focused on technical forecasting accuracy and computational efficiency, contemporary research heavily prioritizes the behavioural, psychological, and trust-based dynamics of AI adoption. Key themes such as robo-advisory services, Explainable AI (XAI), and algorithm aversion now dominate the scholarly discourse, supported by a distinct methodological transition from secondary historical data analysis toward primary empirical research utilizing advanced behavioural frameworks. Ultimately, this review identifies critical literature gaps and provides a strategic roadmap for future interdisciplinary research, emphasizing the pressing need for cross cultural comparative studies, the integration of sustainable finance (ESG) factors, and the establishment of robust ethical and regulatory governance in AI-driven financial ecosystems.