Herding Behaviour and Investor Sentiment: A Bibliometric Mapping of Global Research Trends (2006-2025)

HRMARS - The elements of herding trends and investor feelings influence collective financial patterns, which have profound socio-economic impacts. This study provides an extensive bibliometric mapping of 1,087 Scopus-indexed articles between 2006 and 2025 to depict the intellectual progression, subjective framework, and research directions of herding research. The analysis was performed using Biblioshiny and VOSviewer, and incorporated performance metrics, co-occurrence mapping, and thematic evolution on how behavioural finance has grown from simple models of investor bias to global, empirical, and technology-driven strategies. There are five predominant clusters, including behavioural biases, methodological innovation, regional and institutional dynamics, and sustainability-linked extensions. These findings shed light on the shift from validation-based research to interdisciplinary research on fintech, ESG, and digital sentiment analytics. Baker and Wurgler, as well as Tetlock, still stand as foundational references with recent developments in machine learning and text analytics as the new frontiers. The results highlight the importance of collective behaviour in financial stability, inclusion, and market resilience. The study is relevant to a socio-behavioural roadmap for researchers and policymakers who would need to consider and control the social and economic effects of herding or the generation of modern financial systems.