Global Research Frontiers in Investor Emotion and Financial Decision-Making: A Bibliometric Investigation
Deepa M & Krishnakumar K
What the paper says
Investor emotions are increasingly recognized as central determinants of financial decision-making, yet the evidence base remains dispersed across behavioral finance, psychology, information systems, and data-driven market analytics. This study maps the intellectual structure, thematic evolution, and emerging research frontiers of scholarship on investor emotions and financial decision-making through a bibliometric investigation of Scopus-indexed publications retrieved using the search string. Also Using performance analysis and science mapping, the study synthesizes annual scientific production, citation dynamics, and source distribution, complemented by co-citation, bibliographic coupling, and keyword co-occurrence networks generated via established bibliometric tools (e.g., VOSviewer and Bibliometrix/Biblioshiny). Results indicate a clear growth trajectory with modest output during 2010–2017, consolidation during 2018–2022, and a sharp expansion during 2023–2025, while average citations per year peak in earlier cohorts, reflecting citation-window effects. The co-citation structure reveals three foundational knowledge bases: (i) behavioral finance and investor psychology, (ii) affective decision science and emotion regulation perspectives, and (iii) digitally mediated sentiment and information effects. Bibliographic coupling identifies active research fronts anchored by highly influential synthesis work and extending into applied bias research and computational sentiment analytics. Keyword co-occurrence further demonstrates three dominant thematic clusters: behavioral finance and investment decision biases; psychological and survey/experimental emotion research; and data-driven sentiment analysis linked to information systems, big data, and electronic trading. Overall, the findings show a field transitioning toward interdisciplinary integration and scalable emotion measurement, with emerging frontiers in social media-driven investing, retail investor psychology, and emotion-aware AI. The study provides a structured roadmap for future theory development, methodological triangulation, and context-specific research in technology-mediated and high-volatility markets.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.