Kaushal Kumar

The present study analyzes the evolution and themes of behavioral finance and investment decision-making research through an exhaustive bibliometric analysis. Studies indexed in Scopus and Web of Science since 2000 were analyzed through co-citation, bibliographic coupling, and keyword co-occurrence methods using VOSviewer, while RStudio aided performance and thematic analyses. According to the findings, overconfidence, herding behavior, financial literacy, and investment decisions are major themes in the field with fintech and digital platforms gaining prominence lately. The study highlights a definite move away from early studies centered around the cognitive biases of individuals towards a more integrated approach that involves investment behavior facilitated by technological advancements. Though there were some limitations associated with database selection and language barriers, the present study has provided useful insights regarding contributors and future trends of behavioral finance. The bibliometric approach is a structured way of studying the structure and development of scientific knowledge. The publication data, citation trends, and keyword connections that characterize the field can be used to discover the prominent authors, publications, institutions, countries, and themes within the area of study (Aria & Cuccurullo, 2017; Donthu et al., 2021; Merigó & Yang, 2017). This approach is very helpful in studying the development of research on behavioral bias. Thus, the current research will analyze papers published in Scopus and Web of Science from 2000 to 2025 to present an overall picture of the research area related to behavioral biases and investment decision-making. Through the use of bibliometric methodology such as co-citation, bibliographic coupling, and keywords co-occurrence analysis, the research will explore the intellectual background and structures of the research area as well as research trends in the field. The co-citation analysis performed by the cited authors reveals the most eminent authors in the field of behavioral finance, as depicted in Table 6 and network visualization (Figure 8). The authors Kahneman, Odean, and Tversky are revealed to be most co-cited authors due to their basic contributions to decision-making theories and prospect theory. Meanwhile, the other cluster is comprised of authors Hirshleifer, Barber, and Statman and reveals their importance in explaining behavioral biases in trading and investment decisions. Another cluster consists of Shefrin, Barberis, Shleifer, and Thaler and reveals the contributions made by them in theorizing market inefficiencies, investors’ sentiments, and the integration of psychology into financial models. Thus, the network visualization indicates that these clusters are closely interconnected, implying that the works of these authors are mostly co-cited.

Narrative review of the research concerning the link between behavioral biases, investment decision making, and the rise of AI-powered financial technology is presented in this paper. In particular, attention is paid to the impact that the key concepts of behavioral finance such as overconfidence, herding, loss aversion, anchoring, mental accounting, and regret aversion have on the behavior of the investors. Then, the paper proceeds to the analysis of the rising popularity of robo advisors and other AI-powered financial solutions, underlining the importance of investor's trust to these technologies for their adoption and effectiveness. Algorithmic transparency and explainability of AI become particularly notable factors influencing confidence and adoption of AI-generated financial recommendations. The reactions of the investors to both behavioral biases and technology-enabled financial services are considered from the perspective of financial literacy of the investors. The paper suggests that these literacies might help investors cope with the digital nature of finances and make more effective decisions based on data from earlier researches. There is also a mention of the differences between demographic groups, like age or gender. The paper analyzes several researches to reveal common understanding, debates, and gaps in this field not to test any hypothesis. It should be noted that the impact of behavioral biases, investor’s trust, financial literacy, and the development of artificial intelligence in finance is underlined in the conclusion of the paper.