This bibliometric analysis looks at global research trends in behavioral finance from the past ten years by using the Dimensions AI database to study how many papers were published, how often they were cited, who the key contributors are, and what new topics are emerging in the field. Behavioral finance combines traditional financial models with psychological insights to explain market irregularities that standard models often miss, such as how investors act during market ups and downs and how cognitive biases affect their financial choices. This article gives a numerical picture of how the area has grown by looking at its most important authors, collaboration networks, and top publishing journals. There has been a big increase in research production, with important papers from major countries and institutions that lay a strong foundation for the area of behavioral finance. Investor psychology, decision-making biases, and risk perception are prominent research areas, with recent developments in digital finance and sustainable investment illustrating the field's capacity to address modern financial challenges. This study also finds themes that are changing through keyword co-occurrence and citation analysis. These themes show that there is a growing interest in interdisciplinary research that links finance to psychology, economics, and cognitive science. Collaboration patterns suggest that countries and institutions are working together, and the research environment is shown to be very globalized and networked. Dimensions AI is a great dataset; however, the study of database exclusivity shows that future work should incorporate more sources to get a wider view. This paper is a useful resource for both scholars and practitioners because it outlines the major developments and possible future directions in behavioral finance. It helps people understand better how psychological aspects affect financial markets and encourages them to make better and more reliable financial decisions.
Behavioral finance is the intersection of psychology and finance, analyzing how individuals or groups react towards financial markets based on psychological, social and emotional factors. Behavioral finance is not relevant here when the price of all products and services increase. In contrast, traditional finance is based on the premise that investors behave rationally and that markets are efficient.
Bibliometric analysis is one of the important approaches for studying the trends, productivity and impact of academic publications. Bibliometric analysis opens up the behavioral finance evolution and new topics emerging in this context, as well as impact of collaboration networks on knowledge production. Here, the scientific landscape of behavioral finance as well as its interdisciplinary links and topical research interests are analyzed through publication and citation rates, co-authorship networks and keyword co-occurrence analysis. For instance, bibliometric analyses showed that the field of behavioral finance was impacted by behavioral sciences and psychology, as well as that it was dependent on cross field investigations for theoretical and empirical development.
Behavioural finance is the new branch of behavioural economics, which aims to understand investor behaviour regarding financial decisions. Traditional finance advocates argue that finance is a science; therefore, there exists one correct answer to the problem of finance as there is one correct answer to scientific problems. Classical financial theory relies on two main hypotheses: the rationality of investors and the efficiency of market (Fama, 1970). Every person does things differently when it comes to making decisions. Shortcomings and abnormalities in the financial market create hindrances to investors' rationality and market efficiency, while traditional financial theories have failed to sufficiently explain declining bubbles in the stock market. As a result, behavioral finance aims to clarify how real decision makers make financial decisions and why their choices can sometimes seem irrational (Paule-Vianez et al., 2020a).
Behavioural finance is a broad area that includes many types of human cognition concerns in financial choices. The body of works presented by the research done in behavioural finance has advanced in many different lines to study how psychological, behavioural and cognitive elements affect investors’ financial decisions which exemplifies the need for a bibliometric overview of behavioural finance that aims to cover the various aspects of literature created around such field. With that goal in mind, we did the bibliometric analysis to elaborate out how well and productive behavioural finance is and how differently it is doing in terms of its key themes and future research avenues. While we found a number of bibliometric studies focusing on behavioural finance, none analyzed this field in broad without the inclusion of other areas of research. This study aims to uniquely delineate the field of behavioural finance using a bibliometric lens, focusing on descriptive analysis, intellectual structure, and prospective research directions in behavioural finance.
Literature Review
Vijay Kumar et. et al., (2026)) highlighted a marked increase in the volume of publications alongside an increasing citation impact trend over that period. A more multidisciplined transition emerges, exemplified by the sheer volume of publications in sustainability-oriented journals such as the Journal of Cleaner Production — with a growing emphasis on financial literacy. Traditional core domains, including decision-making, risk assessment, and overconfidence remain essential.
Rahber Alam et al. (2026) cited explosive growth and substantial academic acceptance within the realm of behavioral finance. The United States had the leading number in publications (77) and citation impact (1,059) on behavioral finance. With 60 publications and 710 citations, India came in second. The Journal of Economic Literature and the Journal of Finance were the leading journals in this area, tracing 1,249 and 1,003 citations respectively. The findings revealed a remarkable development in the global landscape of behavioral finance.
Gaurav and Kapil (2023) studied the bibliometric features and literature review of behavioral finance. This conducted a bibliometric study of 1,523 publications from peer-reviewed journals in the Scopus database. Clustering analysis was conducted by utilizing Biblioshiny, while PageRank analysis was carried out using Gephi. Based on the keyword co-occurrence maps and grouping of recent publications, several future research trajectories were suggested. These include risk management and portfolio selection; topics related to market efficiency and investor behavior; influences on investor behavior; behavioral biases and emotions.
Naveen Donthu et al. (2021) attempted to provide an in-depth overview of bibliometric methods and its related techniques with an aim of providing a reliable, step-by-step guideline for conducting robust bibliometric analyses. The authors also clarify when bibliometric analysis can and should (not) be used, how these analyses stack against similar approaches, such as systematic literature reviews and meta-analyses. The readers of this paper will find it a good guidance on the methods and techniques available to perform bibliometric analysis studies.
While the initial studies (Kavita Karan & Rachna Achuta, 2020) looked at demographic and socio-economic determinants step by step, the field opened up to cover topics such as behavioural and psychological constructs subsequently (2019-20). In addition to conceptual structure, this research exposed the intellectual and social structure of the domain. The study offered valuable insights into aspects over which we did not have evidence. The study highlighted key determinants of financial behaviour and offered valuable insights on financial literacy–financial behaviour relationship.
Purpose of the Study
This study aims to conduct a bibliometric analysis of behavioral finance research using the Dimensions AI database. Through documenting worldwide trends related to the number of publications, their citation rates, and co-authorship networks over a period of 10 years. This paper provides have a useful overview of the evolution of the field, what key contributions have been made, and where new research directions are being pursued. It also details the geographical spread of research, alongside key contributing institutions to highlight leading zones of research activity and foreign collaborations.
Behavioral finance is an important subfield of finance that challenges the classical economic theory based on traditional investor rationality. The Systematic Review and Bibliometric Analysis of Behavioral Finance results indicate both a significant growth of the field as well as an increasing academic recognition.
Methodology
Data Acquisition
The Dimensions data set serves as the primary data source for this bibliometric analysis. The data were collected exclusively from dimensions AI ( https://app.dimensions.ai/ ) in order to ensure that no significant publications were overlooked and that the dataset maintained a high quality.
We obtained a total of 5682 articles from dimensions AI ( https://app.dimensions.ai/ ), perhaps the most impressive sources of this kind for the years 2021 until 15 March, 2026. Finally, we carried out a bibliometric analysis of 5682 articles. Furthermore, to conduct citation, co-citation and social network analysis, VOSviewer software was used for network mapping of authors, keywords and resources.
Criteria for Data Selection
Based on the most studied areas in behavioral finance, keywords such as 'behavioral finance', 'investor psychology', 'financial decision', Global Trends and 'market behavior' were selected. The following search statements in title field, abstract and keyword fields were performed for these keywords to determine the most relevant publications. Again, the document types were limited to only articles and reviews because these are both primary sources for research findings and the most inclusive summaries in this respect.
Bibliometric Analysis Techniques
Analyzing the extracted data was done using a combination of bibliometric indicators. These indicators were chosen as they offer a multi-dimensional view of research trends, author productivity and collaboration networks:
Publication Count: To track growth trends in behavioral finance research, we identified the number of publications per year. The annual number of publications gives an idea when activity in this area was most noted and significant peaks may correspond with important events in the finance sector.
Citation analysis: The influence and impact of publications were evaluated through citation counts. A highly cited work guiding subsequent studies and theoretical development was the foundational research in behavioral finance. All calculated average number of citations per document to show the academic impact of journals with a constant weight or influence even if niche.
Collaboration Networks: The co-authorship analysis was employed to delineate the collaboration networks within the discipline. This analysis identifies collaborations among institutions and across nations, illustrating the extent of globalization in behavioral finance research.
Bibliometric Analysis
Year-wise Published Documents:
The number of publications per year shows the growth of the field, and the spikes correspond to the global trends in the study of Behavioral Finance. This analysis also shows how the research activity fits into global trends, which provides an understanding of the external factors that influence academic interest. As per the below-mentioned table and figure, highest publication i.e., 1568 articles in the year of 2025 and the second highest publication (1389 articles) in the year 2024.
Table 1: Year-wise Published Articles
|
Year |
2021 |
2022 |
2023 |
2024 |
2025 |
2026 |
Total |
Average Publication Per Year |
|
Number of Publications |
678 |
714 |
1053 |
1389 |
1568 |
280 |
5682 |
947 |
Source: https://app.dimensions.ai/
Exported: March 15, 2026
Criteria: Evaluating Global Trends in the Study of Behavioural Finance, 2026 or 2025 or 2024 or 2023 or 2022 or 2021, Article, Sustainability or PLOS ONE or IEEE Access or Cogent Business & Management or Journal of Cleaner Production or SAGE Open or Journal of Business Research or Technological Forecasting and Social Change or Heliyon or Environment, Development and Sustainability
Figure 1: Year-wise Published Articles
Network Map - Country-wise Publication and Citation Analysis
The country-wise collaboration networks show the density of international connections and the regions with the most academic collaboration. The distribution of publications by country shows that developed countries are the most productive, although the role of developing countries is steadily increasing, like India and China. As per the below-mentioned table and figure, there are a total of 77 items, 7 clusters and 2860 links. Further, as per the below-mentioned figures 3 and 4, China, United States, UK, India and Malaysia are the topmost countries for conducting the research in the field of behavioural finance. Further, it is evident from the below-mentioned table that China is the topmost country for citations and India is the second most country for citation whereas the United States and UK is the third and fourth country for the citation link.
Table 2: Country-wise Publication and Citation Analysis
Figure 2: Network Map-Country-wise Publication and Citation Analysis
Figure 3: Article Publication Density-Country wise Publication and Citation Analysis
Network Map - Based on Selected Sources and Citation
The result shows there is substantial growth and increasing academic recognition in the field of behavioral finance. The sustainability leads research output and citation impact in behavioral finance, with 589 publications and 7922 citations and Plos One follows with 530 publications and 6144 citations. The Heliyan are the third top selected sources in this field, with 340 publication and 7868 citations, respectively. The findings also highlight a notable rise in international collaborations and an increase in the average number of publications globally in behavioral finance.
Table 3: Selected Sources and Citation
Figure 4 : Network Map Visualisation based on Selected Sources and Citation
Figure 5: Article Publication Density Visualisation based on Selected Sources and Citation
Research limitations and Implications
The limitations of the current study are few in terms of bibliometric context; it relied solely on the Dimensions AI database (https://app.dimensions.ai/) thus, future studies can draw a more comprehensive dataset to provide an insightful bibliometric analysis with strong conceptual frameworks and themes emerging from behavioral finance domain for which this study contributes significantly. In addition, the present study was exclusively based on published articles. Conference proceedings, books chapters and white papers were not included in this study.
This study offers significant insights for academicians, researchers and industry practitioners. This research may aid managers in recognizing the latest improvements in behavioral finance and clarifying how these developments impact effective investing decisions.
The results of this study are expected to contribute to a better understanding of behavioral finance and to encourage a more integrated approach to addressing multifaceted financial behaviors in a rapidly evolving global environment. The domain of behavioral finance aims to examine how investors' psychology affects their financial decisions. Theorists in behavioral finance demonstrate the realities of market crashes, bubbles, and anomalies by excluding behavioral elements from asset pricing models. This study enhances the existing body of knowledge in behavioral finance thorough bibliometric analysis, presenting a unique viewpoint on the academic advancement and changing framework of research in this domain.
Alam, M. R., Ahmad, A., Akhter, J., & Aziz, T. (2025). Mapping the field of behavioral finance: a systematic review and bibliometric analysis. Asian Education and Development Studies, 1–23. https://doi.org/10.1108/aeds-07-2024-0162
Vijay Kumar, V. M., Kumar, V. L., Dupam, S. R., & Ghayas, A. (2026). Behavioral finance: a four-decade bibliometric exploration. Journal of Economics, Finance and Administrative Science, 1–48. https://doi.org/10.1108/jefas-06-2025-0203
Ahmad, A., Alam, M. R., Akhter, J., & Aziz, T. (2024). Research on behavioural finance: a systematic review and bibliometric analysis. International Journal of Behavioural Accounting and Finance, 7(3), 314–335. https://doi.org/10.1504/ijbaf.2024.143838
Kumar, G., & Choudhary, K. (2023). Behavioural Finance: A Review of Major Research Themes and Bibliometric Analysis. Eurasian Journal of Business and Economics, 16(32), 1–22. https://doi.org/10.17015/ejbe.2023.032.01
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Singh, B. (2021). A bibliometric analysis of behavioral finance and behavioral accounting. American Business Review, 24(2), 198–230. https://doi.org/10.37625/ABR.24.2.198-230
Ingale, K. K., & Paluri, R. A. (2020). Financial literacy and financial behaviour: a bibliometric analysis. Review of Behavioral Finance, ahead-of-print(ahead-of-print). https://doi.org/10.1108/rbf-06-2020-0141
Paule-Vianez, J., Gómez-Martínez, R., & Prado-Román, C. (2020a). A bibliometric analysis of behavioural finance with mapping analysis tools. European Research on Management and Business Economics, 26(2), 71–77. https://doi.org/10.1016/j.iedeen.2020.01.001
Paule-Vianez, J., Gómez-Martínez, R., & Prado-Román, C. (2020b). A bibliometric analysis of behavioural finance with mapping analysis tools. European Research on Management and Business Economics, 26(2), 71–77. https://doi.org/10.1016/j.iedeen.2020.01.001
van Eck, N. J., & Waltman, L. (2014). Visualizing Bibliometric Networks. Measuring Scholarly Impact, 285–320. https://doi.org/10.1007/978-3-319-10377-8_13
Zhang, Y., & Chen, X. (2023). Empirical Analysis of University–Industry Collaboration in Postgraduate Education: A Case Study of Chinese Universities of Applied Sciences. Sustainability, 15(7), 6252–6252. https://doi.org/10.3390/su15076252
Zhou, P., Tijssen, R., & Leydesdorff, L. (2016). University-Industry Collaboration in China and the USA: A Bibliometric Comparison. PLOS ONE, 11(11), e0165277. https://doi.org/10.1371/journal.pone.0165277
Download
Download XML