Beyond Rationality: A Systematic Literature Review of Overconfidence Bias in Investment Decision-Making from a Behavioral Finance Perspective
DOI:
https://doi.org/10.58857/JFAE.2026.v03.i02.p05Keywords:
Overconfidence, Behavioral Finance, Investment Decision, Systematic Literature ReviewAbstract
The development of global financial markets, the digitalization of investment services, and the increasing participation of retail investors have changed the characteristics of investment decision-making. Under these conditions, investment decisions are no longer solely influenced by rational analysis as assumed in traditional financial theory, but are also influenced by various psychological biases, particularly overconfidence. This bias encourages investors to overestimate their abilities, knowledge, and predictive accuracy, potentially resulting in suboptimal investment decisions. This study aims to systematically synthesize the development of literature on the influence of overconfidence on investment decisions from a behavioral finance perspective, identify dominant research themes, evaluate the consistency of empirical findings, and uncover research gaps that still require development. The study used a Systematic Literature Review (SLR) approach with reference to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The literature selection process was carried out on articles published between 2015 and 2024 through the Scopus, ScienceDirect, SpringerLink, Taylor & Francis, and Google Scholar databases, resulting in ten articles that met all inclusion criteria for analysis.
The synthesis results indicate that overconfidence is a significant determinant of investment decision-making. Five key themes were identified: the influence of overconfidence on investment activity intensity, increased risk appetite, the influence of demographic characteristics such as gender and generation, the role of investment digitalization in reinforcing behavioral biases, and the consistency of overconfidence across developing countries. In addition to strengthening the relevance of behavioral finance as an approach capable of explaining the limitations of investor rationality assumptions, this study also demonstrates that digital transformation has the potential to reinforce the illusion of knowledge and the illusion of control, thereby increasing investors' tendency to make more aggressive investment decisions. This study provides theoretical contributions by mapping the development of the literature on overconfidence and provides practical implications for investors, regulators, and digital investment platform developers in designing strategies to mitigate behavioral biases and strengthen financial literacy. Furthermore, this study identifies the need for longitudinal research and the development of models that integrate financial literacy, financial technology, artificial intelligence, and risk tolerance to broaden understanding of investment behavior in the era of digital transformation.
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References
Bai, Z. (2024). Examining the Association Between Robo-Advisory and Perceived Financial Satisfaction. Review of Behavioral Finance, 16(4), 668–681. https://doi.org/10.1108/rbf-10-2023-0268
Baker, H. K., Kapoor, S., & Khare, T. (2022). Personality Traits and Behavioral Biases of Indian Financial Professionals. Review of Behavioral Finance, 15(6), 846–864. https://doi.org/10.1108/rbf-11-2021-0246
Ballis, A., & Verousis, T. (2022). Behavioural Finance and Cryptocurrencies. Review of Behavioral Finance, 14(4), 545–562. https://doi.org/10.1108/rbf-11-2021-0256
Bouri, E., Gabauer, D., Gupta, R., & Tiwari, A. K. (2021). Volatility Connectedness of Major Cryptocurrencies: The Role of Investor Happiness. Journal of Behavioral and Experimental Finance, 30, 100463. https://doi.org/10.1016/j.jbef.2021.100463
Daragmeh, A., Lentner, C., & Sági, J. (2021). FinTech Payments in the Era of COVID-19: Factors Influencing Behavioral Intentions of “Generation X” in Hungary to Use Mobile Payment. Journal of Behavioral and Experimental Finance, 32, 100574. https://doi.org/10.1016/j.jbef.2021.100574
Dhawan, A., & Putniņš, T. J. (2022). A New Wolf in Town? Pump-and-Dump Manipulation in Cryptocurrency Markets. Review of Finance, 27(3), 935–975. https://doi.org/10.1093/rof/rfac051
Dubey, P. (2022). Short-Run and Long-Run Determinants of Bitcoin Returns: Transnational Evidence. Review of Behavioral Finance, 14(4), 533–544. https://doi.org/10.1108/rbf-02-2022-0040
Escobar, L., & Pedraza, Á. (2023). Active Trading and (Poor) Performance: The Social Transmission Channel. Journal of Financial Economics, 150(1), 139–165. https://doi.org/10.1016/j.jfineco.2023.103706
Feldman, T., & Lepori, G. M. (2016). Asset Price Formation and Behavioral Biases. Review of Behavioral Finance, 8(2), 137–155. https://doi.org/10.1108/rbf-05-2015-0020
Fonseca, A. d. R., Jorge, S., & Nascimento, C. (2020). The Role of Internal Auditing in Promoting Accountability in Higher Education Institutions. Revista De Administração Pública, 54(2), 243–265. https://doi.org/10.1590/0034-761220190267x
Gaio, C., & Gonçalves, T. C. (2022). Gender Diversity on the Board and Firms’ Corporate Social Responsibility. International Journal of Financial Studies, 10(1), 15. https://doi.org/10.3390/ijfs10010015
Gavrilakis, N., & Floros, C. (2021). The Impact of Heuristic and Herding Biases on Portfolio Construction and Performance: The Case of Greece. Review of Behavioral Finance, 14(3), 436–462. https://doi.org/10.1108/rbf-11-2020-0295
Goyal, P., Gupta, P., & Yadav, V. (2021). Antecedents to Heuristics: Decoding the Role of Herding and Prospect Theory for Indian Millennial Investors. Review of Behavioral Finance, 15(1), 79–102. https://doi.org/10.1108/rbf-04-2021-0073
Hasso, T., Pelster, M., & Breitmayer, B. (2019). Who Trades Cryptocurrencies, How Do They Trade It, and How Do They Perform? Evidence From Brokerage Accounts. Journal of Behavioral and Experimental Finance, 23, 64–74. https://doi.org/10.1016/j.jbef.2019.04.009
Hellström, J., Olsson, R., & Stålnacke, O. (2017). Evaluating Measures of Individual Investors’ Expectations of Risk and Return. Review of Behavioral Finance, 9(3), 206–226. https://doi.org/10.1108/rbf-10-2016-0066
Howell, L. P., Wahl, S. C., Ryan, J. F., Gandour‐Edwards, R., & Green, R. (2019). Educational and Career Development Outcomes Among Undergraduate Summer Research Interns: A Pipeline for Pathology, Laboratory Medicine, and Biomedical Science. Academic Pathology, 6, 2374289519893105. https://doi.org/10.1177/2374289519893105
Kiky, A., Atahau, A. D. R., Mahastanti, L. A., & Supatmi, S. (2024). Framing Effect and Disposition Effect: Investment Decisions Tools to Understand Bounded Rationality. Review of Behavioral Finance, 16(5), 883–903. https://doi.org/10.1108/rbf-11-2023-0311
Kumar, A. (2020). Empirical Investigation of Herding in Cryptocurrency Market Under Different Market Regimes. Review of Behavioral Finance, 13(3), 297–308. https://doi.org/10.1108/rbf-01-2020-0014
Lahtinen, K. D. (2019). Motivating Gender Use in Financial Research: A Survey of Recent Literature. Review of Behavioral Finance, 12(3), 242–258. https://doi.org/10.1108/rbf-06-2018-0056
Liu, C., Sulaeman, J., Shu, T., & Yeung, P. E. (2022). Life Is Too Short? Bereaved Managers and Investment Decisions. Review of Finance, 27(4), 1373–1421. https://doi.org/10.1093/rof/rfac067
Mushinada, V. N. C., & Veluri, V. S. S. (2019). Elucidating Investors Rationality and Behavioural Biases in Indian Stock Market. Review of Behavioral Finance, 11(2), 201–219. https://doi.org/10.1108/rbf-04-2018-0034
Nguyen, L. T. M., & Nguyen, P. T. (2023). Do Crypto Investors Wait and See During Policy Uncertainty? An Examination of the Dynamic Relationships Between Policy Uncertainty and Exchange Inflows of Bitcoin. Review of Behavioral Finance, 16(2), 234–247. https://doi.org/10.1108/rbf-01-2023-0013
Nigam, R. M., Srivastava, S., & Banwet, D. K. (2018). Behavioral Mediators of Financial Decision Making – A State-of-Art Literature Review. Review of Behavioral Finance, 10(1), 2–41. https://doi.org/10.1108/rbf-07-2016-0047
Pearson, B., & Korankye, T. (2022). The Association Between Financial Literacy Confidence and Financial Satisfaction. Review of Behavioral Finance, 15(6), 935–946. https://doi.org/10.1108/rbf-03-2022-0090
Reher, M., & Sokolinski, S. (2024). Robo Advisors and Access to Wealth Management. Journal of Financial Economics, 155, 103829. https://doi.org/10.1016/j.jfineco.2024.103829
Shrotryia, V. K., & Kalra, H. (2021). Herding in the Crypto Market: A Diagnosis of Heavy Distribution Tails. Review of Behavioral Finance, 14(5), 566–587. https://doi.org/10.1108/rbf-02-2021-0021
Souza, V. V., Lucas Lopes Ferreira de Souza, Oliveira, O., Souza, E. M. d., & Costa, J. S. (2023). Do Heuristics Affect Brazilian Investors’ Decision-Making Process? Review of Behavioral Finance, 16(2), 282–299. https://doi.org/10.1108/rbf-09-2022-0210
Trejos, C., Deemen, A. V., Rodríguez, Y. E., & Gómez, J. M. (2019). Overconfidence and Disposition Effect in the Stock Market: A Micro World Based Setting. Journal of Behavioral and Experimental Finance, 21, 61–69. https://doi.org/10.1016/j.jbef.2018.11.001
Wang, S. (2023). Risk Preference, Payday Loans and Other Alternative Financial Services. Review of Behavioral Finance, 16(4), 581–599. https://doi.org/10.1108/rbf-04-2023-0099
