Overconfidence bias among retail investors: A systematic review and future research directions
-
Author(s)Dharmendra SinghLink to ORCID Index: https://orcid.org/0000-0003-0966-6530
,
Garima MalikLink to ORCID Index: https://orcid.org/0000-0002-3892-8299
,
Aruna JhaLink to ORCID Index: https://orcid.org/0000-0002-2850-9576
-
DOIhttp://dx.doi.org/10.21511/imfi.21(1).2024.23
-
Article InfoVolume 21 2024, Issue #1, pp. 302-316
- Cited by
- 123 Views
-
58 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
This paper comprehensively evaluates the literature on retail investor overconfidence using a framework-based systematic approach to understand the various dimensions of overconfidence bias, its effect on investing choices, and market dynamics. A systematic review of 137 publications from the Scopus database have been done to detect the research trend concerning investor overconfidence bias from its inception. An integrated ADO-TCM framework has been employed to present a systematic analysis of the theory, context, and methodologies (TCM) employed in the reviewed studies. The ADO (Antecedents, Decisions, and Outcomes) framework thoroughly examines the antecedents, decisions, and results of investor overconfidence.
The study identified four broad sets of factors contributing to investor overconfidence, as found in the existing literature. These factors include demographic characteristics, personality traits of investors, their knowledge and experience, and the features of investments and investor types. The Prospect theory is the most popular theory in the literature, with much research using secondary data and experiment-based analysis. The prospective study directions, based on the gaps in the existing literature, are as follows: further investigation into the decision-making processes of overconfident retail and professional investors is a worthwhile subject. Future research may shift their focus from financial outcome variables to non-financial outcome variables such as the impact of investor overconfidence on individuals’ stress levels, subjective financial well-being, and overall life happiness.
- Keywords
-
JEL Classification (Paper profile tab)G11, G41, G53
-
References75
-
Tables7
-
Figures4
-
- Figure 1. PRISMA diagram
- Figure 2. Publication trends
- Figure 3. Keyword co-occurrence map
- Figure 4. Percentage distribution of interrelated cognitive biases
-
- Table 1. Leading articles on overconfidence research
- Table 2. Leading authors in overconfidence research
- Table 3. Trends of author keywords in investor overconfidence in the last ten years (2014–2023)
- Table 4. Percentage distribution of overconfidence studies in the top 10 countries
- Table 5. Percentage distribution of the antecedents
- Table 6. Percentage distribution of the decision variables
- Table 7. Percentage distribution of the outcome variables
-
- Adel, B., & Mariem, T. (2013). The impact of overconfidence on investors’ decisions. Business and Economic Research, 3(2), 53-75.
- Ahmad, F. (2020). Personality traits as predictor of cognitive biases: Moderating role of risk-attitude. Qualitative Research in Financial Markets, 12(4), 465-484.
- Ahmad, M., & Shah, S. Z. A. (2020). Overconfidence heuristic-driven bias in investment decision-making and performance: Mediating effects of risk perception and moderating effects of financial literacy. Journal of Economic and Administrative Sciences, 38(1), 60-90.
- Akhtar, F., & Das, N. (2020). Investor personality and investment performance: From the perspective of psychological traits. Qualitative Research in Financial Markets, 12(3), 333-352.
- Alves, C. F. (2023). A note on financial literacy among literate people and their participation in different securities market segments. Studies in Economics and Finance, 40(2), 286-301.
- Baker, H. K., Kumar, S., Goyal, N., & Gaur, V. (2019). How financial literacy and demographic variables relate to behavioral biases. Managerial Finance, 45(1), 124-146.
- Baker, H. K., & Nofsinger, J. R. (2002). Psychological biases of investors. Financial Services Review, 11(2), 97-117.
- Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-151.
- Barber, B. M., & Odean, T. (2000). Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors. The Journal of Finance, 55(2), 773-806.
- Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292.
- Benos, A. V. (1998). Aggressiveness and survival of overconfident traders. Journal of Financial Markets, 1(3-4), 353-383.
- Bhandari, G., & Deaves, R. (2006). The Demographics of Overconfidence. Journal of Behavioral Finance, 7(1), 5-11.
- Bhatia, R., Bhat, A. K., & Tikoria, J. (2021). Life insurance purchase behaviour: A systematic review and directions for future research. International Journal of Consumer Studies, 45(6), 1149-1175.
- Biais, B., Hilton, D., Mazurier, K., & Pouget, S. (2005). Judgemental overconfidence, self-monitoring, and trading performance in an experimental financial market. The Review of Economic Studies, 72(2), 287-312.
- Breuer, W., Riesener, M., & Salzmann, A. J. (2014). Risk aversion vs. individualism: What drives risk taking in household finance? The European Journal of Finance, 20(5), 446-462.
- Broihanne, M.-H., Merli, M., & Roger, P. (2014). Overconfidence, risk perception and the risk-taking behavior of finance professionals. Finance Research Letters, 11(2), 64-73.
- Chen, G., Kim, K. A., Nofsinger, J. R., & Rui, O. M. (2007). Trading performance, disposition effect, overconfidence, representativeness bias, and experience of emerging market investors. Journal of Behavioral Decision Making, 20(4), 425-451.
- Chu, W., Im, M., & Jang, H. (2012). Overconfidence and emotion regulation failure: How overconfidence leads to the disposition effect in consumer investment behaviour. Journal of Financial Services Marketing, 17(1), 96-116.
- Czaja, D., & Röder, F. (2020). Self-attribution bias and overconfidence among nonprofessional traders. The Quarterly Review of Economics and Finance, 78, 186-198.
- Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor Psychology and Security Market Under- and Overreactions. The Journal of Finance, 53(6), 1839-1885.
- Deaves, R., Lüders, E., & Luo, G. Y. (2009). An Experimental Test of the Impact of Overconfidence and Gender on Trading Activity. Review of Finance, 13(3), 555-575.
- Deaves, R., Lüders, E., & Schröder, M. (2010). The dynamics of overconfidence: Evidence from stock market forecasters. Journal of Economic Behavior & Organization, 75(3), 402-412.
- 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.
- Dorn, D., & Huberman, G. (2005). Talk and action: What individual investors say and what they do. Review of Finance, 9(4), 437-481.
- Durand, R., Newby, R., Tant, K., & Trepongkaruna, S. (2013). Overconfidence, overreaction and personality. Review of Behavioral Finance, 5(2), 104-133.
- Duy Bui, L., Chi Le, T., Ngoc Quang, A. H., & Wong, W.-K. (2021). Determinants of the possibilities by investors’ risk-taking: Empirical evidence from Vietnam. Cogent Economics & Finance, 9(1), 1917106.
- Gervais, S., & Odean, T. (2001). Learning to Be Overconfident. The Review of Financial Studies, 14(1), 1-27.
- Glaser, M., & Weber, M. (2007). Overconfidence and trading volume. The Geneva Risk and Insurance Review, 32(1), 1-36.
- Glaser, M., & Weber, M. (2010). Overconfidence. Behavioral Finance: Investors, Corporations, and Markets, 241-258.
- Goyal, K., & Kumar, S. (2021). Financial literacy: A systematic review and bibliometric analysis. International Journal of Consumer Studies, 45(1), 80-105.
- Goyal, P., Gupta, P., & Yadav, V. (2023). Antecedents to heuristics: Decoding the role of herding and prospect theory for Indian millennial investors. Review of Behavioral Finance, 15(1), 79-102.
- Grežo, M. (2021). Overconfidence and financial decision-making: A meta-analysis. Review of Behavioral Finance, 13(3), 276-296.
- Grinblatt, M., & Keloharju, M. (2009). Sensation Seeking, Overconfidence, and Trading Activity. The Journal of Finance, 64(2), 549-578.
- Hsu, Y.-L. (2022). Financial advice seeking and behavioral bias. Finance Research Letters, 46, 102505.
- Islam Khan, M. T., Tan, S.-H., & Chong, L.-L. (2016). The effects of stated preferences for firm characteristics, optimism and overconfidence on trading activities. International Journal of Bank Marketing, 34(7), 1114-1130.
- Jain, J., Walia, N., & Gupta, S. (2019). Evaluation of behavioral biases affecting investment decision making of individual equity investors by fuzzy analytic hierarchy process. Review of Behavioral Finance, 12(3), 297-314.
- Jaiyeoba, H. B., Abdullah, M. A., & Ibrahim, K. (2019). Institutional investors vs retail investors: Are psychological biases equally applicable to investor divides in Malaysia? International Journal of Bank Marketing, 38(3), 671-691.
- Jiang, J., Liao, L., Wang, Z., & Xiang, H. (2020). Financial literacy and retail investors’ financial welfare: Evidence from mutual fund investment outcomes in China. Pacific-Basin Finance Journal, 59, 101242.
- Kahneman, D., & Riepe, M. W. (1998). Aspects of investor psychology. Journal of Portfolio Management, 24(4), 52.
- Kahneman, D., & Tversky, A. (2013). Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I (pp. 99-127). World Scientific.
- Kansal, P., & Singh, S. (2018). Determinants of overconfidence bias in Indian stock market. Qualitative Research in Financial Markets, 10(4), 381-394.
- Khan, M. T. I., Tan, S.-H., & Chong, L.-L. (2017). How past perceived portfolio returns affect financial behaviors – The underlying psychological mechanism. Research in International Business and Finance, 42, 1478-1488.
- Kleine, J., Wagner, N., & Weller, T. (2016). Openness endangers your wealth: Noise trading and the big five. Finance Research Letters, 16, 239-247.
- Koo, J.-H., & Yang, D. (2018). Managerial Overconfidence, Self-Attribution Bias, and Downwardly Sticky Investment: Evidence from Korea. Emerging Markets Finance and Trade, 54(1), 144-161.
- Kumar, S., & Goyal, N. (2016). Evidence on rationality and behavioural biases in investment decision making. Qualitative Research in Financial Markets, 8(4), 270-287.
- Lin, H.-W. (2011). Elucidating rational investment decisions and behavioral biases: Evidence from the Taiwanese stock market. African Journal of Business Management, 5(5), 1630.
- Menkhoff, L., Schmeling, M., & Schmidt, U. (2013). Overconfidence, experience, and professionalism: An experimental study. Journal of Economic Behavior & Organization, 86, 92-101.
- Merkle, C. (2017). Financial overconfidence over time: Foresight, hindsight, and insight of investors. Journal of Banking & Finance, 84, 68-87.
- Michailova, J., & Schmidt, U. (2016). Overconfidence and Bubbles in Experimental Asset Markets. Journal of Behavioral Finance, 17(3), 280-292.
- Mishra, K. C., & Metilda, M. J. (2015). A study on the impact of investment experience, gender, and level of education on overconfidence and self-attribution bias. IIMB Management Review, 27(4), 228-239.
- Moore, D. A., & Healy, P. J. (2008). The trouble with overconfidence. Psychological Review, 115(2), 502-517.
- Nosić, A., & Weber, M. (2010). How Riskily Do I Invest? The Role of Risk Attitudes, Risk Perceptions, and Overconfidence. Decision Analysis, 7(3), 282-301.
- Oberlechner, T., & Osler, C. (2012). Survival of overconfidence in currency markets. Journal of Financial and Quantitative Analysis, 47(1), 91-113.
- Odean, T. (1998a). Are Investors Reluctant to Realize Their Losses? The Journal of Finance, 53(5), 1775-1798.
- Odean, T. (1998b). Volume, volatility, price, and profit when all traders are above average. The Journal of Finance, 53(6), 1887-1934.
- Odean, T. (1999). Do Investors Trade Too Much? American Economic Review, 89(5), 1279-1298.
- Osakwe, C. N., Dzandu, M. D., Amegbe, H., Warsame, M. H., & Ramayah, T. (2022). A two-country study on the psychological antecedents to cryptocurrency investment decision-making. Journal of Global Information Technology Management, 25(4), 302-323.
- Pak, T.-Y., & Chatterjee, S. (2016). Aging, overconfidence, and portfolio choice. Journal of Behavioral and Experimental Finance, 12, 112-122.
- Pan, C. H., & Statman, M. (2013). Investor personality in investor questionnaires. Journal of Investment Consulting, 14(1), 48-56.
- Paul, J., & Rosado-Serrano, A. (2019). Gradual Internationalization vs Born-Global/International new venture models: A review and research agenda. International Marketing Review, 36(6), 830-858.
- Pikulina, E., Renneboog, L., & Tobler, P. N. (2017). Overconfidence and investment: An experimental approach. Journal of Corporate Finance, 43, 175-192.
- Prosad, J. M., Kapoor, S., & Sengupta, J. (2015). Behavioral biases of Indian investors: A survey of Delhi-NCR region. Qualitative Research in Financial Markets, 7(3), 230-263.
- Sabir, S. A., Mohammad, H. B., & Shahar, H. B. K. (2019). The role of overconfidence and past investment experience in herding behaviour with a moderating effect of financial literacy: Evidence from Pakistan stock exchange. Asian Economic and Financial Review, 9(4), 480.
- Sharma, D., Taggar, R., Bindra, S., & Dhir, S. (2020). A systematic review of responsiveness to develop future research agenda: A TCCM and bibliometric analysis. Benchmarking: An International Journal, 27(9), 2649-2677.
- Singh, D., & Malik, G. (2022). A systematic and bibliometric review of the financial well-being: Advancements in the current status and future research agenda. International Journal of Bank Marketing, 40(7), 1575-1609.
- Statman, M., Thorley, S., & Vorkink, K. (2006). Investor Overconfidence and Trading Volume. The Review of Financial Studies, 19(4), 1531-1565.
- Suchanek, M. (2021). The dark triad and investment behavior. The dark triad and investment behavior. Journal of Behavioral and Experimental Finance, 29, 100457.
- Syed, R. T., Singh, D., Agrawal, R., & Spicer, D. P. (2023). Entrepreneurship development in universities across Gulf Cooperation Council countries: A systematic review of the research and way forward. Journal of Enterprising Communities: People and Places in the Global Economy, 17(5), 1045-1062.
- Syed, R. T., Singh, D., & Spicer, D. (2023). Entrepreneurial higher education institutions: Development of the research and future directions. Higher Education Quarterly, 77(1), 158-183.
- Tabash, M. I., Shekhar, S., Singh, P., Shamshad, M., & Al-Absy, M. S. M. (2023). Synthesizing social insurance research: A bibliometric analysis. Insurance Markets and Companies, 14(1), 59.
- Tekçe, B., Yılmaz, N., & Bildik, R. (2016). What factors affect behavioral biases? Evidence from Turkish individual stock investors. Research in International Business and Finance, 37, 515-526.
- Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty. Science, 185(4157), 1124-1131.
- van Rooij, M., Lusardi, A., & Alessie, R. (2011). Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449-472.
- Waweru, N. M., Munyoki, E., & Uliana, E. (2008). The effects of behavioural factors in investment decision-making: A survey of institutional investors operating at the Nairobi Stock Exchange. International Journal of Business and Emerging Markets, 1(1), 24.
- Wilaiporn, P., Nongnit, C., & Surachai, C. (2021). Factors Influencing retail investors’ trading behaviour in the Thai stock market.
-
Multi-agent modeling and simulation of a stock market
Mohamed Amine Souissi ,
Khalid Bensaid ,
Rachid Ellaia
doi: http://dx.doi.org/10.21511/imfi.15(4).2018.10
Investment Management and Financial Innovations Volume 15, 2018 Issue #4 pp. 123-134 Views: 2108 Downloads: 930 TO CITE АНОТАЦІЯThe stock market represents complex systems where multiple agents interact. The complexity of the environment in the financial markets in general has encouraged the use of modeling by multi-agent platforms and particularly in the case of the stock market.
In this paper, an agent-based simulation model is proposed to study the behavior of the volume of market transactions. The model is based on the case of a single asset and three types of investor agents. Each investor can be a zero intelligent trader, fundamentalist trader or traders using historical information in the decision making process. The goal of the study is to simulate the behavior of a stock market according to the different considered endogenous and exogenous variables. -
The level of digital transformation affecting the competitiveness of banks
Oleh Kolodiziev 
,
Mykhailo Krupka ,
Nataliya Shulga ,
Myroslav Kulchytskyy ,
Olha Lozynska
doi: http://dx.doi.org/10.21511/bbs.16(1).2021.08
Banks and Bank Systems Volume 16, 2021 Issue #1 pp. 81-91 Views: 1517 Downloads: 598 TO CITE АНОТАЦІЯThe article examines the competitiveness of Ukrainian banks influenced by economy digitalization, the dynamic spread of electronic payments and e-commerce, as well as innovative technologies aimed at providing digital services. When shifting to an Online Platform business model, a bank can expand its range of banking products, attract more customers, thereby forming a competition policy and gaining competitive advantages. The paper aims to assess the digitalization level affecting the general competitiveness of banks and its components based on Ukrainian banks. For this purpose, the following methods were used: standardized input statistical indicators, comparison and ranking, a cluster analysis, and a regression and correlation analysis. The cluster analysis confirmed the current role of digitalization as a competition driver that determines the competitive advantages of banks and creates additional opportunities to expand the customer base and the range of services. The correlation and regression dependence of the competitive position identified by the activity indicators of certain banks on the level of competitive digitalization confirmed a close direct impact on the competitive position of personal deposits arising from the development of digital banking technology; the pre-tax income, profiles of assets and personal loans, and corporate deposits are subject to a significant direct impact, while the weakest direct impact determines corporate loans. The foregoing substantiates the feasibility of large-scale introduction of innovative digital technologies by banks to maintain competitive positions in the banking sector of the economy. Applying the proposed approach based on certain regression equations, managers of Ukrainian banks will be able to assess the efficiency and make appropriate decisions concerning investing in digital tools and services.
-
Are bitcoin futures contracts for hedging or speculation?
Ramzi Nekhili
doi: http://dx.doi.org/10.21511/imfi.17(3).2020.01
Investment Management and Financial Innovations Volume 17, 2020 Issue #3 pp. 1-9 Views: 1331 Downloads: 478 TO CITE АНОТАЦІЯThe emerging interest in Bitcoin futures market has led to questions on its trading form and contribution to risk minimization. These questions are important for market participants, including hedgers and speculators. This paper addresses the possible trading motive in Bitcoin futures market in being speculation or hedging. The author first tests a model relating Bitcoin futures returns with trading volume and conditional volatility, estimated with a GJR-GARCH specification, on a full sample of daily futures prices. A robustness check is then conducted by investigating the hedging effectiveness of Bitcoin futures and the speculation-hedging ratios on individual Bitcoin futures contracts. The estimation results on Bitcoin futures contracts, spanning from December 2017 to February 2020, show a significant positive relationship between futures returns and lagged volume. The speculation-hedging measures used for Bitcoin futures contracts maturing in March, June, September, and December reveal an increasing demand for speculation. Also, the Bitcoin spot’s full-hedge and OLS-hedge strategies with Bitcoin futures provide no gain over a no-hedge strategy. The results reveal strong evidence that traders in the Bitcoin futures market are motivated by speculation rather than hedging. This further puts in evidence the existence of asymmetric information within informed traders in Bitcoin futures market, and therefore market participants would not insure their positions against Bitcoin price movements.
