Overconfidence bias among retail investors: A systematic review and future research directions
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Author(s)Dharmendra SinghLink to ORCID Index: https://orcid.org/0000-0003-0966-6530
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Garima MalikLink to ORCID Index: https://orcid.org/0000-0002-3892-8299
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Aruna JhaLink to ORCID Index: https://orcid.org/0000-0002-2850-9576
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DOIhttp://dx.doi.org/10.21511/imfi.21(1).2024.23
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Article InfoVolume 21 2024, Issue #1, pp. 302-316
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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
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JEL Classification (Paper profile tab)G11, G41, G53
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References75
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Tables7
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Figures4
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- Figure 1. PRISMA diagram
- Figure 2. Publication trends
- Figure 3. Keyword co-occurrence map
- Figure 4. Percentage distribution of interrelated cognitive biases
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- 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
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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
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Myroslav Kulchytskyy ,
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doi: http://dx.doi.org/10.21511/bbs.16(1).2021.08
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Ramzi Nekhili
doi: http://dx.doi.org/10.21511/imfi.17(3).2020.01
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