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Are crypto-investors overconfident? The role of risk propensity and demographics. Evidence from Brazil and Portugal

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  • Gustavo Iamin

Abstract

Purpose - The crypto market is growing quickly, marked by a lack of fundamentals, and the risks are not yet fully comprehended by participants. Our goal is to investigate overconfidence in this market and analyze the role that risk propensity and certain demographics play. Design/methodology/approach - We conducted a survey in Brazil and Portugal, leveraging an online questionnaire disseminated via social media channels to engage a diverse adult population. We collected a total of 826 responses, addressing ethical considerations throughout the process. The data analysis was conducted using SPSS statistical software and logit regression modeling. Findings - Our study reveals that overconfidence is a notable bias that distinguishes individuals who invest in cryptocurrencies from those who do not. Although overconfidence and risk propensity are closely linked, they originate from distinct personal characteristics. Furthermore, our findings indicate that age and market experience positively correlate with overconfidence and negatively correlate with risk propensity. Financial knowledge, interestingly, did not prove to be a significant factor for cryptocurrency investment. Originality/value - Our research augments the existing literature on overconfidence, delving into this phenomenon in a new subdomain, and in doing so, it enriches our comprehension of the unique and still relatively under-researched cryptomarket. Moreover, we illuminate individual factors that sway the decision to invest in cryptocurrencies and should be considered by market participants. Highlights - (1)Pioneering work examining the presence of overconfidence bias among crypto-investors, using a robust data set collected from a binational survey.(2)Verifies the relations among overconfidence, risk propensity, and demographics.(3)Examines the influence of age and experience on investment decisions, revealing a positive relationship with overconfidence and a negative correlation with risk propensity.(4)Logistic regression is used to determine the combined effect of overconfidence, risk propensity, and demographics on the decision to invest in cryptocurrencies.

Suggested Citation

  • Gustavo Iamin, 2024. "Are crypto-investors overconfident? The role of risk propensity and demographics. Evidence from Brazil and Portugal," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 26(1), pages 147-173, November.
    • Handle: RePEc:eme:jrfpps:jrf-04-2024-0109
    DOI: 10.1108/JRF-04-2024-0109
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    References listed on IDEAS

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    1. Bouri, Elie & Gupta, Rangan & Roubaud, David, 2019. "Herding behaviour in cryptocurrencies," Finance Research Letters, Elsevier, vol. 29(C), pages 216-221.
    2. Grobys, Klaus & Ahmed, Shaker & Sapkota, Niranjan, 2020. "Technical trading rules in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 32(C).
    3. Katsiampa, Paraskevi, 2019. "An empirical investigation of volatility dynamics in the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 50(C), pages 322-335.
    4. Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2019. "On long memory effects in the volatility measure of Cryptocurrencies," Finance Research Letters, Elsevier, vol. 28(C), pages 95-100.
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    More about this item

    Keywords

    Cryptocurrencies; Investor behavior; Bias; Overconfidence; Risk propensity; Demographics; G11; G12; G14; G15;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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