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Can self-assessed financial risk measures explain and predict bank customers’ objective financial risk?

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  • Hermansson, Cecilia

Abstract

This paper evaluates risk preference measures by contrasting subjective or self-assessed risk with objective risk, as implicated by bank customers’ actual portfolio allocation. Using a detailed data set of 7,234 bank customers, we find that subjective risk measures can explain and predict objective risk, but that the relationship is relatively weak. Subjective measures that uses survey questions about the customers’ trade-off between risk and return is a better measure than the hypothetical lottery for explaining objective risk. Both measures are relatively weak at predicting objective risk, but perform better than using a naïve model. We also find that multiple-item variables are somewhat better than single-item variables for explaining objective risk.

Suggested Citation

  • Hermansson, Cecilia, 2018. "Can self-assessed financial risk measures explain and predict bank customers’ objective financial risk?," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 226-240.
    • Handle: RePEc:eee:jeborg:v:148:y:2018:i:c:p:226-240
    DOI: 10.1016/j.jebo.2018.02.018
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    Cited by:

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    2. Elena Lagomarsino & Alessandro Spiganti, 2023. "Risk Aversion and the Size of Desired Debt," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(1), pages 369-396, March.
    3. Baeckström, Ylva & Marsh, Ian W. & Silvester, Joanne, 2021. "Variations in investment advice provision: A study of financial advisors of millionaire investors," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 716-735.
    4. D’Hondt, Catherine & De Winne, Rudy & Merli, Maxime, 2021. "Do retail investors bite off more than they can chew? A close look at their return objectives," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 879-902.
    5. Eriksson, Kent & Hermansson, Cecilia & Jonsson, Sara, 2019. "The viability of the bank advisory service business model - effects of customers' trust, satisfaction and loyalty on client-level performance," Working Paper Series 19/4, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    6. Hermansson, Cecilia & Jonsson, Sara, 2021. "The impact of financial literacy and financial interest on risk tolerance," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).

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    More about this item

    Keywords

    Risk preferences; Elicitation methods; Household survey; Household finance;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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