Reconciling Self-Assessed with Psychometric Risk Tolerance: A New Framework for Profiling Risk among Investors.
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| Title: | Reconciling Self-Assessed with Psychometric Risk Tolerance: A New Framework for Profiling Risk among Investors. |
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| Authors: | Mazzoli, Camilla (AUTHOR), Palmucci, Fabrizio (AUTHOR) |
| Source: | Journal of Behavioral Finance. Apr-Jun2025, Vol. 26 Issue 2, p201-214. 14p. |
| Subjects: | Investors, Financial risk, Financial planners, Portfolio management (Investments), Investment risk, Individual investors, Investment advisors |
| Abstract: | Financial advisors need to assess their clients' risk profile to properly manage their portfolio risk and comply with regulatory provisions. Assessing an investor's financial risk tolerance (FRT) is a challenge in the advisory process and none of the existing measures can be easily employed on a large scale. Previous literature has revealed a gap between self-assessed and psychometrically assessed measures of FRT (PA_FRT) but has not yet offered a solution to fill this gap. Thus, we propose a model that consistently estimates the PA_FRT by leveraging retail investors' self-assessment and other information typically submitted in standard bank questionnaires. Our model represents a promising tool for financial advisors looking to improve their customers' risk profiling. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Financial advisors need to assess their clients' risk profile to properly manage their portfolio risk and comply with regulatory provisions. Assessing an investor's financial risk tolerance (FRT) is a challenge in the advisory process and none of the existing measures can be easily employed on a large scale. Previous literature has revealed a gap between self-assessed and psychometrically assessed measures of FRT (PA_FRT) but has not yet offered a solution to fill this gap. Thus, we propose a model that consistently estimates the PA_FRT by leveraging retail investors' self-assessment and other information typically submitted in standard bank questionnaires. Our model represents a promising tool for financial advisors looking to improve their customers' risk profiling. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 15427560 |
| DOI: | 10.1080/15427560.2023.2271108 |
