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Measuring Financial Advice: aligning client elicited and revealed risk

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  • John R. J. Thompson
  • Longlong Feng
  • R. Mark Reesor
  • Chuck Grace
  • Adam Metzler

Abstract

Financial advisors use questionnaires and discussions with clients to determine a suitable portfolio of assets that will allow clients to reach their investment objectives. Financial institutions assign risk ratings to each security they offer, and those ratings are used to guide clients and advisors to choose an investment portfolio risk that suits their stated risk tolerance. This paper compares client Know Your Client (KYC) profile risk allocations to their investment portfolio risk selections using a value-at-risk discrepancy methodology. Value-at-risk is used to measure elicited and revealed risk to show whether clients are over-risked or under-risked, changes in KYC risk lead to changes in portfolio configuration, and cash flow affects a client's portfolio risk. We demonstrate the effectiveness of value-at-risk at measuring clients' elicited and revealed risk on a dataset provided by a private Canadian financial dealership of over $50,000$ accounts for over $27,000$ clients and $300$ advisors. By measuring both elicited and revealed risk using the same measure, we can determine how well a client's portfolio aligns with their stated goals. We believe that using value-at-risk to measure client risk provides valuable insight to advisors to ensure that their practice is KYC compliant, to better tailor their client portfolios to stated goals, communicate advice to clients to either align their portfolios to stated goals or refresh their goals, and to monitor changes to the clients' risk positions across their practice.

Suggested Citation

  • John R. J. Thompson & Longlong Feng & R. Mark Reesor & Chuck Grace & Adam Metzler, 2021. "Measuring Financial Advice: aligning client elicited and revealed risk," Papers 2105.11892, arXiv.org.
    • Handle: RePEc:arx:papers:2105.11892
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    References listed on IDEAS

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    1. John R. J. Thompson & Longlong Feng & R. Mark Reesor & Chuck Grace, 2021. "Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions," JRFM, MDPI, vol. 14(2), pages 1-29, January.
    2. Stephen Diacon & Christine Ennew, 2001. "Consumer Perceptions of Financial Risk," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 26(3), pages 389-409, July.
    3. Sharma, Sankalp & Schoengold, Karina, 2016. "A Comparison of Stated and Revealed Risk Preferences using Safety-First," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236126, Agricultural and Applied Economics Association.
    4. Ganzach, Yoav, 2000. "Judging Risk and Return of Financial Assets," Organizational Behavior and Human Decision Processes, Elsevier, vol. 83(2), pages 353-370, November.
    5. Keith Kuester & Stefan Mittnik & Marc S. Paolella, 2006. "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 53-89.
    6. Philip Dybvig & Heraklis Polemarchakis, 1981. "Recovering Cardinal Utility," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 48(1), pages 159-166.
    7. Michael Guillemette & Chris Browning & Patrick Payne, 2015. "Don't like the picture? Change the frame: the impact of cognitive ability and framing on risky choice," Applied Economics Letters, Taylor & Francis Journals, vol. 22(18), pages 1515-1518, December.
    8. Dybvig, Philip H & Rogers, L C G, 1997. "Recovery of Preferences from Observed Wealth in a Single Realization," The Review of Financial Studies, Society for Financial Studies, vol. 10(1), pages 151-174.
    9. Stephen Foerster & Juhani T. Linnainmaa & Brian T. Melzer & Alessandro Previtero, 2017. "Retail Financial Advice: Does One Size Fit All?," Journal of Finance, American Finance Association, vol. 72(4), pages 1441-1482, August.
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