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Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions

Author

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  • John R. J. Thompson

    (Department of Mathematics, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada)

  • Longlong Feng

    (Department of Mathematics, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada)

  • R. Mark Reesor

    (Department of Mathematics, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada)

  • Chuck Grace

    (Department of Finance, Ivey Business School, London, ON N6G 0N1, Canada)

Abstract

In Canada, financial advisors and dealers are required by provincial securities commissions and self-regulatory organizations—charged with direct regulation over investment dealers and mutual fund dealers—to respectively collect and maintain know your client (KYC) information, such as their age or risk tolerance, for investor accounts. With this information, investors, under their advisor’s guidance, make decisions on their investments that are presumed to be beneficial to their investment goals. Our unique dataset is provided by a financial investment dealer with over 50,000 accounts for over 23,000 clients covering the period from January 1st to August 12th 2019. We use a modified behavioral finance recency, frequency, monetary model for engineering features that quantify investor behaviours, and unsupervised machine learning clustering algorithms to find groups of investors that behave similarly. We show that the KYC information—such as gender, residence region, and marital status—does not explain client behaviours, whereas eight variables for trade and transaction frequency and volume are most informative. Hence, our results should encourage financial regulators and advisors to use more advanced metrics to better understand and predict investor behaviours.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:2:p:50-:d:486652
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    Cited by:

    1. Cynthia Pagliaro & Dhagash Mehta & Han-Tai Shiao & Shaofei Wang & Luwei Xiong, 2021. "Investor Behavior Modeling by Analyzing Financial Advisor Notes: A Machine Learning Perspective," Papers 2107.05592, arXiv.org.
    2. Emilia Herman & Kinga-Emese Zsido, 2023. "The Financial Sustainability of Retail Food SMEs Based on Financial Equilibrium and Financial Performance," Mathematics, MDPI, vol. 11(15), pages 1-26, August.
    3. 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.
    4. Jerinsh Jeyapaulraj & Dhruv Desai & Peter Chu & Dhagash Mehta & Stefano Pasquali & Philip Sommer, 2022. "Supervised similarity learning for corporate bonds using Random Forest proximities," Papers 2207.04368, arXiv.org, revised Oct 2022.

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