Chuck teaches courses on institutional investing, personal wealth management and Fintech. He also serves as the Program Director for Western’s Financial Wellness Lab and Program Director for the Ivey Academy’s Investment Professional Leadership Program. He has a particular interest in the area of financial advice and the impact of emerging technologies. He was the recipient of Ivey’s David G Burgoyne Teaching Award in 2019.
Previously, Chuck has served as HBA faculty director for the Ivey Field Project (IFP), Associate Director for The Scotiabank Digital Banking Lab and as a research engineer at the Centre for Quantitative Analysis and Modeling (CQAM). He also chaired Ivey's advisory council for Household Finance research.
Chuck stays grounded in the realities of Canada’s wealth management industry as a strategic consultant to firms attempting to thrive in this highly competitive arena. In he was awarded the 2021 Patrick Culhane Special Contributor Award in recognition of an individual who has made a significant contribution to the payroll profession.
Prior to pursuing his passions for teaching and consulting, Chuck held a progression of senior management positions with one of Canada’s largest insurance and wealth management companies. As the COO of Quadrus Investment Services, Chuck and his team were responsible for growing the firm from its inception into one of the largest mutual fund dealers in Canada.
Chuck regularly presents at industry events, writes for industry publications and has authored over 50 case studies for Ivey Publishing. When not working, you will probably find him with his family and friends … or in a swimming pool.
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Thompson, J. R. J.; Feng, L.; Reesor, R. M.; Grace, C.; Metzler, A., 2022, "Measuring the gap between elicited and revealed risk for investors: An empirical study", FINANCIAL PLANNING REVIEW, December 5(4)
Abstract: AbstractFinancial advisors use questionnaires and discussions with clients to determine investment goals, elicit risk preference and tolerance and establish a suitable portfolio allocation for different risk categories. Financial institutions assign risk ratings to their financial products. Advisors use these ratings to categorize products into the same risk categories used for portfolio allocation. Subsequently, clients select a portfolio of assets whose risk profile we call revealed risk. This paper proposes a novel methodology for comparing an individual's elicited and revealed risk. We propose using Value‐at‐Risk to measure elicited and revealed risk and the discrepancy between them, showing whether clients are over‐risked or under‐risked. We demonstrate the methodology using a dataset from a Canadian private financial dealer. We find that elicited risk is consistently higher than revealed risk–advisors build a safety buffer into their recommendations–and elicited risk varies with respect to demographic features and trading behaviors in expected ways–investors are receiving sound advice. This risk discrepancy could be used, for example, to gauge the quality of financial advice an individual is receiving, or it could be used to help advisors communicate inconsistencies between client trading actions and client goals. Our methodology falls into the interest realms of advisors, regulators, and dealerships.Link(s) to publication:
http://dx.doi.org/10.1002/cfp2.1151
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Metzler, A.; Zhou, Y.; Grace, C., 2021, "Learning about financial health in Canada", Quantitative Finance and Economics, June 5(3): 542 - 570.
Abstract: This paper applies cluster analysis to eleven (continuous) years' worth of responses to the Canadian Payroll Association (CPA) survey of employed Canadians. The clustering algorithm clearly identifies three distinct groups of respondents. Between-group comparison of response patterns reveals that two of the groups lie on opposite sides of the financial health spectrum, and leads us to label their members "financially stressed" and "financially capable", respectively. The third group shares characteristics with both the stressed and capable groups, and we label its members as "financially coping". We find that financial stress is both widespread (one third of all respondents are identified as stressed) and complex (stress is only weakly related to simple demographics such as age or income). From a methodological perspective, an important point is that our use of cluster analysis allows us to generate rigorous insights into financial well-being, without having to measure it directly.
Link(s) to publication:
http://dx.doi.org/10.3934/QFE.2021024
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Thompson, J. R. J.; Feng, L.; Reesor, R. M.; Grace, C., 2021, "Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions", Journal of Risk and Financial Management, January 14(2): 50 - 50.
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.
Link(s) to publication:
http://dx.doi.org/10.3390/jrfm14020050
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