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

Thompson, John R. J.; Feng, Longlong; Reesor, R. Mark; Grace, Chuck

doi:10.2139/ssrn.3595433

Cited by 1 publication

(1 citation statement)

References 10 publications

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“…The dataset in this paper consists of daily KYC information (age, gender, annual income, risk tolerance) and financial information (account type, total value of that account, proportion of total value invested into different securities) for

50,980

accounts of

27,644

clients and 311 advisors. The time period covered is March 29, 2019 to August 12, 2019 and the dataset is drawn from the same database provided by a private investment dealership discussed in Thompson et al (2021). KYC information is collected via a questionnaire when the account is opened and is updated at least yearly, and the financial information is updated each business day to reflect transactions and market value fluctuations.…”

Section: Datamentioning

confidence: 99%

Measuring the gap between elicited and revealed risk for investors: An empirical study

et al. 2022

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Financial 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.

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