了解客户行为:金融交易的聚类分析

John R.J. Thompson, Longlong Feng, R. Reesor, Chuck Grace
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引用次数: 9

摘要

在加拿大,负责直接监管投资交易商和共同基金交易商的省级证券委员会和自律组织要求财务顾问和交易商分别收集和维护了解客户(KYC)信息,例如投资者账户的年龄或风险承受能力。有了这些信息,投资者在他们的顾问的指导下,对他们的投资做出有利于他们的投资目标的决定。我们独特的数据集由一家金融投资交易商提供,该交易商拥有超过50,000个账户,超过23,000个客户,涵盖时间为2019年1月1日至8月12日。我们使用改进的行为金融学近因、频率、货币模型来量化投资者行为的工程特征,并使用无监督机器学习聚类算法来找到行为相似的投资者群体。我们表明,KYC信息(如性别、居住地和婚姻状况)并不能解释客户行为,而交易、交易频率和交易量的八个变量是最具信息量的。因此,我们的研究结果应该鼓励金融监管机构和顾问使用更先进的指标来更好地理解和预测投资者的行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Know Your Clients' Behaviours: A Cluster Analysis of Financial Transactions
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.
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