基于高维特征因子的券商客户流失预警模型研究  

Customer Churn Model Based on High-Dimensional Factors——A Study on Securities Companies

作　　者：高天辰 曲浩 王菲菲[3] 周静[3] Tianchen Gao;Hao Qu;Feifei Wang;Jing Zhou(School of Economics,Xiamen University;Founder Securities Co.,Ltd.;Center for Applied Statistics,School of Statistics,Renmin University of China)

机构地区：[1]厦门大学经济学院 [2]方正证券股份有限公司 [3]中国人民大学应用统计科学研究中心,统计学院

出　　处：《经济管理学刊》2023年第4期143-168,共26页Quarterly Journal of Economics and Management

基　　金：中国人民大学科学研究基金(中央高校基本科研业务费专项资金资助)项目(21XNA027)的研究成果。

摘　　要：随着证券行业的竞争日趋激烈,如何留住现有客户并预防潜在的客户流失已成为该行业管理者普遍关心的重要问题之一。本文针对证券行业客户流失进行研究。首先,结合证券行业的实际业务背景,探索了证券行业客户流失的定义;其次,提出了基于高维特征因子的独立性筛选方法;最后,基于筛选的因子,分别构建了客户流失预警日模型和周模型。研究结果表明,资产类因子和非资产类因子对预测客户流失具有显著效果,复合因子的预测效果不显著。客户流失预警日模型的外样本AUC值平均可以达到0.95以上,说明模型具有良好的预测精度。客户流失预警周模型的预测效果与日模型基本一致,并且具有计算成本低、预测效率高、模型更加稳定的特点。本文的研究结果可以为企业进行客户挽回提供策略分析,划分客户群体,针对不同流失风险的客户制定不同的挽回策略。另外,本文提出的流失预警模型在企业实际环境测试中也具有良好表现。The securities industry is widely recognized as one of the most data-intensive sectors,characterized by diverse business scenarios.However,due to stringent regulations and high entry barriers,the growth of new securities companies is sluggish.Nevertheless,competition among the industry is intensifying.In order to expand their market share,securities companies have employed various strategies to attract new customers.They have invested considerable effort in providing personalized marketing services to enhance their customer relationship management capabilities.However,a top priority for securities companies in their customer relationship management efforts is retaining existing customers and preventing potential customer churn.This research focuses on customer churn in the securities sector.The data used in this study consists of user-level transaction data provided by a prominent domestic securities company.Initially,we conduct data inspection and categorize the raw data into asset variables and non-asset variables.After cleaning the original data and filtering out relevant variables for later analysis,we define customer churn based on the stability of the churn status,drawing from practical experience within the securities industry.We then investigate how the number of trading days,logins,and assets impact the state of customer churn,ultimately arriving at a viable and effective definition of customer churn.Having established the response variable,the subsequent crucial task is to identify meaningful impact factors from hundreds of raw variables.To address this,we propose an independent screening method based on high-dimensional features.We first divide the churn factors into asset variables and non-asset variables,each represented by 8 and 4 aspects,respectively.From these aspects,we derive a series of indicators.We also calculate the ratios or products of asset and non-asset factors to obtain 10 composite churn factors.Subsequently,we employ the univariate AUC method,considering data quality and actual busine

关 键 词：客户流失 证券行业 高维特征因子 独立性筛选 逻辑回归 

分 类 号：F490.6[经济管理—产业经济]