英文标题：
《Customer Selection Model with Grouping and Hierarchical Ranking Analysis》
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作者：
Bowen Cai
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最新提交年份：
2017
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英文摘要：
The purpose of this study was to build a customer selection model based on 20 dimensions, including customer codes, total contribution, assets, deposit, profit, profit rate, trading volume, trading amount, turnover rate, order amount, withdraw amount, withdraw rate, process fee, process fee submitted, process fee retained, net process fee retained, interest revenue, interest return, exchange house return I and exchange house return II to group and rank customers. The traditional way to group customers in securities or futures companies is simply based on their assets. However, grouping customers with respect to only one dimension cannot give us a full picture about customers\' attributions. It is hard to group customers\' with similar attributions or values into one group if we just consider assets as the only grouping criterion. Nowadays, securities or futures companies usually group customers based on managers\' experience with lack of quantitative analysis, which is not effective. Therefore, we use kmeans unsupervised learning methods to group customers with respect to significant dimensions so as to cluster customers with similar attributions together. Grouping is our first step. It is the horizontal analysis in customer study. The next step is customer ranking. It is the longitudinal analysis. It ranks customers by assigning each customer with a certain score given by our weighted customer value calculation formula. Therefore, by grouping and ranking customers, we can differentiate our customers and rank them based on values instead of blindly reaching everyone.
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中文摘要：
本研究旨在建立一个基于20个维度的客户选择模型，包括客户代码、总贡献、资产、存款、利润、利润率、交易量、交易金额、周转率、订单金额、提款金额、提款率、手续费、提交的手续费、留存的手续费、留存的净手续费、利息收入、利息回报、，将exchange house return I和exchange house return II分配给集团和排名客户。证券或期货公司对客户进行分组的传统方式只是基于他们的资产。然而，仅从一个维度对客户进行分组并不能全面了解客户的属性。如果我们仅仅将资产作为唯一的分组标准，那么很难将具有类似属性或价值的客户分组到一个组中。目前，证券或期货公司通常根据管理者的经验对客户进行分组，缺乏定量分析，效果不佳。因此，我们使用kmeans无监督学习方法根据重要维度对客户进行分组，以便将具有相似属性的客户聚在一起。分组是我们的第一步。这是客户研究中的横向分析。下一步是客户排名。这是纵向分析。它通过为每个客户分配一个由我们的加权客户价值计算公式给出的特定分数来对客户进行排名。因此，通过对客户进行分组和排名，我们可以区分我们的客户，并根据价值观对他们进行排名，而不是盲目地影响每个人。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：General Finance 一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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