TITLE:
Quantitative Analysis in Securities and Futures Company Customer Value Assessment
AUTHORS:
Jianlei Huang, Ning Ma, Yutong Wang, Jiarui Li, Shuya Liu
KEYWORDS:
Factor Analysis, K-Means Clustering, Regression Tree, Feature Importance
JOURNAL NAME:
Modern Economy,
Vol.13 No.11,
November
28,
2022
ABSTRACT: This paper focuses on the evaluation process of
users in an Investment company in China. We find different types of users
through the process of clustering, and make an evaluation on users by using
regression to give them a score. These two aspects provide a standard for this
company to form different strategies for different customers in order to
benefit both company and users. The meaning of the project is to provide better
service to the old customers that have less trading frequency, and to lower the
risk of the loss of valuable new customers. We perform data cleansing to remove
inactive accounts and outliers and do logarithmic transformation to reduce the
influence of extreme monetary values. Because of the strong correlation between
variables, it is hard to perform algorithms on original data. Thus in order to
reduce the large dimension of data, we perform factor analysis to create three
dimensions that represent users’ information, one relating to monetary, one to
their transaction number, one to their profit. For clustering, we perform
widely used K-means clustering methods. Using the elbow method, customers are
clustered into four groups. The resulting four groups show one group with high
trading frequency; one with large money and profit, one with large money and
loss, and also one majority group with less money and trading deals. We use a
regression tree to perform regression based on the reduced dimensions and their
contribution. The model reaches 97% accuracy showing that monetary aspects of a
user make up the most important to a company. Further discussion uses
classification methods to check our clustering result and performs regression
on some of the variables composing contributions to reveal more details of each
dimension.