Author(s)

Jianlei Huang¹, Ning Ma², Yutong Wang³, Jiarui Li⁴, Shuya Liu⁵

¹Fudan University, Shanghai, China.
²University of California San Diego, La Jolla, USA.
³University of Illinois Urbana-Champaign, Champaign, USA.
⁴Rose-Hulman Institute of Technology, Terre Haute, USA.
⁵University of Southern California, Los Angeles, USA.

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.

Factor Analysis, K-Means Clustering, Regression Tree, Feature Importance

Huang, J. , Ma, N. , Wang, Y. , Li, J. and Liu, S. (2022) Quantitative Analysis in Securities and Futures Company Customer Value Assessment. Modern Economy, 13, 1471-1487. doi: 10.4236/me.2022.1311079.