摘要
针对上市公司信用风险评估问题进行研究.选取的50家上市公司2020年的财务数据,首先应用因子分析法筛选出能有效反映上市公司财务状况的指标,构建了信用风险评估指标体系,然后建立了基于密度和权重改进的K-means聚类模型对上市公司的信用风险进行评估,按照信用风险等级将上市公司分为三类:高风险、中风险和低风险,再将改进后的K-means聚类模型和传统K-means聚类模型进行比较分析.研究结果显示:改进的K-means聚类信用风险评估模型提高了估计精度,对上市公司的信用风险评估更合理,其中高风险有25家、中风险24家、低风险1家.
It is necessary to made a research on the credit risk assessment of listed companies. Based on the financial data of 50 selected listed companies in 2020, the factor analysis method is used to screen out the indicators that can effectively reflect the financial status of listed companies, and the credit risk assessment index system is thus constructed. And then an improved density-and weight-based K-means clustering model is established for the credit risk assessment of listed companies. The listed companies are classified into three categories according to their credit risk level: high risk, medium risk and low risk ones so that the improved K-means clustering model and traditional K-means clustering model are subjected to a comparative analysis. It is found that the improved K-means clustering credit risk assessment model enhances the estimation accuracy and makes the credit risk assessment of listed companies more reasonable. Among the 50 listed companies, there are 25 high-risk companies, 24 medium-risk companies and 1 low-risk company.
作者
赵衡
彭铃
李云飞
ZHAO Heng;PENG Ling;LI Yunfei(College of Mathematics and Information,China West Normal University,Nanchong,Sichuan 637009,China)
出处
《内江师范学院学报》
CAS
2022年第12期77-83,共7页
Journal of Neijiang Normal University
基金
西华师范大学英才科研基金项目(17YC381)。
