Logistic回归模型在信用风险分析中的应用

通过运行SPSS,建立L og istic回归信用评价模型(cred it eva luation m odel),用来对中国2000年106家上市公司进行两类模式分类,这两类模式是指按照公司的经营状况分为“差”和“正常”两个小组.对每一家上市公司,考虑其经营状况的4个主要财务指标:每股收益、每股净资产、净资产收益率和每股现金流量.仿真结果表明,L og istic回归信用评价模型对总体106个样本,判别准确率达到99.06%.此外,本文的研究结果还发现,当利用SPSS的D iscrim inan t给出的模型系数建立的线性判别分析模型和利用SPSS的M u ltinom ia lL og istic给出的模型参数建立的L og istic回归模型,L og istic回归模型的判别结果不如线性判别模型.但如果剔除不合格的样本,或是将样本数据规格化,则可以提高L og istic回归模型的分类准确率.

An Application of Logistic Regression Model in Credit Risk Analysis

The paper establishes a Logistic regression credit evaluation model by running SPSS software.It is used to classify the 106 listed companies of China iin 2000 into two patterns.The two patterns mean that the listed companies are divided into two groups according to their business conditions: one is a ″bad″ group and the other is a ″normal″ group.To each listed company,the 4 main financial indexes are considered: earning per share,net asset per share,return on equity,cash flow per share.The simulating resutls showed that,to the 106 samples,the discriminant accuracy rate is 99.06% by using the Logistic regression credit evaluation model.In addition,the research still found that,when we use the model coefficients given by Discriminan in SPSS to establish the model of linear discriminant analysis and use the model parameters given by Multinomial Logistic in SPSS to establish the model of Logistic regression model,the discriminant results of the Logistic regression model is not as good as that of the linear discriminant analysis.