Financial Distress Analyzing And Predicting For The Chinese Listed Companies

With the deepening of Chinese economic marketization,the competition among enterprises is becoming fiercer and fiercer.Everyday,there are enterprises which are getting into financial distress or will get into financial distress.Based on the analysis on the financial distress of listed companies,this thesis is exploring the ways to forecast financial distress with the application of statistics model.In this thesis,Chinese listed companies are taken as research objects,paired matching are applied on 36 sampling companies with financial distress and 36 sampling companies without financial distress selected from the A share market with data ranging from 2003 to 2006,collinearity diagnostic testing and backward regression testing are applied to get 7 financial variables, Logistic Regression model and Fisher discriminant model on financial distress forecasting are established respectively based on this 7 variables and financial data of each year.As a result of the forecasting analysis,we found:(1) the financial indexes contain information about financial distress that will take place in listed companies so it can be used in predicting financial distress of listed companies,(2) the variable " profit ratio of net asset X₃" and "profit ratio of main business X₅" have more contribution than the other variables in Logistic Regression model;the variable "main operating margins X₅" and "total assets turnover X₁₀" have more contribution than the other variables in Fisher discriminant model(3) Logistic Regression model is better than Fisher discriminant model in the forecasting;(4) the model testing has time effect,namely,the closer the year of ST,the lower the incorrectness rate of the model discriminability.