The Empirical Research On Financial Distress Prediction Of The Listed Companies In China

With the sign of whether the corporation is "special treated" or not, the thesis uses the financial datas of China's listed companies in recent years and selects the initial samples which are composed of 100 enterprises with 50 firms in each of the two groups. In virtue of SPSS, firstly, the text discusses the data characterictics and the differences of predictor variables between the two groups for up to three years prior to the failure event. Then the study uses Factor Analysis to reduce datas, and establishes Linear Multiple Discrimant Analysis Model and Logistic Regression Model. Lastly, the research tests 40 firms of the holdout samples by Logistic Regression Model.The results are as the following: firstly, financial ratios aren't identical to the normal distribution, so it's not suitable to conduct the early-warning model by Linear Multiple Discrimant Analysis. Secondly, the accounting datas are not imprecise, but they show some information. Thirdly, for the same datas, the model by Logistic Regression is more precise in predicing financial distress. Lastly, the model by Logistic Regression can yield an overall correct classification accuracy of 95.87% one year prior to failure and of 84% two year prior to failure to the initial samples. The model is validated by using the holdout samples, the overall correct classification of the model for the first year to two year prior to distress is 95% and 82.5%.