The Comparative Study Of Recognition Methods Of Credit Risk Of Chinese Listed Companies

At present, the stable development of the listed companies plays an extremely important role in promoting economic growth and economic structure optimization in china. Therefore, in order to maintain normal economic order and guard against risk effectively, we need to assess the credit risk of listed companies accurately. In our country, the credit risk of commercial Banks accumulate over a long period, and it is a huge hidden trouble for normal operation of the economy in our country, the effects of the listed companies credit risk are greater than general enterprises. It is very important to analyze the listed companies credit risk for preventing and dealing with the credit risk. Risk identification is the starting point for risk management, and credit risk management is of great significance for commercial Banks. At this point, in 2008, the subprime mortgage crisis is a wake-up call for us. Therefore, it is obligate to select proper credit-risk recognition method, for effectively restraining the increasing bad-loan.Credit-risk is a possibility for bank to get losing, because the firm can not fulfill the credit-contract on time. The credit-risk recognition is using the appropriate method of qualitative and quantity research to distinguish the firm, dividing them into high credit risk firm and low credit risk firm; after loaning, using the same, method to track the changing of the firm’s credit-risk. Comparing with the actual credit-risk recognition method of state-owned commercial banks, aboard already gets some advanced method, including multiple discriminant analysis, logistic model, BPNN model and KMV. BPNN model has bug. KMV lack the exterior-condition. I choose the multiple discriminant analysis and logistic model to research the credit-risk recognition fitting for china.This paper is divided into five parts. The first part introduces the background, meaning and methods of this research. The second part introduces the research status of credit risk identification at abroad and China, and also analyzes the difference of credit risk identification methods. The third part introduces the definition and characteristics of credit risk. Besides, in this part we also introduce and analyze the sample data. In the fourth part, the empirical analysis is used in multivariate discriminant model and Logistic model and then the results are analyzed. The fifth part is conclusion and recommendations of this study. After conducting empirical analysis we find:non-financial indicators are not pass significant test and could not be put into the model; and logistic model has more self-validate-veracity, forecast ability and stability than multiple discriminant analysis. Therefore, logistic model is the best model for nowadays china. Besides, we also give some pieces of policy advice for commercial banks to use the proper credit-risk recognition method. Mainly include the following five:establish information sharing mechanism, establish a suitable model for their own, combined with a variety of models, quantitative analysis combined with qualitative analysis, and study the international advanced concepts and methods.