Study On Credit Risk Management Of Small And Medium-sized Enterprises In Chinese Commercial Banks

Small and Medium-sized Enterprise (SMEs) financing is a challenge besetting the world, particularly in China. It is seriously unbalanced between the supports to SMEs in social funding and the contributions of SMEs to society. There are many reasons accounting for this situation. A significant one is that under the influence of information asymmetry, the credit officers in Chinese commercial banks are unable to grasp the law of loan default, thereby they always prefer to lend to large enterprises rather than SMEs, or they unilaterally emphasize the role of collaterals, which result in an increasing threshold for SME financing. As a result, it is necessary to do some researches on the rules of credit risk management to improve our levels of credit risk management.This article discusses credit risk of SMEs from three different perspectives:the timing of granting credit and post-lending as well as portfolio management of SMEs. Research findings indicated that different information should be emphasized in different scenarios.At first, through adopting factor analysis, Fuzzy analysis, AHP analysis, Fuzzy-neural adaptive analysis, the article build a Fuzzy-neural Adaptive model to deal evaluate credit risk of SMEs. There are many researches focusing on credit risk evaluation, but many of them haven't taken the inaccuracy of financial information and the vagueness of non-financial information into considerations. In order to overcome the shortage of the present research, the article collects 65 samples of SMEs applying loans from banks. By inducing factor analysis and fuzzy analysis as pretreatment process to reduce the complexity of data, the results are inputted to Fuzzy-neural Adaptive network model. Thereby, we can get the final classifying result. By comparing the output of the model and the other models, such as BP, XU_G, Logistic, we can conclude that Fuzzy-neural Adaptive Network model can get the most accurate result.Secondly, this article constructs a post-lending credit risk warning model for SMEs based on Logistic regression method by employing some tools, such as description analysis, factor analysis, Logistic regression. There are not unique researches focusing on post-lending credit risk warning model. The similar topics are enterprise bankrupting prediction, enterprise failure and so on. Generally speaking, these models are often based on accounting variables, and take the financial ratios as explanation variables. Differed from the present researches, this article successfully combines financial and non-financial factors. The result shows that:(1) warning model has a better performance than random model; (2) financial variables have the warning ability before risk happens; (3) non-financial information can really improve warning performance.At last, this article construct economic capital allocation model for loan portfolio of SMEs by taking Copula function to optimize VaR method. An important assumption in calculating VAR through traditional method is the general normal distribution assumption of loan portfolio, but numerous theoretical and empirical studies have shown that this assumption is not realistic. Empirical studies showed that income distribution of loan portfolio has distinct "heavy-tailed" feature, and the normal distribution assumptions may underestimate the risk values. Thereby, Copula function was introduced to deal with this problem. Copula functions were used to improve the calculation of probability of joint distributions of Credit Metrics model. The Monte Carlo method was employed to simulate the model and compare the result of VAR based on traditional distribution assumption and the t-Copula methods. The result indicated that the t distribution assumption based on Copula method can simulate the heavy-tailed distributions, and close to the realities of credit risk, as a conclusion, the traditional VAR method underestimated the value of credit risk.