Research On Credit Risk Of Small And Medium - Sized Enterprises Based On Logistic Model

In recent years, SMEs have become the main point of promoting our country’s economy and the growth of our economy. In promoting sustained economic growth, promoting technological innovation, increasing national revenue and employment, SMEs play an irreplaceable role. However, it is difficult to control credit risk with little capital of SMEs、an incomplete financial system、difficulties to quantify the intangible assets、lacking of effective collateral assets in our country. In addition, the financial business of commercial banks mainly aim at the large-scale enterprises at the present stage, which led to the credit financing difficulties of SMEs. To the development of SMEs, we must first solve the problem of credit financing of SMEs in our country. Also, because the credit risk is the main risk commercial banks faced. Solving the problem of credit financing for SMEs becomes a problem to solve SMEs credit risk. Based on this, it is necessary to carry out research about the SMEs credit risk, establishing appropriate credit risk evaluation system for SMEs.The paper reviewed credit risk research literature in international and domestic academics. From the causes and economic mechanisms of credit risk, combined with the "credit rationing" model of commercial banks, the status of credit financing difficulties for SMEs, the underdeveloped capital market environment, the paper compare credit risk rating methodology and credit risk rating models in international and domestic academics. With the advantages and disadvantages of each model, I find that Logistic regression model is a suitable method for evaluating credit risk and building related credit risk rating models. To further illustrate the scientific and applicability of Logistic regression model, the paper selected 51 listed enterprises form the financial and non-financial indicators sample data on SMEs, combined index selection principles and characteristics of domestic SMEs, selecting some quantitative indicators from financial and non-financial indicators to establish a relatively complete SMEs credit risk assessment system. Then, I take factor analysis method and Logistic regression model to analysis empirically SMEs credit risk assessment system in this article. Through the analysis of the results, Logistic regression model which is built in SMEs credit risk rating models in this paper are more suitable for our measuring about SMEs, and the model is also more satisfactory.Based on the above research, the paper focus on the issue of the SMEs credit risk evaluation. It divided into five parts, SMEs debt factor, profit factor, turnover factor and SMEs credit risk is inversely proportional derived by empirical analysis; the model overall predictive analysis associated with the selection of default demarcation point by comparing the results of the model. It adopt logistic regression models to analysis probit regression about SMEs credit risk, using the weighted average method, which is different from the traditional probability calculation method in the past and the innovation of this paper. In addition, the paper also proposes combine the P value and the specific risk premium as one loan rate pricing standards.