SME Credit Risk Measurement Basing On The Perspective Of Guarantee Institutions

The development of SMEs in the national economy plays an increasingly important role, particularly in the expansion of employment, enhancing vitality of the market in a force to be reckoned with. However, SMEs contribute to economic development while they suffered discrimination in the financing issue, the SME financing difficulties have been a hot topic, how to solve it is a worldwide problem. From the present international experience and China’s practice of view, the introduction of the larger security institutions can ease the difficult situation of SME finance, the risk transferred to the security agencies, banks increased lending initiative. But this also raises a question, as a guarantee agencies, how to identify the credit risk of SMEs to achieve their own sustainable development, so its win-win situation with SMEs?This paper studies the idea is to use the guarantee from his or her internship has been collected by the data, standing on the perspective of the security agencies, using logistic regression model estimates the probability of default of SMEs to identify the credit risk of SMEs to help guarantee institutions the correct decision.This paper is divided into six chapters, text structure and main contents are as follows:The first chapter of this paper is the main account of the background and motivation for writing, thesis structure, characteristics and innovation points, as a prelude.The second chapter describes the definition of security, function and analyzes the development of SME credit guarantee institutions present situation and existing problems and the risks and characteristics of the face, which leads to the subject of this article-Perspective on SME credit guarantee institutions risk measure.The third chapter reviews our predecessors on the theory of credit risk measurement literature, by method of time the birth order of the Description and comparison. Credit risk measurement methods become increasingly mature, taking into account more and more widely, but not suitable for direct copy applied to the body of SMEs in China.Chapter IV of this article detailed empirical research methods used and the index system. From the practical point of view of security, when combined with financial indicators and non-financial indicators determined on the basis of the application of logistic regression to build models. In the analysis of financial indicators, in order to avoid multi-collinearity effects logistic regression results, the first factor analysis of financial indicators.Chapter V with a security company has internship empirical data obtained and analyzed the results of the model fit better the degree, get a higher prediction probability, an explanation of the method has practical application value.Chapter VI is the full text of the summary, combined with my personal guarantee company internship experience, the company’s risk prevention on the guarantees and guarantee the healthy development of the industry to make some specific recommendations.The principal innovation of this article is to analyze the perspective of the first novel that the starting point to solve the financing problem of SMEs is discussed in the security sector rather than by their predecessors more small and medium enterprises and the banks themselves, try from the perspective of the security agencies to measure the credit risk of SMEs, a unique point of view; followed in the use of logistic regression modeling, not only take into account financial indicators, but also with the characteristics of small and medium enterprises to consider non-financial indicators, a more fitting security practices, in line with the characteristics of small and medium enterprises to improve the final prediction accuracy. This deficiency is that, because of the small and medium enterprises is very difficult for the actual data collection, empirical analysis, the number of samples too few, to a certain extent affected the model results; Moreover, in addition, quantification of non-financial indicators are also subject to further study and improved.