Influencing Factors And Evaluation Method Of High-tech Enterprise Credit Risk

It is helpful to improve the objectivity and accuracy of credit risk evaluation that determining the influence of the non-financial factors, such as independent innovation capacity et al, to credit risk of high-tech enterprises, and then introducing those significant factors into the credit evaluation index system of high-tech enterprises; exploring and applying the advanced quantitative analysis method and means to scientifically evaluating credit risk of high-tech enterprises. Our study will have important theory meaning and practice value to dredge and widen the financing channels of high-tech enterprises, enhance the conversion ratio of technological achievement, and promote the sustainable and healthy development of high-tech industry. Therefore, in this thesis, those problems, such as the influence of the non-financial factors, such as independent innovation capacity et al, to credit risk of high-tech enterprises, and the credit risk evaluation method of high-tech enterprises based on classification et al, are explored and investigated.The main research contents are as follows:1. For the sake of extracting the industry (or regional) factor which may have significant influence to credit risk of high-tech enterprises, we attempt to identify the industry (or regional) difference of high-tech enterprises credit risk in China. Firstly, from the perspective of borrower credit rating transfer, following the basic hypothesis of CreditMetrics model and the prospective requirements of risk identification, a system for identifying industry (or regional) difference of high-tech enterprises credit risk based on Markov chain is constructed, where the credit state space and credit rating of high-tech enterprises is obtained through a new credit rating model for enterprises based on Projection Pursuit and optimal partition. Secondly, taking the high-tech listed companies in China as samples, the empirical analysis on industry (or regional) difference of high-tech enterprises credit risk is carried out.Where the modeling approach of credit rating model for high-tech enterprises based on Projection Pursuit and optimal partition as follows: (1) Using Projection Pursuit, the comprehensive credit score of each sample is obtained. After sorting the comprehensive credit score descending, the ordered samples series is generated. (2) A clustering analysis of the ordered samples is carried out with the optimal partition method, so the clustering results are obtained definitely. (3) Each optimal partition point is regarded as the threshold to divide the credit grades, and then the credit rating for enterprises is achieved.2. Independent innovation is the lifeline of survival and development of high-tech enterprises. Therefore, in order to investigate the influence of independent innovation capacity to credit risk of high-tech enterprises, we must scientifically evaluate the independent innovation capacity of high-tech enterprises. Firstly, we propose an improved TOPSIS method based on connection degree. In this method, the ideal point and negative ideal point is regarded as mutual opposition set in a system both having certainty and uncertainty. When inspects the connection degree between the objective project and the ideal point or negative ideal point, the opposition set's existence is considered fully. Using the connection vector distance redefined by us, the relative similarity scale is calculated. So the draw back of the traditional TOPSIS method is overcome to a certain extent. Secondly, through adding time dimension in the improved TOPSIS method based on connection degree, a new dynamic comprehensive evaluation model is constructed. Using this model, the independent innovation capacity of high-tech industries in China is evaluated dynamically.3. Based on Cobb-Douglas production function and net present value method, an analysis on the default behavior of enterprises is carried out. Therefore, the relationship between independent innovation capacity and credit risk of enterprises is preliminarily explained theoretically. On this basis, a Cox model to analyze credit risk of high-tech enterprise is constructed. Let independent innovation capacity, financial factors, growth phrase, enterprise scale, regional factor and industry factor be covariate, through the Cox regression analysis, the effect degree and direction of those factors above on high-tech enterprise credit risk is tested. And then, the influence of independent innovation capacity to the results of credit risk evaluation of high-tech enterprises is investigated.4. In view of qualitative index existing in the credit evaluation index system for high-tech enterprises, we also study the credit evaluation method for high-tech enterprises which can process qualitative index. According to the draw back of traditional MCGC, using the basic idea of TOPSIS method for reference, the comprehensive membership cloud gravity center vector is normalized based on ideal state and negative ideal state. Through the modified weighted deviation degree, the change of membership cloud gravity center is measured scientifically. Thus, an improved MCGC is proposed and applied in credit evaluation of high-tech enterprises. The empirical analysis results show that the improved MCGC can successfully process the mutual conversion of qualitative and quantitative.5. In view of the classification problem of two-types of samples, we propose a credit risk evaluation model for high-tech enterprises based on multi-objective programming and Support Vector Machines (SVM). Based on TOPSIS method, respectively taking the"normal enterprise"sample similarity to ideal point and the"default enterprise"sample similarity to negative ideal point as the goal, the multi-objective programming model is established. Using real coded accelerating genetic algorithm (RAGA), above model is solved, and then the combination weight of index is obtained. Through constructing the weighted sample, the overlap of the credit conditions of two types of samples is reduced. As a result, the predicting accuracy of SVM can be raised to a certain extent.6. In view of the classification problem of multi-types of samples, based on the idea of'non-dimension reduction', we propose a new credit rating model for high-tech enterprises based on Projection Pursuit and K-means clustering algorithm. Firstly, using Projection Pursuit, the comprehensive credit score of each sample is obtained, so as to reflect the structure or characteristics of original multi-dimensional data. Secondly, the distribution density of the comprehensive credit score series is estimated by the kernel density estimation method, and then the initial cluster centers are determined according to the local maximum points of density function. Finally, using K-means clustering algorithm, the final cluster centers are obtained, and then the credit grades are partitioned. Thus, the credit rating for enterprises is realized.