我国中小企业信用风险度量研究
发布时间：2018-09-09 20:44
【摘要】：中小企业的蓬勃发展具有较强的经济外部性,不仅是推动国民经济持续稳定高速增长的重要力量,而且在调整经济结构、扩大社会就业等方面都发挥着积极的作用。然而,我国中小企业融资一直面临着“麦克米兰缺口”。不管是我国中小企业的融资趋势还是国外中小企业的融资结构都表明:在现实环境的约束下,解决中小企业融资困境只有一个方向——扩大间接融资体系,增加来自银行的债务融资,而加强对中小企业的信用风险评估是其中的关键环节。随着优秀大企业客户资源的逐渐减少和信贷市场细分,针对中小企业的信用风险度量必然成为我国银行未来需要重点关注的一大课题。 本文围绕如何建立适合我国国情的中小企业信用风险评估模型这一主题,针对中小企业这一特殊的企业群体,在对信用风险度量的相关方法、模型回顾和比较的基础上,首先指出了现阶段我国中小企业信用风险度量最可行的方法是多元统计分析。然后,依据113家中小企业样本2002年和2003年的财务数据和非财务数据,对20个最初输入指标进行独立样本T检验和主成分分析,得到一个仅含10个指标的简化指标体系。接着,输入指标数据详细实证检验了多元线性判别模型和Logit模型,得到的结论显示:多元线性判别模型倾向于将简化后的所有10个指标全部进入判别方程,综合预测正确率在违约前2年达到76.1%,违约前1年达到81.4%;Logit模型倾向于选择使用“Backword:Conditional”方法,当Logit分析进行到第4步时的模型,综合预测正确率在违约前2年达到78.8%,违约前1年达到85.8%;Logit模型的综合预测率虽然高于多元线性判别模型,但其在关键的第二类误判率上却不如后者,容易将违约类企业判断为正常企业,忽视其潜在的信用风险。最后,在模型结果的基础上,分析了我国中小企业的信用风险特征,同时与穆迪公司用RiskCalc~(TM)违约模型对美国、澳大利亚等十个较发达国家私营企业信用风险研究的结果相比较,表明:除去非财务因素外,显著性较高且较为稳定,能体现我国中小企信用风险特征的前两个因素依次为资本结构指标、收益性指标,与十国的平均结果基本类似。
[Abstract]:The vigorous development of small and medium-sized enterprises has a strong economic externality, which is not only an important force to promote the sustained, stable and high speed growth of the national economy, but also plays an active role in adjusting the economic structure and expanding social employment. However, the financing of small and medium enterprises in China has been facing the "Macmillan gap." Both the financing trend of small and medium-sized enterprises in China and the financing structure of foreign small and medium-sized enterprises show that under the constraints of the real environment, there is only one direction to solve the financing dilemma of small and medium-sized enterprises-to expand the indirect financing system. Increasing debt financing from banks and strengthening credit risk assessment of small and medium-sized enterprises is the key link. With the decreasing of customer resources and the subdivision of credit market, the measurement of credit risk for small and medium-sized enterprises will inevitably become a major issue that Chinese banks should pay more attention to in the future. This paper focuses on the topic of how to establish a credit risk assessment model suitable for China's national conditions, aiming at SMEs as a special enterprise group, on the basis of reviewing and comparing the relevant methods and models of credit risk measurement. Firstly, it points out that multivariate statistical analysis is the most feasible method to measure the credit risk of SMEs in China at present. Then, based on the financial data and non-financial data of 113 small and medium-sized enterprises in 2002 and 2003, the independent sample T test and principal component analysis are carried out on 20 initial input indicators, and a simplified index system with only 10 indicators is obtained. Then, the multiple linear discriminant model and the Logit model are tested in detail by the input index data. The conclusion is that the multivariate linear discriminant model tends to enter the discriminant equation with all the 10 simplified indexes into the discriminant equation. The accuracy rate of comprehensive prediction reached 76.1g 2 years before default, 81.4 logit model one year before default preferred to use "Backword:Conditional" method, and the model when Logit analysis reached step 4, The accuracy rate of comprehensive prediction is 78.8% two years before default, and 85.8% of logit model is higher than that of multivariate linear discriminant model one year before default, but it is not as good as the latter in the second kind of critical misjudgment rate. It is easy to judge the defaulting enterprise as a normal enterprise and ignore its potential credit risk. Finally, on the basis of the results of the model, this paper analyzes the credit risk characteristics of small and medium-sized enterprises in China, and compares the credit risk characteristics of small and medium-sized enterprises in China with the credit risk study of private enterprises in ten more developed countries, such as the United States, Australia and other more developed countries, using Moody's default model. The results show that, except for non-financial factors, it is significant and stable. The first two factors which can reflect the characteristics of credit risk of SMEs in China are capital structure index and profitability index, which are basically similar to the average results of the ten countries.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2006
【分类号】：F224
本文编号：2233520
[Abstract]:The vigorous development of small and medium-sized enterprises has a strong economic externality, which is not only an important force to promote the sustained, stable and high speed growth of the national economy, but also plays an active role in adjusting the economic structure and expanding social employment. However, the financing of small and medium enterprises in China has been facing the "Macmillan gap." Both the financing trend of small and medium-sized enterprises in China and the financing structure of foreign small and medium-sized enterprises show that under the constraints of the real environment, there is only one direction to solve the financing dilemma of small and medium-sized enterprises-to expand the indirect financing system. Increasing debt financing from banks and strengthening credit risk assessment of small and medium-sized enterprises is the key link. With the decreasing of customer resources and the subdivision of credit market, the measurement of credit risk for small and medium-sized enterprises will inevitably become a major issue that Chinese banks should pay more attention to in the future. This paper focuses on the topic of how to establish a credit risk assessment model suitable for China's national conditions, aiming at SMEs as a special enterprise group, on the basis of reviewing and comparing the relevant methods and models of credit risk measurement. Firstly, it points out that multivariate statistical analysis is the most feasible method to measure the credit risk of SMEs in China at present. Then, based on the financial data and non-financial data of 113 small and medium-sized enterprises in 2002 and 2003, the independent sample T test and principal component analysis are carried out on 20 initial input indicators, and a simplified index system with only 10 indicators is obtained. Then, the multiple linear discriminant model and the Logit model are tested in detail by the input index data. The conclusion is that the multivariate linear discriminant model tends to enter the discriminant equation with all the 10 simplified indexes into the discriminant equation. The accuracy rate of comprehensive prediction reached 76.1g 2 years before default, 81.4 logit model one year before default preferred to use "Backword:Conditional" method, and the model when Logit analysis reached step 4, The accuracy rate of comprehensive prediction is 78.8% two years before default, and 85.8% of logit model is higher than that of multivariate linear discriminant model one year before default, but it is not as good as the latter in the second kind of critical misjudgment rate. It is easy to judge the defaulting enterprise as a normal enterprise and ignore its potential credit risk. Finally, on the basis of the results of the model, this paper analyzes the credit risk characteristics of small and medium-sized enterprises in China, and compares the credit risk characteristics of small and medium-sized enterprises in China with the credit risk study of private enterprises in ten more developed countries, such as the United States, Australia and other more developed countries, using Moody's default model. The results show that, except for non-financial factors, it is significant and stable. The first two factors which can reflect the characteristics of credit risk of SMEs in China are capital structure index and profitability index, which are basically similar to the average results of the ten countries.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2006
【分类号】：F224
【引证文献】
相关硕士学位论文 前4条
1 徐璐;中小房地产企业贷款信用风险评价研究[D];西南财经大学;2011年
2 应千凡;中国非上市公司信用风险度量研究[D];浙江大学;2007年
3 李鑫;物流企业信贷信用风险度量研究[D];西南交通大学;2009年
4 陈平平;中国商业银行信用风险度量实证研究[D];江西财经大学;2012年
，本文编号：2233520
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