基于供应链融资模式的中小企业信用风险评价研究

发布时间：2018-12-14 18:59

【摘要】：近年来,随着我国中小企业数量的增加,其在促进经济增长、维护社会稳定、增加就业等方面的重要作用日益显现出来。但是,中小企业在发展的过程中面临许多难题,特别是资金障碍问题已经日益凸显。然而,中小企业的信贷融资现状与其在经济发展中的地位极不相符,其面临的信贷缺口很大。如何有效缓解中小企业的融资困境,特别是信贷融资困境应是解决中小企业发展问题时关注的重点。现有的解决对策多是从宏观层面出发,国家建立相关的融资制度、发展金融支持体系等,但考虑到目前我国金融市场的发展现状,仅依靠以上途径解决中小企业的信贷融资问题仍需较长时间,短期内无法达到帮助企业融通资金的目的。目前已有学者建议通过供应链为中小企业提供融资,供应链融资突破了只考察单个企业的局限,从整个供应链的角度出发,既为长期存在的中小企业融资问题提供了对策,也增加了银行等金融机构的创新服务,拓展了业务量,在供应链融资活动中参与主体达到共赢。如何用科学的方法帮助银行等金融机构对企业的融资信用风险进行评价是供应链融资使用的先决条件。对现阶段我国的金融市场而言,已有的信用评级模型方法均存在一定的缺陷。而因变量二项分布的logistic回归模型不要求解释变量之间符合正态分布及等协方差的假定,模型也更容易理解和运用,所以本文选取logistic回归模型对供应链融资模式下中小企业的信用风险进行评价。首先设立了共含23个指标的基于供应链融资的信用风险评价指标体系,并选取了已在银行获得贷款的51家中小企业,其中包括正常还款的企业46家和发生不良贷款的企业5家。通过分析企业的调查报告,首先运用PASW Statistics18软件对正常还款企业近两年、发生不良贷款企业近四年共112组数据进行主成分分析,结果指标变量综合成供应链运营、偿债能力、融资项下资产、质物特征、企业盈利能力、企业营运能力、企业发展潜力和初始资本金8个主成分。再根据提取的主成分进行logistic回归分析,选择向前逐步选择引入法计算出保留在模型中的变量,模型求得的P值代表了中小融资企业的守约概率。本文还将供应链融资模型与一般融资模型进行了对比,发现前者的拟合优度和预测的准确率都比较高,这部分证明了供应链融资模式未将现有的信用风险放大。最后,在本文研究结论的基础上,对可继续深入探讨的问题进行了展望。
[Abstract]:In recent years, with the increase of the number of small and medium-sized enterprises in China, its important role in promoting economic growth, maintaining social stability, increasing employment and other aspects has become increasingly apparent. However, SMEs face many difficulties in the process of development, especially the problem of financial obstacles has become increasingly prominent. However, the credit financing situation of small and medium-sized enterprises is very inconsistent with its position in economic development, and the credit gap it faces is very large. How to effectively alleviate the financing difficulties of SMEs, especially credit financing difficulties, should be the focus of attention in solving the development of SMEs. Most of the existing solutions are based on the macro level, the state establishes the relevant financing system and develops the financial support system, etc., but considering the current situation of the development of the financial market in our country, Only relying on the above ways to solve the problem of credit financing of SMEs still needs a long time, and can not help enterprises to finance the purpose in the short term. At present, some scholars have proposed to provide small and medium-sized enterprises with financing through the supply chain. Supply chain financing has broken through the limitation of examining only a single enterprise, and from the point of view of the whole supply chain, it has provided countermeasures for the long-standing financing problems of small and medium-sized enterprises. It also increases the innovative services of banks and other financial institutions, expands the volume of business, and achieves win-win results in the financing activities of supply chain. How to use scientific methods to help banks and other financial institutions to evaluate the financing credit risk of enterprises is a prerequisite for the use of supply chain financing. For the present financial market, the existing credit rating models have some defects. The logistic regression model for binomial distribution of dependent variables does not require interpretation of the assumptions of normal distribution and iso-covariance between variables, and the model is easier to understand and use. So this paper selects logistic regression model to evaluate the credit risk of SMEs in supply chain financing mode. Firstly, a credit risk evaluation index system based on supply chain financing is set up, which includes 23 indexes, and 51 small and medium-sized enterprises which have obtained loans in banks are selected, including 46 enterprises with normal repayment and 5 enterprises with non-performing loans. Through the analysis of the investigation report of the enterprise, the principal component analysis of 112 groups of data of the normal repayment enterprise in the past two years and the non-performing loan enterprise in the past four years is carried out by using PASW Statistics18 software. The result is that the index variable synthesizes into the supply chain operation and the solvency. There are eight principal components under financing, such as assets, properties, profitability, operational capacity, development potential and initial capital. Then according to the extracted principal components of the logistic regression analysis, select forward selection and introduction method to calculate the variables retained in the model, the P value obtained by the model represents the probability of small and medium-sized financing enterprises. This paper also compares the supply chain financing model with the general financing model, and finds that the former has a higher degree of fitness and accuracy of prediction, which proves that the supply chain financing model does not amplify the existing credit risk. Finally, on the basis of the conclusion of this paper, the problems that can be further discussed are prospected.
【学位授予单位】：南京农业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F275;F832.4;F276.3

【相似文献】