基于层次分析法的我国中小企业信用评估方法研究

发布时间：2018-01-05 00:34

本文关键词：基于层次分析法的我国中小企业信用评估方法研究　出处：《电子科技大学》2014年硕士论文　论文类型：学位论文

【摘要】：我国经济的迅速发展将广大中小型企业的融资需求领入一个高潮时期。但是,由于缺乏抵押物、信用信息披露少、银行借贷的成本高,收益率低,以及银行在中小型企业信用评估体系方面的缺失,致使银行无法准确获得目标中小型企业的信用质量,这些都严重的阻碍了银行与中小企业的融资合作。因此,立足于商业银行,设计一种专门针对我国中小型企业的科学的信用评估方法,既可以帮助银行与中小企业展开良好的合作,同时也能促进优质的中小企业解决融资受限的难题。本文在参考了大量的文献资料及数据的基础之上,首先,对中小型企业的发展现状、融资现状和信用风险做了总结与分析;然后,对国内外各种信用评估理论方法的发展现状进行了阐述,介绍了几种常用的评估方法,例如传统的专家评定法、基于财务基础的Probit模型和Logit模型、借助数理模型的现代信用风险评估方法,以及基于机器学习的人工智能方法等,并对这些方法的优劣性进行了总结;最后,本文通过分析中小型企业的自身特征,通过因子分析法量身定制了一套专用的指标体系用于判别中小企业的信用状况,并且用层次分析法对该类型企业的信用等级做了进一步的评估。在理论研究之后,本人还借助真实的案例检验了该方法的有效性。通过本文的研究,共确定了十四个评价我国中小型企业信用水平的指标。该指标体系符合中国中小型企业财务数据有限,企业发展容易受行业前景等宏观条件的影响以及中小企业的发展突破重在创新等独有的特征,因此具有科学性和实用性。本文通过层次分析法也赋予了该指标体系中每一个指标在总体系中所占的合理的权重。因此本文的结论对实际解决中小型企业的信用评价的难题有很大的推动作用,满足了本文的研究目的。本文的主要创新点包括:(1)通过剖析中小型企业的自身特点,获得了针对中小型企业的专用特征指标以判断其信用级别;(2)设计了具体的信用评估的方法,并且通过实例加以分析。本文的工作可以有效的帮助商业银行从良莠不齐的中小型企业中筛选出有优良成长趋势的信贷客户。
[Abstract]:The rapid economic development of our country will lead the financing demand of the majority of small and medium-sized enterprises into a climax period. However, due to the lack of collateral, less credit information disclosure, the high cost of bank loans, low returns. And the lack of bank credit evaluation system in small and medium-sized enterprises, resulting in the bank can not accurately obtain the credit quality of the target small and medium-sized enterprises, which seriously hindered the financing cooperation between banks and small and medium-sized enterprises. Based on commercial banks, this paper designs a scientific credit evaluation method for small and medium-sized enterprises in China, which can help banks and small and medium-sized enterprises to carry out good cooperation. At the same time, it can also promote high-quality SMEs to solve the problem of financing constraints. This paper refers to a large number of literature and data on the basis of, first of all, the development of small and medium-sized enterprises. The present situation of financing and credit risk are summarized and analyzed. Then, the development status of various credit evaluation theories and methods at home and abroad is expounded, and several commonly used evaluation methods, such as traditional expert evaluation method, are introduced. Probit model and Logit model based on financial foundation, modern credit risk assessment method based on mathematical model and artificial intelligence method based on machine learning, etc. The advantages and disadvantages of these methods are summarized. Finally, this paper analyzes the characteristics of small and medium-sized enterprises, through factor analysis to customize a set of specific indicators to identify the credit status of small and medium-sized enterprises. And the use of AHP to further evaluate the credit rating of this type of enterprise. After theoretical research, I also use a real case to verify the effectiveness of this method. Through the study of this paper. A total of 14 indicators to evaluate the credit level of small and medium-sized enterprises in China are determined. The index system is in line with the limited financial data of small and medium-sized enterprises in China. The development of enterprises is easy to be affected by macro-conditions such as industry prospects and the development of small and medium-sized enterprises focus on innovation and other unique characteristics. Therefore, it is scientific and practical. This paper also gives the reasonable weight of each index in the total system through the analytic hierarchy process. Therefore, the conclusion of this paper is practical to solve the credit of small and medium-sized enterprises. The difficult problem of evaluation has a great impetus. The main innovation points of this paper include: 1) by analyzing the characteristics of small and medium-sized enterprises, we obtain the special characteristic index for small and medium-sized enterprises to judge their credit grade; 2) the method of credit evaluation is designed. The work of this paper can effectively help commercial banks to screen out the credit customers with good growth trend from the mixed small and medium-sized enterprises.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F276.3;F832.2

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