节点文献
商业银行对企业信用风险度量和管理的研究
【作者】 徐莉;
【导师】 贺伟奇;
【作者基本信息】 中南大学 , 应用经济学, 2010, 硕士
【摘要】 企业已经成为促进我国经济持续快速、健康发展的重要力量,而我国企业通过证券市场直接融资的上市条件较为严格,因此,通过商业银行进行间接融资成为企业获得资金的重要渠道。研究银行对企业的信用风险度量具有重要经济价值和社会价值。本文利用四种统计模型来度量和评价企业间接融资的信用风险。除绪论及参考文献、附表部分外,本文实体部分共分为四个部分:第一部分,主要介绍了国内外商业银行对企业信用风险度量的研究情况,探究适合我国国情的对企业信用风险度量的研究方法。第二部分,首先阐述信用风险的相关概念及其信用风险评级在我国经济发展中的作用和意义,为信用风险模型的建立提供准备。第三部分,结合我国中小企业信用风险度量的特点,建立企业信用风险度量的分类模型,本文利用企业财务数据,首先对原始数据进行主成分分析,选取出合理的指标,再分别采用Logistics回归、贝叶斯判别分析方法、贝叶斯网络分类等方法对企业的资本负债及财务进行中小企业信用风险度量的实证研究。第四部分,结论与评价部分,针对模型实际结果与理论结果的差异,提出了研究的评价和建议。
【Abstract】 The enterprises have become important strength to promote national’s economy developing continued, fast and healthily. It is an important channel to acquire capital by indirect fancying from commercial banks, because the requirement for the enterprise by going public is very strict. It has important economical and social value to research the enterprise’s credit rating.This thesis makes use of four kinds of statistical models to measure and evaluate the indirect fancying credit rating. In addition to introduction, references and attached lists, the physical part of this paper is divided into four parts:In the first part, it introduces the studies on credit rating from commercial banks at home and aboard, for exploring the solution fitting for our own national situation.In the second part, it introduces related concepts, effects and sense on credit rating. And then, we analyze and set up indexes system for constructing credit rating models.In the third part, it combines our national situation to construct the enterprise’s credit rating classing models. Besides, this article chooses financial data in the annul report. First, we choose reasonable indexes by principal component analysis. And then, use Logistical regression, Bayesian discrimination analysis and Bayesian networks methods to make empirical studies on credit rating.In the forth part, it is conclusion and evaluation part. We find the causes of difference between actual results and theoretical results, and then propose evaluations and advices.
【Key words】 credit rating; principal component analysis; Logistical regression; Bayesian discrimination analysis; Bayesian networks;
- 【网络出版投稿人】 中南大学 【网络出版年期】2011年 02期
- 【分类号】F224;F832.2
- 【被引频次】2
- 【下载频次】242