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论文编号：	11808
作者编号：	2120183049
上传时间：	2020/6/22 11:07:34
中文题目：	B银行医药制造企业信用评级模型优化研究
英文题目：	Research on Credit Rating Model Optimization of Pharmaceutical Manufacturing Enterprises of Bank B
指导老师：	张晓农
中文关键字：	信用评级，信贷风险，层次分析法，模糊综合评价法
英文关键字：	credit rating, credit risk, analytic hierarchy process, fuzzy comprehensive evaluation method
中文摘要：	B银行的企业信用评级结果影响着该银行信贷活动的各个环节。由于原有信用评级模型在指标体系的构建、评级资料的选取和评级流程上存在诸多不足，本文通过研究信贷风险相关文献和当前国内外信用评级机构的评级方法，以医药制造企业信用评级为例对B银行现有的企业信用评级模型进行了优化。 论文的研究方法主要有文献研究法、问卷调查法、层次分析法和模糊综合评价法。首先，结合B银行的实际情况和我国医药制造行业的特点，通过文献分析法从盈利能力、偿债能力、资产质量、竞争优势、管理水平五个方面选取了21个指标，构造了优化信用评级模型的指标体系；随后，论文设计了指标相对重要性问卷，通过问卷调查法邀请银行信贷和财务研究领域的专家对不同指标间的相对重要性进行打分；接着，基于专家的打分结果，在所有专家打分表通过了一致性检验的基础上，以层次分析法确定了各指标权重。最后，以H公司为例，基于模糊综合评价法构建了优化信用评级模型的评级流程。 论文以X、Y两公司的案例证明了优化后的信用评级模型能够改善原有评价模型存在的不足。X公司的案例说明了优化模型如何帮助B银行避免错过优质客户，Y公司的案例说明了优化模型如何帮助B银行避免放贷给无力还款的客户。结论是采用新模型能使B银行信用评级结果更好地反映企业客户实际还贷能力，提高B银行的信贷活动效率。
英文摘要：	Enterprise credit rating results of B bank affect every link of the bank’s credit-loan releasing. The original credit rating model has a number of deficiencies in its index system construction, rating material selection and rating process. By studying the literature home and abroad relating to credit risk and current rating method of credit rating agencies, this paper takes pharmaceutical manufacturing enterprise credit rating as an example to optimize B bank’s existing enterprise credit rating model. The research methods of this paper mainly include literature research, questionnaire survey, analytic hierarchy process and fuzzy comprehensive evaluation. Firstly, based on the actual situation of bank B and the characteristics of China's pharmaceutical manufacturing industry, 21 indicators were selected from five aspects of profitability, solvency, asset quality, competitive advantage and management level through literature analysis, and an indicator system for optimizing the credit rating model was constructed. Secondly, after designing a questionnaire on the relative importance of indicators, experts in the field of bank credit and finance were invited to rate the relative importance of different indicators. Thirdly, based on the scoring results， the weight of each index is determined by analytic hierarchy process. Finally, taking H company as an example, the scoring process of optimizing credit rating model is constructed based on fuzzy comprehensive evaluation method. The cases of X company and Y company prove that the optimized credit rating model can improve the deficiencies of the original evaluation model. The case of company X illustrates how the optimized model can help bank B avoid missing out high-quality customers, and the case of company Y illustrates how the optimized model can help bank B avoid lending to customers who are unable to repay. The conclusion is that the optimized model can better reflect the actual repayment ability of corporate customers and improve the credit activity efficiency of bank B.
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