企业债券信用评级体系在矿山机械制造业的应用分析

发布时间：2018-01-14 13:34

本文关键词：企业债券信用评级体系在矿山机械制造业的应用分析　出处：《太原理工大学》2015年硕士论文　论文类型：学位论文

【摘要】：煤炭产业是国民经济发展的基础产业,矿山机械制造业是采矿业及煤炭开采和洗选业发展的基础产业,具有行业关联度高、技术带动力度大等特点,被称为煤矿机械化建设的“发动机”,作为国家重点培育和发展的七大战略性新兴产业之一,近年来呈现出快速发展的态势。受当前宏观经济下行和煤炭企业固定资产投资增速下降的影响,自2013年以来,矿山机械制造业销售增速明显回落,而同期行业投资扩张的趋势明显,因此,要实现矿机企业的创新发展,资金支持必不可少,作为企业融资重要渠道之一的债券融资,一定程度上解决了矿机企业的资金困境。对矿山机械制造业企业进行债券信用评级也因此显得意义重大,而要对发债企业的信用状况和债项具体保护措施做出全面、综合的评价,就必须依据一套适合该行业特征的,完整统一、而又能科学量化的评级指标体系。本文站在独立第三方的角度,从债券信用评级体系中较为核心的评级指标体系出发,结合矿山机械制造行业的典型特征,完善了现有债券信用评级指标体系,增加“行业状况”和“外部风险与支持”两项信用评价指标,从企业所处行业状况、自身综合实力、生产经营与管理质量、财务状况及外部风险与支持等五个方面,内外因素相结合,更全面、客观对矿山机械制造企业的信用风险状况进行评估。同时,采用Logit模型作为信用风险评估的辅助验证模型,从评价指标体系中选取35项主要财务指标,运用SPSS软件分别对所选指标数据进行多元共线性分析和Logit回归分析,对发债企业违约概率做出预测。研究表明,该模型对企业的信用风险评估预测性较好,平均准确率为91.6%,通过指标体系和Logit模型评价结果比较,验证矿山机械制造业债券信用评级指标体系的合理性。结合实践应用,本文以A矿山机械制造企业为实例,进行信用评级实际操作分析,使抽象的理论与实践操作融合为一体,验证了指标体系及所选方法的可靠性和实用性,丰富了企业债券信用评级体系,有一定的参考价值。
[Abstract]:The coal industry is the basic industry of national economic development, mining machinery manufacturing industry is the basic industry of the development of mining industry and coal mining and washing industry, with a high degree of correlation, the characteristics of technology with high power, known as mechanized coal mine construction "engine", as one of the seven strategic emerging industry's focus on fostering with the development in recent years, showing a trend of rapid development. Affected by the current economic downturn and the coal enterprise fixed asset investment growth decline, since 2013, mining machinery manufacturing industry sales growth rate dropped significantly, while the industry investment expansion trend is obvious, therefore, to realize the innovation and development of mining enterprises, financial support is essential as bond financing, corporate finance one of the important channels, to a certain extent to solve the financial difficulties of its enterprises. In mining machinery manufacturing enterprises bonds The credit rating and therefore is of great significance to, and specific measures for the protection of the credit and debt of corporate bonds to make a comprehensive, comprehensive evaluation, it must be based on a set of suitable for the characteristics of the industry, complete and unified, and scientific quantitative evaluation index system.
This paper stands in the perspective of an independent third party, starting from the bond credit rating system is the core of the evaluation index system, combined with the typical characteristics of mining machinery manufacturing industry, improve the existing bond credit rating index system, increase the "industry" and "external risk and support two credit evaluation index, from the status of the industry the enterprise, its comprehensive strength, production management and quality management, the five aspects of financial condition and external risk and support, the combination of internal and external factors, more comprehensive and objective of mining machinery manufacturing enterprise credit risk situation evaluation. At the same time, aided verification Logit model was used as a credit risk assessment, selection of 35 the main financial indicators from the evaluation index system, using SPSS software were selected index data by multi linear analysis and Logit regression analysis, the probability of default of corporate bonds do A prediction is made. The research shows that the prediction accuracy of the model is better than that of the Logit model, and the prediction accuracy is 91.6%. The rationality of the credit rating index system of mining machinery manufacturing industry is verified through the comparison between the index system and the evaluation result of the model.
Combining with the practical application, based on A mining machinery manufacturing enterprise as an example, the credit rating of actual operation analysis, make the abstract theory and practice integration, validated the reliability and practicability of the index system and the selected method, enrich the corporate bond credit rating system, there is a certain reference value.

【学位授予单位】：太原理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F426.4;F406.7

【参考文献】