煤炭行业发债企业信用风险影响因素研究

发布时间：2018-05-09 20:08

本文选题：煤炭行业 + 发债企业　；参考：《北京交通大学》2017年硕士论文

【摘要】：2015年,26家上市的煤炭企业只取得15.43亿元净利润,同比骤跌超过95%。其中有11家出现亏损,占比高达四成,煤炭行业呈整体亏损局面。2016年开始触底反弹,但情况仍然不容乐观。同时,银行对煤炭行业的企业授信开始缩紧,煤炭行业大部分企业融资渠道受限,资金面紧张。2014-2016年,中国债市爆发违约事件,刚性兑付神话不再,大众开始真正关注信用风险,以探求信用风险的影响因素,通过因素的数据分析,对债券或企业信用风险进行提前预测,以进行合理投资或提前采取措施减少损失。煤炭行业境况不乐观,同时作为违约债券主体中占比较大行业,受到投资者和债权人关注。因此,研究煤炭行业发债企业信用风险具有现实意义。本文通过地区宏观经济水平、地区财政实力以及地区行业水平对煤炭行业发债企业信用风险进行宏观层面成因分析,从企业性质、信用评级以及企业的财务状况进行微观层面成因分析。本文以2016年具有存量债券的85家煤炭行业发债企业作为样本,并根据现代信用风险定义,将样本分为包括23家样本企业的发生信用风险事件组和包括62家样本企业的未发生信用风险事件组。在成因分析基础上,分别从宏观层面选取5个指标和微观层面选取10个指标,对各指标进行相关性分析,剔除具有共线性解释变量。之后利用Logistic模型对煤炭行业发债企业的信用风险影响因素进行实证分析,并得到预测煤炭行业发债企业信用风险大小的方程。通过实证分析,最后显示宏观层面的地区CPI及地区固定资产投资价格指数以及微观层面信用评级、总资产报酬率、总资产周转率、资产负债率以及流动资产与总资产的比率共7个指标对煤炭行业发债企业信用风险具有显著影响。其中,资产负债率、流动资产与总资产的比率对煤炭行业发债企业信用风险具有正相关影响,即该值越低,煤炭行业发债企业发生信用风险事件可能性越低。其余指标对煤炭行业发债企业信用风险产生负方向影响,即指标数值越低,煤炭行业发债企业信用风险事件发生可能性越高。通过以上7个指标,本文生成预测煤炭行业发债企业信用风险大小的方程,其预测正确率达到89.4%,具有实用价值。可帮助投资者提前预测煤炭行业发债企业信用风险大小,协助企业债权人、投资者以及管理者对企业未来信用风险进行判断。
[Abstract]:In 2015, 26 listed coal companies made only 1.543 billion yuan in net profits, down more than 95 percent from a year earlier. Eleven of them lost money, or as much as 40 percent, with the coal industry as a whole losing money. A bottoming out in 2016 is still not encouraging. At the same time, banks began to tighten credit to enterprises in the coal industry, and most of the enterprises in the coal industry were restricted in their financing channels. During the period 2014-2016, when China's bond market defaulted, the myth of rigid payment ceased, and the public began to pay real attention to credit risks. By exploring the influencing factors of credit risk and analyzing the data of the factors, the paper forecasts the credit risk of bond or enterprise in advance, so as to make reasonable investment or take measures to reduce the loss ahead of time. The coal sector is not optimistic, and as the main body of defaulting bonds, the larger sector, investors and creditors are concerned. Therefore, it is of practical significance to study the credit risk of bond issuing enterprises in coal industry. Based on the regional macroeconomic level, regional financial strength and regional industry level, this paper analyzes the causes of the credit risk of the coal industry bond issuing enterprises at the macro level, and analyzes the nature of the enterprises from the point of view of the nature of the enterprises. Credit rating and the financial situation of enterprises are analyzed at the micro level. This paper takes 85 coal industry bond issuers with stocks of bonds in 2016 as a sample, and defines the credit risk according to the modern credit risk definition. The samples are divided into two groups: the occurrence of credit risk event group which includes 23 sample enterprises and the non-occurrence credit risk event group which includes 62 sample enterprises. On the basis of cause analysis, 5 indexes were selected from macro level and 10 indexes were selected from micro level, and the correlation of each index was analyzed, and the co-linear explanatory variables were eliminated. Then the Logistic model is used to analyze the influencing factors of the credit risk of the bond issuing enterprises in coal industry, and the equation of predicting the credit risk of the bond issuing enterprises in the coal industry is obtained. Through the empirical analysis, it shows that the regional CPI and regional fixed asset investment price index, as well as the micro-level credit rating, total asset return rate, total asset turnover rate, The ratio of assets to liabilities and the ratio of current assets to total assets have a significant impact on the credit risk of bond issuing enterprises in coal industry. Among them, the ratio of assets and liabilities, the ratio of current assets to total assets has a positive correlation with the credit risk of bond issuing enterprises in coal industry, that is, the lower the value, the lower the probability of credit risk events. The other indicators have a negative impact on the credit risk of the bond issuing enterprises in coal industry, that is, the lower the index value, the higher the probability of the credit risk events of the bond issuing enterprises in the coal industry. Through the above seven indexes, this paper generates the equation of predicting the credit risk of the bond issuing enterprises in the coal industry, and its correct rate of prediction is up to 89.4, which is of practical value. It can help investors predict the credit risk of bond issuing enterprises in the coal industry in advance, and help the creditors, investors and managers to judge the future credit risk of enterprises.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F426.21;F832.51

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