电力行业上市公司财务预警研究

发布时间：2018-06-03 23:18

本文选题：电力上市公司 + 财务预警　；参考：《西安工业大学》2013年硕士论文

【摘要】：电力行业是国民经济的重要组成部分。电力行业不仅在国民经济中占有很高的比重,同时也关乎国计民生和公益事业；对国民经济和工业部门的发展起着非常重要的支撑作用。作为核心资产板块,受到越来越多投资者的关注。电力上市公司之间的竞争日趋激烈,财务健康状况成为利益相关者各方关注的焦点。财务健康是企业经营的基础。没有健康的财务状况企业随时都处在财务风险之中。企业因为财务风险的发生而陷入财务困境,最终导致破产的情况很多。任何企业财务危机的发生都是一个逐渐恶化的过程。选定财务比率,构建财务预警模型发现财务风险的信号,能够有效预测企业的财务风险。文章在对国内外研究进行综述的基础上,梳理了上市公司财务预警理论,对相关研究进行了分析和归纳。选取证监会2001发布《上市公司分类指引》中的电力、煤气及水的生产和供应大类中51家电力上市公司作为研究样本；将这些公司分为财务健康公司、潜在危机公司和ST公司；利用2006-2011年年报数据,筛选并计算反映盈利能力、偿债能力、成长能力、运营能力和现金流量五个方面27个财务比率指标。采用2010、2011两年的财务比率指标运用因子分析方法进行统计分析,KMO抽样适度测定值均大于0.5,Bartlett球形度检验结果显著说明因子分析的有效性；计算了盈利能力、偿债能力、成长能力、运营能力和现金流量五个方面提取公共因子得分,对2010、2011年电力上市公司进行了综合排序和比较分析；利用多元线性回归,找到了影响绩效的关键词财务指标。针对样本中的ST公司和非ST公司,利用BP_adaboost算法构建BP神经网络模型对2010、2011年电力上市公司进行分类,显示分类的有效性在80%以上。以2006年财务比率为基础,提取公共因子,利用COX模型对2006-2011年电力板块上市公司财务状况构建生存分析模型进行预测分析,实证结果显示三类公司具有不同的波动结果。文章最后对研究内容进行了总结,并指出今后研究的方向。
[Abstract]:Electric power industry is an important part of national economy. The electric power industry not only occupies a high proportion in the national economy, but also relates to the national economy and the people's livelihood and the public welfare, and plays a very important supporting role in the development of the national economy and the industrial sector. As a core asset plate, by more and more investors concern. The competition among listed power companies is becoming more and more fierce, and the financial health has become the focus of the stakeholders. Financial health is the foundation of business operation. There is no healthy financial situation at any time enterprises are in financial risk. Because of the occurrence of financial risk, enterprises fall into financial distress and eventually lead to bankruptcy. The occurrence of any enterprise financial crisis is a process of gradual deterioration. Choosing financial ratio and constructing financial warning model to find financial risk can effectively predict the financial risk of enterprises. On the basis of summarizing the domestic and foreign research, this paper combs the financial early-warning theory of listed companies, and analyzes and summarizes the related research. Select 51 listed power companies in the production and supply categories of electricity, gas and water issued by CSRC 2001 as the research sample, divide these companies into financial health companies, potential crisis companies and St companies; Based on the annual report data for 2006-2011, 27 financial ratio indicators reflecting profitability, solvency, growth capacity, operational capacity and cash flow are selected and calculated. Using the financial ratio index of 2010 / 2011 to carry on the statistical analysis by using the factor analysis method, the results of the KMO sampling test are all larger than 0.5% Bartlett's spherical degree test results show the validity of the factor analysis, and calculate the profitability, the solvency, the growth ability, the efficiency of the factor analysis, the calculation of the profit ability, the debt service ability and the growth ability. This paper extracts the common factor scores from five aspects of operation ability and cash flow, and makes a comprehensive ranking and comparative analysis of the power listed companies in 2010 and 2011. By using multiple linear regression, we find the key word financial indicators that affect the performance. For St companies and non-St companies in the sample, BP neural network model based on BP_adaboost algorithm is used to classify the listed power companies in 2010 and 2011. The results show that the validity of the classification is more than 80%. Based on the financial ratio of 2006, this paper extracts the common factors and uses COX model to predict and analyze the financial situation of the listed companies in the electric power plate from 2006 to 2011. The empirical results show that the three types of companies have different volatility results. Finally, the research content is summarized and the future research direction is pointed out.
【学位授予单位】：西安工业大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F406.7;F426.61

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