本文選題：電力上市公司 + 財務預警��； 參考：《西安工業(yè)大學》2013年碩士論文

【摘要】：電力行業(yè)是國民經濟的重要組成部分。電力行業(yè)不僅在國民經濟中占有很高的比重,同時也關乎國計民生和公益事業(yè)；對國民經濟和工業(yè)部門的發(fā)展起著非常重要的支撐作用。作為核心資產板塊,受到越來越多投資者的關注。電力上市公司之間的競爭日趨激烈,財務健康狀況成為利益相關者各方關注的焦點。財務健康是企業(yè)經營的基礎。沒有健康的財務狀況企業(yè)隨時都處在財務風險之中。企業(yè)因為財務風險的發(fā)生而陷入財務困境,最終導致破產的情況很多。任何企業(yè)財務危機的發(fā)生都是一個逐漸惡化的過程。選定財務比率,構建財務預警模型發(fā)現(xiàn)財務風險的信號,能夠有效預測企業(yè)的財務風險。文章在對國內外研究進行綜述的基礎上,梳理了上市公司財務預警理論,對相關研究進行了分析和歸納。選取證監(jiān)會2001發(fā)布《上市公司分類指引》中的電力、煤氣及水的生產和供應大類中51家電力上市公司作為研究樣本；將這些公司分為財務健康公司、潛在危機公司和ST公司；利用2006-2011年年報數(shù)據(jù),篩選并計算反映盈利能力、償債能力、成長能力、運營能力和現(xiàn)金流量五個方面27個財務比率指標。采用2010、2011兩年的財務比率指標運用因子分析方法進行統(tǒng)計分析,KMO抽樣適度測定值均大于0.5,Bartlett球形度檢驗結果顯著說明因子分析的有效性；計算了盈利能力、償債能力、成長能力、運營能力和現(xiàn)金流量五個方面提取公共因子得分,對2010、2011年電力上市公司進行了綜合排序和比較分析；利用多元線性回歸,找到了影響績效的關鍵詞財務指標。針對樣本中的ST公司和非ST公司,利用BP_adaboost算法構建BP神經網(wǎng)絡模型對2010、2011年電力上市公司進行分類,顯示分類的有效性在80%以上。以2006年財務比率為基礎,提取公共因子,利用COX模型對2006-2011年電力板塊上市公司財務狀況構建生存分析模型進行預測分析,實證結果顯示三類公司具有不同的波動結果。文章最后對研究內容進行了總結,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.
【學位授予單位】：西安工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F406.7;F426.61

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本文編號：1974671