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论文编号：	747
作者编号：	2120062016
上传时间：	2008/6/23 14:14:31
中文题目：	中国制造业上市公司财务危机预警
英文题目：	The Empricial Research on Fina
指导老师：	陈国欣
中文关键字：	财务危机；财务危机预警；因子分
英文关键字：	Financial Distress;Financial D
中文摘要：	随着我国经济的发展和上市公司数量的逐渐增多，财务危机越来越成为影响企业生存和发展的重大问题。许多企业因为种种原因发生了财务危机，这不仅给股东、债权人、企业员工等利益相关者造成了重大的经济损失，而且也造成了整个社会经济资源的浪费，甚至影响整体经济的正常发展。因此如何有效地预测和防范财务风险，日益成为学术界和企业界关注的热点问题。本研究正是基于这样的研究背景，通过对以往财务危机预警理论与实证方法的回顾，试图建立科学有效的财务预警模型，对企业可能面临的财务危机进行预警和防范。 本研究选取了2003-2007年度被首次ST的108家A股制造业上市公司，并按照资产规模相近原则配对选取了108家财务健康公司共同组成本研究的研究样本，并随机组成估计样本和检验样本Ⅰ；同时为验证模型对亏损公司的预测能力，本研究还选取了2003-2007年度发生亏损的54家公司和另外54家财务健康的公司作为检验样本Ⅱ。结合前人的研究成果与现实研究需要，本研究选取了涵盖企业偿债能力、盈利能力、营运能力、成长能力与现金流量状况5大方面的22个财务指标作为构建财务危机预警模型的原始指标体系，在此基础上本研究利用估计样本数据，采用因子回归法、Logistic回归法以及两种方法相结合这3种方法构建了3种上市公司财务危机的预警模型，并利用检验样本对3种模型的预测效果进行评价。 研究结果表明：（1）财务危机的发生是循序渐进的过程，因此具有可预测性；（2）因子分析法构建的预警模型包含了原始财务指标83.94%的信息，预测准确率达到80%以上，但存在着无法做出明确判断的灰色区域；（3）传统Logistic回归法构建的预警模型预测准确率高于因子分析法构建的模型，但可能造成重要财务信息的缺失；（4）因子分析与Logistic回归结合法构建的财务预警模型有效地解决了灰色区域和财务信息缺失的问题，且模型的预测正确率在三种模型中居于首位，具有良好的实际应用价值。
英文摘要：	With the development of the Chinese economy and increase of listed companies, financial distress is gradually becoming a life-and-death question for companies. Financial distress occurred in companies for various reasons, which not only engenders the economic loss of shareholders, creditors and employees, but also wastes the economic resource of the whole society, causing damages to the healthy development of the national economy. So how to effectively forecast and prevent financial distress is becoming the focus for theoretical and practical research. Therefore, through reviewing the theories of financial distress and empirical research methods, this paper tries to build scientific and effective financial warning models to forecast and prevent financial distress. This paper chooses 108 manufacturing listed companies implemented special treated for the first time between 2003 and 2007 and paired 108 non-financial distress companies at the same period according to the similarity in asset and scale as the research samples which constitute estimate samples and test samples Ⅰ at random. Meanwhile in order to test the models’ forecasting ability to loss-making companies, 54 manufacturing loss-making companies and 54 manufacturing profit-making companies have been selected as the test samples Ⅱ. Considering former research accomplishments and real research requirement, this paper chooses 22 financial indexes to build up original index system. These financial indexes cover 5 financial aspects, which are debt paying ability, earning capacity, operation efficiency, growth ability and cash flow condition. On this basis, the paper utilizes estimate samples and adopts factor analysis, logistic analysis and the combination of the two above-mentioned methods to build up 3 financial warning models. After that, the test samples are used to evaluate the forecasting efficiency of these 3 models. The conclusions show that: (1) the occurrence of financial distress is a gradual process, so it can be predicted; (2) the financial warning model established by factor analysis contains 83.94% information of the original financial indexes and the correct rate of prediction is over 80%, but the defect is that a gray area exits in the model which can not be predicted definitely;(3) the financial warning model established by traditional logistic regression has a higher correct rate of prediction than the former, but it may engender the loss of significant financial information;(4) the model established by the method which combines factor analysis with logistic regression effectively solves the problems of gray area and loss of financial information, meanwhile, the correct rate of prediction is the highest in the 3 models which is of greater practical value.
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