【作者】 李志毅；

【导师】 巢剑雄；

【作者基本信息】 湖南大学 ， 管理科学与工程， 2003， 硕士

【摘要】 财务困境的判别和预测已经成为商业评级中的重要内容。因为它会极大地影响投资者、信贷者以及银行官员的财务决策。审计人员也需要通过财务困境的判别和预测来获取财务信息，从而判断企业的经营是否具有可持续性。有很多的方法可以用于解决企业财务困境的预测问题。其中运用最多的是统计方法，包括多元判别分析法(MDA)、Logit法和Probit法。自从Altman将MDA引入企业财务困境判别后，MDA方法已被广泛应用于企业破产预测、企业信用评级、信贷评级等等领域。但是MDA的使用在方法论上却正遭受到挑战。由于有些变量分布并不符合MDA所需要的统计假设，所以使用MDA方法有可能导致判别结果产生偏差。决策树、Logit法和Probit法是MDA的替代方法。但它们同样要求样本满足一定的统计假设，而这些统计假设也限制了这些方法的应用。 作为另一个可供选择的模型，神经网络是完全适合于解决企业财务困境的预测问题的。神经网络通过其神经元之间的联接权来代表非线性的判别关系。本文将神经网络应用到企业财务困境的判别问题中。利用1999年-2002年ST公司的财务数据，本文将25个财务指标作为神经网络的输入，而企业陷入财务困境的概率作为神经网络的输出。实证结果显示，神经网络从其预测精度，预测的适应性，以及鲁棒性来说完全适合于解决企业财务困境的判别和预测问题。本文同时讨论了神经网络在方法论上的一些局限性。更多还原

【Abstract】 Financial distress prediction is one of major business classification tasks because it greatly affects the financial decision making of investors, credits, and bank officers. Auditors also need information on financial distress prediction for the going-concern judgment. There are many statistical procedures to handle this financial distress prediction problem. The most widely used classification technique is statistical methods including MDA, logit, and probit methods. Since Altman introduced the use of MDA to financial distress prediction, MDA has been widely applied to the business classification, including bankruptcy prediction, credit rating, and bank loan classification, etc. The studies using MDA have encountered, however, some methodological problems. The violation of the underlying normality assumption of independent variables causes the biased results.. The decision tree, logit, and probit methods have been used as alternative statistical methods. However, they also require different kinds of statistical assumptions which limit the usefulness of their application.As one of alternative methods, it is well known that neural network approach is very promising for the financial distress prediction problem. A neural net represents a nonlinear discriminant fuction as a pattern of connections between its processing units. In this paper, we propose a new neural network model to the financial distress prediction problem. Based on the financial data of the specially treated companies during the year from 1999 to 2002, we use 25 finacial ratios as input and the probability as output. Empirical results show that neural network is a promising method of financial distress prediction in terms of predictive accuracy, adaptability, and robustnee. Limitations of using neural nets as a general moding tool are also discussed.更多还原