【作者】 冯涛；

【导师】 李力；

【作者基本信息】 哈尔滨工业大学 ， 金融学， 2015， 硕士

【摘要】 自沪深证券交易所相继成立以来,我国国内的证券交易市场已经走过了25个年头,它的繁荣发展是有目共睹的,尤其是我国的股市。股市尽管依然避免不了政策市的影响,但也愈加完善,不断地向发达国家的成熟市场靠拢,为国内的实体经济发展做出了不可否认的贡献。然而,即使是上市公司也不会一直持续健康地发展下去,其中的小部分会面临陷入财务困境的风险。本研究致力于预测上市公司财务困境,这对于企业的经营者、投资者、债权人和整个社会经济都具有重大的意义。本文选取了2008年至2013年间A股上市公司中的95家ST公司和190家非ST公司作为研究样本,并初步选取了22个财务指标作为模型初始自变量。应用逐步判别法对这22个初始自变量进行精简,结果显示逐步判别法能有效降低模型输入变量的维度,并且精简后的变量包含了足够反映上市公司未来财务状况的信息。本文构建了一个组合预测模型,使用(T-2)年的财务数据对样本T年的财务状况进行预测。首先在传统自适应神经网络模糊推理系统(ANFIS)的基础上,对其加以改进,增加了可以优化输出结果的分类层;然后用训练样本的数据分别训练改进的ANFIS系统和马氏距离判别法,再分别用训练后的两个模型对检验样本进行预测,把两个模型各自的预测结果和预测准确率作为贝叶斯网络的输入自变量,得到组合模型的预测结果。实证研究结果发现,马氏距离判别法能有效判别财务困境公司与财务健康公司,证明财务困境企业在未陷入财务困境之前的某些财务数据确实异于财务健康企业,同时在空间中会产生不同的聚类现象;组合预测模型分别计算对财务困境与财务健康公司的预测准确率,其预测性能要优于神经网络、支持向量机、Logit模型和单纯贝叶斯网络。更多还原

【Abstract】 Chinese security exchange market has gone through 25 years since Shanghai and Shenzhen Stock Exchange have been established. Its prosperity and development is huge, especially the stock market of our country. Although the stock market still can’t avoid the impact from policies of the government, it is improving increasingly, moving closer to the mature stock markets of developed countries, and makes great contribution to the development of the domestic real economy. However, even if listed companies will not be developing sustained and healthily forever, a small portion will face the risk of financial distress. This study is to predict the financial distress of listed companies, which are of great significance for the business owners, investors, creditors and the whole economy.95 companies listed in Shanghai and Shenzhen Stock Exchange, specially treated because of abnormal financial status between 2008 and 2013, are chosen as the financial distress company samples, and 190 healthy listed companies are chosen as paired samples. Meanwhile, 22 financial indicators of listed companies are chosen as the initial independent variables, and then these indicators are refined by the Stepwise method. The result shows that the Stepwise method is capable of reducing the dimension of model variables, and variables after refining cover the sufficient information which reflects the company’s future financial status.This study proposes a combined forecasting model which predicts the companies’ financial status of year T with financial data of year(T-2). Firstly, a classification layer is added to optimize the output based on the traditional Adaptive-network-based Fuzzy Inference System(ANFIS). Then the data of the training sample is used to train the improved ANFIS and Mahalanobis distance discriminant method, which respectively predict consequence of the checking sample. Finally, the consequence and prediction accuracy rate of these two models are input into the Bayesian network, getting the prediction consequence of the combined models.The empirical analysis results show that Mahalanobis distance discriminant method can discriminant those financial distress companies and healthy finance companies effectively, which proves that some financial indicators of companies before being involved in financial distress is obvious different from healthy finance companies and it exists different clustering phenomena in space; the prediction accuracy rate of the financial distress companies and the healthy finance companies are counted separately, and the prediction performance of the proposed combined model is better than that of the Neural Network, Support Vector Machine, Logit model and pure Bayesian Network.更多还原