碩士 / 明新科技大學 / 企業管理研究所 / 95 / Corporation financial distress could not only have an impact on the economic order of a country, but also on its society. The Beaver’s study (1966) on the prediction model of financial distress was recognized as the benchmark research in the academic circle. With the due progress in the fields of information technology and data mining technique, the prediction model of financial distress could break through various limitations on traditional models by using statistical methods, and be built by utilizing knowledge on decision tree, neural networks and logistic regression. The purpose of this study is to construct a prediction model for corporation financial crisis using financial ratio, intellectual capital, and macroeconomic index as variables. The model is applicable with the analytic methods of decision tree, neural networks, and logistic regression. This study’s result shows that a prediction model using all the three variables together performs better than one only using financial ratio. The former model provides lower prediction error than the latter. Furthermore, the study finds that the methods of neural networks and logistic regression offer better analytical framework than decision tree. And both neural networks and logistic regression perform equally well with the predictive model. In conclusion, these three analysis techniques have found that the credits interdependent degree is the predictor of financial distress. In other words, the higher credits interdependent degree, the occurrence of financial distress would be.
| Identifer | oai:union.ndltd.org:TW/095MHIT5121008 |
| Date | January 2007 |
| Creators | Long Shao-qi, 龍劭琪 |
| Contributors | Lin Li-hsueh, 林麗雪 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 82 |
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