碩士 / 國立臺灣海洋大學 / 應用經濟研究所 / 92 / A series of crises are often arisen if there is something wrong with the management of the community financial, the damage and impact of which to economy is far more serious the that caused by the bankruptcy of a company. A financial warning system for the governing community financial institutions was more important. In the past researches of financial distress prediction, traditional statistical techniques such as multivariate statistical method, Before using the multivariable statistical method. There have been more artificial neural network applications to this field in domestic since 1994. According to those researches, financial distress prediction models build by artificial neural network was more feasible than traditional statistical methods. In this paper applied back-propagation network the build the financial distress prediction models, and to make the function of crisis management mechanism toward the community financial institution in Taiwan, and based on theoretical and legal construction. The predictability comparison provides the highest accuracy for Primitive BPN(81.10％) in the surveillance system, followed by Factory BPN(77.85％) and Ordered Logit(75.9％).
| Identifer | oai:union.ndltd.org:TW/092NTOU5452003 |
| Date | January 2004 |
| Creators | Ming-Feng Wu, 吳明峰 |
| Contributors | Hsiang-Hsi Liu, Ching-Ta Chuang, 劉祥熹, 莊慶達 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 85 |
Page generated in 0.0019 seconds