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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/45078

Title: An Application of Neural Network on Early Warning System by Rating for the Credit Department of Fishermen Association in Taiwan
Authors: 莊慶達;劉祥熹;吳明峰
Contributors: 國立臺灣海洋大學:海洋事務與資源管理研究所
Date: 2004
Issue Date: 2017-12-28T01:08:43Z
Publisher: the 12 th Biennial Conference of IIFET
Abstract: Abstract:This paper applies the Back-Propagation Network (BPN) to build the financial distress prediction models. Empirical results show that the effect of BPN on crisis management mechanisms towards communities' financial institutions in Taiwan is doing quite fine. In addition, the predictability comparison indicates that the highest accuracy is the Primitive BPN (81.1%) in the surveillance system, followed by the Factory BPN (77.85%) and the Ordered Logit (75.9%). Damages and impacts to the fishing community and industry are always far more serious when financial crises occur in the community's financial institutions. Thus, a more accurate financial warning system for governing these financial institutions is needed more than ever. The artificial neural network (ANN) suggested in this study can provide a bankruptcy predictor of financial distress among credit unions.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/45078
Appears in Collections:[海洋事務與資源管理研究所] 演講及研討會

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