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Application of Neural Network on Early Warning System by Rating for the Credit Department of Fishermen Association in Taiwan

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  • Series of financial crises arose in Taiwan since 1997 Asia financial crises, and it starts happened to the community financial institutions since then. In general, damages and impacts to the community and industry are always far more serious than that caused economy by the bankruptcy of a company. Thus, a more accurate financial warning system for governing community financial institutions is more needed than ever. However, past researches of financial distress prediction, which applied the traditional statistical techniques is very limited, and there have been more artificial neural network (ANN) suggested to this field recently. According to these ANN researches, financial distress predictions models build by artificial neural network are more feasible than traditional statistical methods. This paper, theoretically and empirically, applied the back-propagation network (BPN) to build the financial distress prediction models, and empirical results show function of BPN on crisis management mechanism toward the community financial institution in Taiwan is quite well. In addition, 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 (75.90%).
  • KEYWORDS: Early Warning System, Artificial Network, Credit Department of Fishermen, Fisheries economics, Fisheries-related Activities, Association
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  • Chuang, Ching-Ta, Hsiang-His Liu, Ming-Feng Wu. 2004. Application of Neural Network on Early Warning System by Rating for the Credit Department of Fishermen Association in Taiwan. Peer Review: No. In: Proceedings of the Twelfth Biennial Conference of the International Institute of Fisheries Economics & Trade, July 20-30, 2004, Tokyo, Japan: What are Responsible Fisheries? Compiled by Ann L. Shriver. International Institute of Fisheries Economics & Trade, Corvallis, Oregon, USA, 2004. CD ROM. ISBN 0-9763432-0-7
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