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

台灣漁會信用部金融預警系統之研究－類神經網路模式之應用

莊慶達(Ching-Ta Chuang) ； 劉祥熹(Hsiang-Hsi Liu) ； 吳明峰(Ming-Fong Wu)

《農業經濟叢刊》 13卷1期 (2007/12) Pp. 125-145

https://doi.org/10.6196/TAER.2007.13.1.5

摘要

本文探討倒傳遞類神經網路(BPN)應用於基層金融財務危機之預測，本研究比較各預警模式之估計樣本預測能力的實證結果顯示，其正確預測能力依序為原始財務變數倒傳遞網路模式正確預測率(81.1%)為最佳模式，其他依序為因素分析後倒傳遞網路模式(77.85%)及Ordered Logit模式(75.9%)。事實上，漁會信用部這類基層金融經營若出現問題，將會引起連鎖損害與信用危機問題，故基層金融營運更需要有效的預警系統，基此，本文建議之類神經網路可提供基層金融單位及早發現問題，並採取相關的預防或管理措施。

關鍵字

早期預警系統 ； 漁會信用部 ； 類神經網路

並列摘要

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.

並列關鍵字

Early Warning System ； Credit Department of Fishermen Association ； Artificial Neural Network

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