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  • 學位論文

公司財務危機預警模型之研究-以美國商業銀行為例

A Study on prediction model of financial crisis firms- Taking Federal Commercial Bank for instance

指導教授 : 張百棧

摘要


近年來財務危機頻傳,美國次級房貸危機間接影響至全球,造成許多銀行倒閉,企業大規模裁員,對消費者更是一大打擊,有鑑於此,財務危機預測研究,一直是國內外學者所關心的課題。近年來許多學者運用人工智慧方法建構危機預警系統,如倒傳遞類神經網路 (Back-Propagation Neural Network,BPN)、支持向量機 (Support Vector Machine, SVM) 等。這些方法均學習能力佳,能夠處理非線性問題,但在運用上皆有不同的限制,也各有其優缺點,目前亦廣泛的應用於資料分析。 本研究將試以倒傳遞類神經法、支持向量機,再結合K-means分群,分析銀行公開財務報表資料,以建構出財務預警系統,並與傳統統計的Bayes分類法做出綜合比較。本研究資料來源為美國芝加哥商業銀行資料車,本研究將樣本區分為訓練與測試,將1987至1992之樣本做為訓練樣本,1992至2008為測試樣本,以建立模型。 結果顯示,K-means整合傳遞類神經網路預測能力最佳,可提供投資者參考,以預防企業發生財務危機。

並列摘要


In recent years, there were many cases for bank failure and financial crisis, the US Subprime Lending makes many banks into bankruptcy and enterprises reduce the staff. Therefore, describing to have an early warning system has already become a hot issue. Recently, a lot of research used artificial intelligence methods to build financial early warning system for failure prediction, ex: Back-Propagation Neural Network and Support Vector Machine. This objective of this study is to use financial variables with a proposed novel model to integrate K-means with Back-Propagation Neural Network (BPN) and Support Vector Machines (SVM) technique to increase the accuracy of the prediction of bank failure. And then compare with Bayes classifier. The research data are provided by Federal Reserve Bank of Chicago. The data set is arbitrarily split into two subsets: about five sixth of the data is used for a training set (1987 to 1992) and one sixth for a validation set (from 1992 to 2008). Comparing to other methods, the proposed K-means BPN model outperforms other forecasting methods, it not only can increase the accuracy of the prediction of bank failure, but also provides great information for business owners and investors.

參考文獻


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