企業財務危機診斷模型-粗略集合論與支援向量機之應用

Analysis of Enterprise Financial Crisis by Rough Set Theory and Support Vector Machines

李飛毅(Fei-Yi Lee)

指導教授 ： 白炳豐

國立暨南國際大學/管理學院/資訊管理學系/碩士(2010年)

摘要

從2007年末美國的次貸風暴引發了第一波金融海嘯到現在，金融海嘯對各國經濟產生了明顯衝擊，能夠準確分析及預測財務危機顯的更為重要，本研究透過投資者和企業經營者兩個面向進行研究，結合救濟特徵擷取方法和支援向量機提供給予投資者一個財務危機預警模型再透過粗略集合方法找出規則提供給企業經營者一個維持及改善企業狀況之方向，結果顯示救濟特徵擷取方法結合支援向量機能夠提供較佳的準確率作為財務危機預警模型，粗略集合論能夠正確的提供規則給予企業經營者一個經營方針。

關鍵字

財務危機預警模型 ； 救濟特徵擷取演算法 ； 粗略集合論 ； 支援向量機

並列摘要

From the end of 2007 the U.S. sub-prime mortgage crisis triggered a first wave of financial tsunami until now; this had a significant impact on financial economy. Correctly analyzing and forecasting the financial crisis is more important. The investigation provides two instructions for researchers and executives. The investors adopted RELIEF method and support vector machine as an early warning system. Sequentially, rough set theory is used to identify informative information and decision rules for managers to improve the financial situations. The results showed that hybrid RELIEF and Support Vector Machine perform a satisfactory job in detecting financial crisis; the decision rules derive from rough set theory can provide instruction for managers to change their management policy.

並列關鍵字

financial crisis early warning models ； RELIEF ； rough set theory ； support vector machine

參考文獻

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