企業財務預警模型之研究; Research on the Prediction Model of Corporate Financial Distress

NCUIR > School of Management at National Central University > Executive Master of Finance Management > Electronic Thesis & Dissertation > Item 987654321/12298

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/12298

Title:	企業財務預警模型之研究;Research on the Prediction Model of Corporate Financial Distress
Authors:	吳美玲;Mei-Ling Wu
Contributors:	財務金融學系碩士在職專班
Keywords:	財務危機;羅吉斯;Logit;Financial Distress
Date:	2005-06-16
Issue Date:	2009-09-22 14:44:32 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	財務預警模型之發展由來已久，其目的就是幫助銀行或投資人能及早發現問題公司，而國內地雷股及公司弊案如博達、衛道、皇統、訊碟等事件接二連三爆發，雖然如類神經或KMV等較複雜預警模型近年來頗受廣泛研究，但傳統之財務預警模型如「區別分析模型」與「Logit迴歸模型」至今日是否仍然適用是本研究關心的問題，以協助投資大眾從簡易財報數字及早發現危機公司。本研究以民國七十九年至九十三年國內發生危機之上市櫃公司為例，除了實證原始Altman Z-Score模型之辨識率，另以原始之Altman Z-Score區別分析模型中認為有用之五個財務變數，重新迴歸計算新的區別模型參數，亦針對財務上常用之五大類財務比率及市場因子，以統計方式在五大類中各挑選一個最能夠預測企業體質良窳的指標，據以建構實務與理論上常用的「區別分析模型」、「Logit迴歸模型」等兩種財務危機預警模型，並將兩種財務預警模型比較其辨識率，得到以下之結果: 1.單變量變異數分析法之結果為，在五大類財務比率指標中，前10大顯著差異財務比率依序為「淨值/總資產％」、「負債比率％」、「常續性EPS（元）」、「現金流量比率％」、「稅前純益?實收資本」、「每股稅前淨利（元）」、「稅後淨利率％」、「速動比率％」、「資產報酬率(稅後息前折舊前)％」與「稅前淨利率％」。 2.「區別分析模型」在財務危機發生前一年、前二年、前三年預測正確率分別為88.75%、80.00%、73.75%；「Logit迴歸模型」在財務危機發生前一年、前二年、前三年預測正確率分別為90.00%、72.50%、67.50%，在危機發生前一年以Logit迴歸模型預測正確率較佳，但在危機發生前二年與前三年以區別分析模型預測正確率較佳。 Financial Crisis Warning Models have been developed for a long time and it main purpose is to find out the financial problems within companies in advance for bankers or investors. Though some complicated warning models, such as Artificial Neural Net Work Model or KMV Model have been investigated recently, in order to assist investors aware the finial crisis in advance by using the brief financial report, this study focuses on whether some traditional financial crisis warning models, such as The Discriminate Model and Logistic Regression Model are still available presently. Companies with the financial crisis which listed on TSE and OTC between 1990 and 2004 in Taiwan were targets of this study. This study investigates not only the identification ratio of original Altman Z-Score model, but re-regress the parameters of the five financial variables within the original Altman Z-Score models. In addition, this study focuses on the market factors and financial ratios in five main divisions that have been used frequently. Furthermore, the statistics methods were used to find out the most dependable indication within the six main divisions which predicts the financial systems of the enterprise and then to build up the two popular financial crisis warning models that has been used theoretically and practically (i.e., the Discriminate Model and the Logistic Regression Model). This study compares the identification ration of these two warning models and the findings reveal that: 1.The results of T-test: It can be understood that within the six main divisions the net worth/total assets and total liability/ total assets ratio are the most important variables of the indication of company paying ability followed by the variables of EPS and cash flow ratio which means the distinguishable differences between companies with and without financial crisis is the degree of paying and earning ability. The findings coincide with people’s intuition to the companies with financial crisis. Moreover, the cash flow ratio is not only a distinguishable variable which is used to analyze the asset of the companies, but it also coincides with the present scholars’ concept of cast flow. 2.Starting from one to three years prior to the financial crisis, the justified predication rate of the Discriminate Model in order is 88.75%, 80.00%, and 73.75%. On the other hand, the justified predication rate of the Logistic Regression Model in order is 90.00%, 72.50% and 67.50%. Based on these findings, it suggests that one year before the financial crisis, the Logistic Regression Model provides a more correct predication while the Discriminate Model provides a better predication two and three years before the financial crisis.
Appears in Collections:	[Executive Master of Finance Management] Electronic Thesis & Dissertation

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