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績效與策略研究

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篇名 臺灣企業財務危機預警模型建構之研究
卷期 4:3
並列篇名 A Study on Constructing the Financial Crisis Model of Taiwanese Enterprises
作者 劉邦典梁榮輝粘元馨
頁次 15-27
關鍵字 Financial crisisdiscriminated regions modelbinary logistic regression modelNon-financial index財務危機預警模型區別分析非財務指標潳Logistic迴歸
出刊日期 200712

中文摘要

企業營運狀況會反應在財務報表上,但需等到財務報告揭露之後投資人才能
了解公司是否出現狀況。如企業刻意窗飾財務報告之資訊,則投資大眾無法直接
由財務資訊得知企業營運情形,但會計師所出具之意見仍會在一定程度上反映公
司之營運情況。
本研究結合企業之公司治理指標和會計師查核後所出具之查核意見指標建構
出企業財務危機預警模式。研究樣本取20家財務危機公司,以一比一配對方式選
取20家正常營運公司,共40家公司樣本,資料蒐集法令規章和前人學著經驗法則,
使用5項公司治理變數和7項經編碼後之會計師意見作為輸入變數。
本研究使用區別分析法和二元Logistic迴歸分析進行企業財務危機預警模式模
式比較。研究結果顯示,財務報表之會計師意見及公司治理變數對於發生財務危
機之公司確實有預測效果。於公司財務危機發生前一季,二元Logistic迴歸預測模
型之型二分類錯誤率優於區別分析法方法,最能有效地幫助投資人規避風險。而
財務危機發生前二季至前四季之型二分類錯誤率則此兩種方法之預測效果相近。
區別分析在財務危機發生前一期之財務危機鑑別率上較優於二元Logistic 迴歸分
類模式,二元Logistic 迴歸模式於財務發生前三期至前四期財務危機鑑別率上較優
於區別分析法,有助於提供未來可能發生財務危機之企業名單,亦可相互參考以
降低損失。

英文摘要

This study shows the combination of financial index and accountants auditing opinions as the predictors of the financial statements crisis models. We take 40 samples as the experiments study, which one half samples are financial crisis and the same numbers of the regular companies. Besides, we review the former studies and some experiences about the laws and regulations. So totally, we collect 5 items of regulations on corporate governance variables and 7 items of input variables.
This study adopts the discriminated regions methods and binary logistic regression model to explore the financial crisis corporations. The results show that there are some good predictors, which we adopt the corporate governance variables and accountant opinions. While there are one season time lag, the performances of the binary logistic regression model is superior than the discriminated regions model. Except the time lags is 2-4 seasons before. The best performance is to hedge the risk of investors. Their superior performance shows on different region area, e.g. the discriminated regions model is better than the binary logistic regression model at the one season lag. But the binary logistic regression model is better than the discriminated regions model at the former 2-4 seasons. The results provide the good predictors to reduce some risks and costs of companies for financial risks.

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