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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/33637


    Title: 企業財務危機預警模式之研究 : 並論公司治理及裁量性交易行為之影響
    Other Titles: A study of the predictive financial distress of corporate-considering the impact of corporate governance and discretionary transaction
    Authors: 黃俊彰;Huang, Chun-chang
    Contributors: 淡江大學會計學系碩士在職專班
    黃振豊;Huang, Cheng-li
    Keywords: 財務危機;預警模式;非財務因素;公司治理;financial distress;warning model;non- financial factor;corporate governance
    Date: 2006
    Issue Date: 2010-01-11 04:22:43 (UTC+8)
    Abstract: 有關企業財務危機之研究一直是政府機關、金融業者、企業單位及投資者所關注的課題,然而,傳統上相關之研究大多偏重以財務因素構建模式,而忽略較不易量化之非財務因素,恐有因財務資料與揭露有時間誤差,而錯估企業之財務狀況,或因察覺財務因素惡化之時,該企業早已發生嚴重的財務危機。因此,建立企業財務危機預警模式時,有必要加入非財務之因素,以提早偵測企業發生財務危機之預警時間,並提高預測之正確率,故本研究預期以財務變數和非財務變數所構建出的預警模式不僅較其他只用財務變數或非財務變數所建構出的預警模式較準確之預測能力,亦可較早預測企業發生財務危機的時機。
    本研究以民國91年至93年3月底46家失敗公司及92家正常公司為研究對象,蒐集危機發生前三年之財務資料與非財務資料,並以23個非財務資料及18個財務比率為變數來構建模型。步驟為檢定變數是否呈常態分配,其次探究危機公司與正常公司二群體之差異,再使用因素分析和變數選擇分析來萃取關鍵因素,最後以Logit模式構建財務、非財務及財務與非財務共同之預警模式,並分析比較以財務或非財務為基礎,逐步考慮相關變數對預警模型之影響。
    本研究之實證結果顯示:
    1.評估各個模式危機發生前三年期間之預測準確率高低,在非財務變數及財務變數建構的模式中,均以採變數選擇分析法建構的模式較採因素分析為佳。
    2.分別以非財務變數及財務變數構建模型,採變數選擇分析法下,其預測的準確度以非財務變數具有較早的預測能力(危機發生前二年、前三年),但在接近危機發生年度,則以財務變數構建之預警模式具有較高的預測能力。
    3.以非財務變數及財務變數共同構建之模型,其預測準確度不論採因素分析法或變數選擇分析法均較分別採因素分析法或變數選擇分析法來得高。
    4.以財務變數為基礎,逐步考慮非財務相關之構面,無論採用因素分析法或變數選擇分析法均可有效降低型一錯誤(將失敗公司誤認為正常公司),且越接進危機發生年度,越多非財務構面影響預警模型。
    5.無論以財務變數為基礎,逐步加入非財務變數,或以非財務變數為基礎,逐步入加財務變數,均能有效降低型二錯誤(將正常公司誤認為失敗公司)。
    This research paper studies 46 failed companies and 92 normally performing companies from 2002 through March 2004. This paper gathered the financial and non-financial data within the three years before the crises stroke and constructed the model with 23 non-financial variables and 18 financial ratios (as variables) to verify whether the variables are normally distributed. This research paper explores the differences between the crisis group and the normal group and extracts key factors with factor analysis and variable selection analysis. Finally, this model constructed a pre-warning financial model, non-financial model and a financial/non-financial model based on Logit model.
    The major findings of this research are as follows:
    1. The models constructed with financial and non-financial variables, the models constructed with the method of variable selection analysis method yield better results.
    2. The models constructed with non-financial and financial variables with the method of variable selection analysis are able to predict ahead of the model constructed with non-financial variables (two, or three years before the crises). However, during the year of crises, the pre-warning model constructed with financial variables is more accurate.
    3. The model with financial and non-financial variables, no matter whether it adopts the method of factory analysis factor or the method of variable selection analysis, yields more accuracy than those adopt factory analysis factor or variable selection analysis.
    4. Based on financial variables and structured to conclude non-financial variables is an approach to effective reduce Type 1 Errors (to mistake failed companies for normal companies) no matter it adopts the method of factor analysis or the method of variable selection analysis. The closer it gets to the year of crises, the more non-financial factors influence the pre-warning model.
    5. Either the approach to use financial variables as a basis and to gradually incorporate non-financial variables, or the approach to use non-financial variables as a basis and to gradually incorporate financial variables, can effectively reduce Type Two Errors (to mistake normal companies for failed companies).
    Appears in Collections:[會計學系暨研究所] 學位論文

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