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金融預警、合併監理與分級管理制度之研究 / A Study on Early Warning System, Unified Financial Supervision, and Classified Regulatory Principle.鄭璟紘, Cheng, Ching Hung Unknown Date (has links)
本研究分析我國49家本國銀行、55家信用合作社、287家農會信用部及27家漁會信用部等四類金融機構之經營現況，並參照各國金融預警制度運作方式，選取適合的財務比率，運用SAS統計軟體及Z-score、Logistic等模型，分別找出造成各類金融機構經營失敗之顯著相關財務比率，評估各類金融機構之經營效率、失敗機率與模型之正確區別率，以建立預測金融機構失敗機率之預警模型。研究之樣本資料分別為：本國銀行49家、2001年第2季~2003年底共計11季25項財務比率，信用合作社55家、1998年底~2003年底共計21季26項財務比率，農會信用部287家1998年底~2003年底共計21季25項財務比率，漁會信用部27家1998年底~2003年底共計21季25項財務比率。
本研究之結論為：
一、彙整Z-Score模型對各類金融機構具有顯著性之財務變數，本國銀行有6項、信用合作社有7項、農會信用部有6項，漁會信用部有4項。
二、彙整Logistic模型對各類金融機構具有顯著性之財務變數，本國銀行、信用合作社各有6項，農會信用部有5項，漁會信用部有4項。
三、金融預警模型中，Logistic模型較Z-Score模型有較高的正確區別率。 / This research analyzes 49 domestic banks, 55 credit cooperative unions, 287 credit department of farmer associations and 27 credit department of fisherman associations above four kind of financial institution´s management situation, and refers the operation ways of various countries financial early warning system, selects suitable financial ratios , utilizes SAS statistics software and Z-score, Logistic models, it identifies the root cause of bankruptcy thus reveals finance of ratio the correlation, appraises management efficiency, the defeat probability each kind of financial institution if the correct difference rate. It appraises each kind of financial institution´s management efficiency, defeats probability and correct difference rate. It establishes early warning model that forecasts financial institutions failure rate. The research model and period: used 49 domestic banks from 2001 in 2nd season to the end of 2003 total 11 seasons and 25 items of finance ratio、55 credit cooperative associations from the end of 1998 to the end of 2003 total 21 seasons and 26 items of finance ratio、287 credit department of farmer associations and 27 credit department of fisherman associations from the end of 1998 to the end of 2003 total 21 seasons which used respectively 25 items of finance ratio.
The conclusion of this research are:
Firstly, it collects the entire Z-Score model to have significant financial indicator to each kind of financial institution, the domestic banks have 6 items, the credit cooperative associations have 7 items, the credit department of farmer associations have 6 items, and the credit department of fisherman associations have 4 items.
Secondly, it collects the entire Logistic model to have significant financial indicator to each kind of financial institution, the domestic banks and the credit cooperative associations have 6 items respectively, the credit department of farmer associations have 5 items, and the credit department of fisherman associations have 4 items.
Thirdly, in the financial early warning model, when comparing Z-Score with Logistic model , the latter appears to have a higher correct difference rate.
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