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台灣農漁會信用部體制改革之研究：倡議聯盟途徑之初探許玉青 Unknown Date (has links)
政府將民國九十年定為金融改革年，積極整頓問題基層金融機構。民國九十一年八月二十二日，財政部通令各縣市政府實施農漁會信用部分級管理措施，未料引發了基層農漁會與農漁民強烈抗議。為平抑農漁民團體抗爭的情緒，政府最後終於在十一月十七日當晚，宣布暫緩實施農漁會信用部分級管理措施，改革政策出現大逆轉，乃為本文的研究動機。
本研究主要目的在探討台灣農漁會信用部體制改革之政策，參考美、日、荷等先進國家之農業金融改革經驗，並以彰化縣農漁會信用部作為個案分析對象，以倡議聯盟架構（Advocacy coalition framework）途徑分析台灣農漁會信用部體制改革政策，運用德菲法（Delphi technique）進行問卷調查，期望藉助專家學者的共識獲得「解決問題」（problem-solving）的「最適方案」。
本研究以倡議聯盟架構分析農漁會信用部改革政策，發現目前尚無「政策仲裁者」之存在。其原因在於農漁會信用部改革政策當中，只有一個農業聯盟，因此並未有居間協調不同倡議聯盟與政府決策者之「政策仲裁者」出現。此外，在檢視美、日、荷等三國之農業金融體制改革經驗後，透過「德菲法」問卷調查尋求專家學者及政策利害關係人之意見，發現日本的農業金融改革較適合作為我國之借鏡。本研究並依據問卷調查結果，統整兩回合之問卷中專家學者及政策利害關係人的意見，分別從法規面、制度面及發展面等三方向，提出政府改革農漁會信用部體制之政策建議。
在法規面，除了確立主管機關一元化原則外，尚應修改農會法以符時宜。在制度面，則建議由合作金庫轉型或改制成立全國農業（金庫）銀行；建立全國農業（金庫）銀行、農漁會信用部之二級制農業金融體系；最後，建議應考慮恢復農會股金制之設計，然恢復股金制之過程繁複耗時，仍需進行深入研究方可實行。在發展面上，則鼓勵農會信用部按縣市或地區進行合併，並且強化全國農業（金庫）銀行與農漁會信用部之關係。
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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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