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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/71775


    Title: 企業信用評等模型—以營造業為例
    Authors: 鄭宇庭;蔡紋琦;鄧家駒;林孟寬
    Contributors: 統計系
    Keywords: 資料採礦;信用評等;違約機率;Data Mining;Credit Rating;Default Probability
    Date: 2013-05
    Issue Date: 2014-12-02 15:47:54 (UTC+8)
    Abstract: 本研究目的，是以資料採礦的觀點，配合SPSS Clementine 11.0軟體所提供的資料採礦工具，將資料採礦進行的分析流程，導入企業信用評等模型的建置程序，針對內部評等法中的企業型暴險，根據新版巴塞爾資本協定與金管會的準則，建立信用評等模型。投入模型的變數，分為財務變數以及總體經濟變數。在精細抽樣比例與模型方法的比較上，1:2比例訓練出的模型在反查率(Recall)較佳且在整體正確率(Accuracy)上亦有不錯的表現；最後模型評估結果決定使用羅吉斯迴歸模型。本研究所建構出的信用評等系統分為8個評等等級，違約的機率隨評等遞增，以第8等作為違約戶的評等結果。信用評等的各項驗證，首先各等的授信戶均勻分布於8等之間，各評等的預測違約機率，亦相當接近實際違約機率，總結來說，本研究建構之模型具有一定的穩定性與預測效力，並且皆通過新巴塞資本協定與金管會的各項規範，顯示本研究之信用評等模型能夠在銀行授信流程實務中加以應用。
    The purpose of this research is to introduce the analysis procedure of data mining into the corporate credit rating model and to use the financial variable and the economic variable to create a credit evaluation model that aimed at the risk exposed in the corporations. The credit evaluation is based on the guidance set forth by the New Basel Capital Accord and the Standard of Financial Supervisory Commission. After the final evaluation of the model, the study decided to use the Logistic Regression model. The credit rating system is categorized into eight levels where the eighth level has the highest probability of being default. The resulting model formulated by this study exhibits stability and predictive capability that is within the accordance of the New Basel Accord and the Standard of Financial Supervisory Commission and has demonstrated its ability for its application in a real case in the corporations.
    Relation: Journal of Data Analysis, 8(3), 71-90
    Data Type: article
    Appears in Collections:[Department of Statistics] Periodical Articles

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