完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | 莊欣霖 | en_US |
dc.contributor.author | Hsin-Lin Chuang | en_US |
dc.contributor.author | 唐麗英 | en_US |
dc.contributor.author | Lee-Ing Tong | en_US |
dc.date.accessioned | 2014-12-12T02:27:06Z | - |
dc.date.available | 2014-12-12T02:27:06Z | - |
dc.date.issued | 2001 | en_US |
dc.identifier.uri | http://140.113.39.130/cdrfb3/record/nctu/#NT900031046 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/68166 | - |
dc.description.abstract | 授信評等是銀行衡量借款者償還能力的依據,然而在現今經濟不景氣與逾放比例過高的壓力下,越來越多的銀行開始檢討其現有的評等方法,以改善成更有效之評等制度。能夠代表一企業信用狀況之因素不外乎是其品格、能力、資本、擔保品及業務狀況,但要如何有效地審視這些因素以判斷其履約能力,是所有放款公司最想要知道的。目前台灣許多放款公司或銀行仍是以人為判斷的方式做企業放款之授信評等,但要從多個影響企業信用之因素中正確的對企業做出信用評等,並非易事,且這些人為之經驗也很難傳承。針對此問題,現有之中、外文獻雖發展了許多信用評等模式,但這些模式均只能針對企業之信用作二分之分類,這使放款公司或銀行在授信時彈性不大,因此應用價值有限。本研究為解決以上問題,提出了構建企業多等級信用評等之流程,此流程分成三階段進行:(1)變數之選擇與資料的收集、(2)利用羅吉斯迴歸構建放款風險評估模式、(3)正常與違約資料多等級劃分及企業信用評等模式之構建。其中,放款風險提供了正常與違約資料之判斷,企業信用評等模式則可將申請者作多等級的評等分類。本研究最後以台灣某放款公司為例,利用本研究流程來構建其借款者之信用評等模式,結果發現正常與違約資料兩類別之正確分類率及違約多等級區分之正確率皆相當高,顯示本研究之流程不但能區分出正常與違約資料,更能幫助放款公司找出使其損失較大之嚴重違約申請者,可有效地降低放款之風險。 | zh_TW |
dc.description.abstract | Credit rating is used for bank to investigate the repayment ability of borrower. Because of economics depression and higher overdue loan rate, more and more bank start to improve their present credit rating model to be a batter one. Character, Capacity, Capital, Collateral, and Condition of business are the factors that impact the borrower’s credit. And how to use these factors to built a useful credit rating model is just the importance that bank want to know. Based on the above, we should use logistic regression to build a clear procedure for bank to construct a batter credit rating model. The procedure has three stages:(1)variables selection and data collection, (2)building risk assessment model for bank loan (binary discrimination),(3)constructing multi-level credit rating model. Among that, risk assessment model is used to classify borrowers into regular group and default group and credit rating model is used for bank as a multi-level discrimination to investigate borrowers’ credit more flexibly. Finally a finance company is chosen as a example in the study. We use above procedure to construct this company’s credit rating model. Our finding shows that not only risk assessment model for binary classification has high correct rate but using multi-level credit rating model we could find out the default borrowers which have shortest payment life(under 3 months). According to above, it shows the model constructing by this procedure is useful for the finance company. Another bank and finance company also can follow this procedure to build their own credit rating model. | en_US |
dc.language.iso | zh_TW | en_US |
dc.subject | 信用評等 | zh_TW |
dc.subject | 風險評估 | zh_TW |
dc.subject | 羅吉斯迴歸 | zh_TW |
dc.subject | 放款 | zh_TW |
dc.subject | credit rating | en_US |
dc.subject | risk assessment | en_US |
dc.subject | logistic regression | en_US |
dc.subject | bank loan | en_US |
dc.title | 應用羅吉斯迴歸構建銀行放款信用評等模式 | zh_TW |
dc.title | Constructing Credit Rating Model for Bank Loan Using Logistic Regression | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | 工業工程與管理學系 | zh_TW |
顯示於類別: | 畢業論文 |