Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/23701
DC FieldValueLanguage
dc.contributor謝登隆zh_TW
dc.contributor張國雄zh_TW
dc.contributor.advisor徐俊明zh_TW
dc.contributor.author李基賢zh_TW
dc.contributor.authorLi, Chi-Hsienen_US
dc.contributor.other中興大學zh_TW
dc.date2011zh_TW
dc.date.accessioned2014-06-06T07:20:59Z-
dc.date.available2014-06-06T07:20:59Z-
dc.identifierU0005-1507201010125900zh_TW
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dc.identifier.urihttp://hdl.handle.net/11455/23701-
dc.description.abstractThis study employs logistic model containing financial ratios and non-financial variables to predict the debt default rate of 2,628 small- and medium-sized enterprises (SMEs). Technically, I use the forward method of stepwise regression to select variables, which results in 5 financial and 3 non-financial variables. I then use regressions consisting of these variables to estimate credit scores, and judge whether the sample companies would default by setting the critical point to be 0.25. The results show that the model has a good fitness, while the accurate rate of predicting default companies is low. More specifically, area under the curve (AUC) increases from 0.670 to 0.802 when I add non-financial variables to the model. This indicates that experts (bank credit managers) who in practice use qualitative variables to judge their customers do contribute to increase bank loan assets quality. I thus suggest that banks should consider non-financial variables in the default evaluation process.en_US
dc.description.abstract本研究利用Logistic模型,針對財務比率變數與非財務變數之專家經驗,選取2,628個中小企業樣本,評估其信用評等模型之配適能力與對違約區別能力之差異。利用逐步迴歸分析向前選取法(Forward)以選出具最適變數之Logistic模型,計算於考量非財務變數之專家經驗後AUC值之變化,以探究專家經驗對信用評等模型之影響。 本研究實證結果顯示,Forward共選取了5個財務變數與3個專家經驗之非財務變數,在0.25的臨界點(cut-off)下,整體預測準確性為93.5%、AUC為0.67;然而,納入專家經驗之Logistic模型預測準確性下降為90.6%、AUC值提高為0.802,表示專家經驗明顯提升了樣本內模型配適能力、即對違約區別之能力提高。說明了中小企業財務資料之透明度與可信度確實偏低,若能透過專家經驗之判斷,應可降低金融機構對中小企業授信之違約機率。顯示金融機構對中小企業之授信,若完全依所取得之財務資料作為授信准駁之主要參考依據,將大幅提高金融機構之授信違約風險。zh_TW
dc.description.tableofcontents第一章 緒論 ………………………...1 第一節 研究背景與動機 ............1 第二節 研究目的……...............2 第三節 研究架構……...............4 第二章 文獻探討 ………………......5 第一節 信用評等模型簡介 ..........5 第二節 國內外信用評等模型相關文獻........…7 第三章 研究方法 ………………………....…11 第一節 Logistic 模型…............…… 11 第二節 信用評等模型驗證方法.................14 第四章 實證結果 ………….…………….......23 第一節 資料來源與變數說明…………....23 第二節 實證違約的估計結果….………………..24 第五章 結論與建議 ………………..…………..28 第一節 研究結論……………………..…......28 第二節 後續研究方向與建議…….……….…...28 參考文獻 ..............................30 附 錄 ……..……….………………34zh_TW
dc.language.isoen_USzh_TW
dc.publisher財務金融系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1507201010125900en_US
dc.subjectCredit scoring modelen_US
dc.subject信用評等模型zh_TW
dc.subjectLogistic Modelen_US
dc.subjectAUCen_US
dc.subjectSMEsen_US
dc.subjectLogistic模型zh_TW
dc.subjectAUCzh_TW
dc.subject中小企業zh_TW
dc.title中小企業信用評等模型之評估—財務比率與專家經驗zh_TW
dc.titleAn Evaluation of Credit Scoring Models of Small and Medium Enterprises-Financial Ratios and Experts Experiencesen_US
dc.typeThesis and Dissertationzh_TW
item.languageiso639-1en_US-
item.fulltextno fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.openairetypeThesis and Dissertation-
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