| dc.contributor | 統計系 | en_US |
| dc.creator (作者) | 李昂軒;鄭宇庭;劉揚 | zh_TW |
| dc.creator (作者) | Li, Ang-Shyung ; Cheng, Yu-Ting ; Liu, Yang | en_US |
| dc.date (日期) | 2009-09 | en_US |
| dc.date.accessioned | 2-Dec-2014 15:49:23 (UTC+8) | - |
| dc.date.available | 2-Dec-2014 15:49:23 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-Dec-2014 15:49:23 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/71783 | - |
| dc.description.abstract (摘要) | 新巴塞爾資本協定（the New Basel Capital Accord）已於2004年底定案，2007年正式開始實施。在新的協定及金管會的規定裡面，金融機構必須建立自己的評等模型，重視放款的風險。而除了上市、櫃公司會和金融機構有借貸往來之外，許多中小企業也有融資的情形。因此，希望能有效掌握貸後風險及了解影響貸後風險的一些因素即是本研究的研究重點。 本研究以資料採礦的觀點蒐集了92至94台灣中小企業基本資料及財務資料，並加上年度的經濟指標，以資料採礦的流程去建立違約及評等模型，以有效管理貸後風險。 本研究係以羅吉斯迴歸（Logistic Regression）建立違約模型，找出了影響違約機率顯著的九個變數：「企業年度營收」、「產業類別」、「現金流量為負，但淨利為正」、「最近三個月查詢家數」、「擔保授信餘額比率」、「是否動用現金卡」、非現金之流動資產比」、「借款比率」、「進出口貿易年增率」，利用評等評分系統分出九個評等等級，違約機率隨著評等等級變高而增加，而在不同的測試下，建立出的模型有不錯且穩定的表現，皆通過新巴塞爾及金管會的規範。希望此模型及評分系統在實務上可有效利用資訊掌握違約的可能性及貸後風險。 | en_US |
| dc.description.abstract (摘要) | The new Basel Capital Accord (Basel capital accord the new) has to be finalised by the end of 2004, commenced implementation in 2007.In the new Protocol and the HKMA will law, financial institutions must create your own rating model, the risk attaches importance to the lenders.Apart from the listing, cabinet companies and financial institutions have in addition, many borrowings dealings also have the financing of the medium-sized enterprises.Therefore, hope to keep track of the loan the effect of risk and learn about loan risk factors that is, the research priorities in this study. This study to the views of data mining of 92 to 94 amassed Taiwan business basic data and financial data, plus year`s economic indicators, data mining process to establish a breach of contract and assessment model, such as to effectively manage loan risk. 1 of this Research Department of fine sampling (using logistic regression Luo JI) establishment of a breach of contract model regression, a significant impact the chances for breach of the nine variable: | en_US |
| dc.format.extent | 1776708 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Journal of Data Analysis, 4(2),1-22. | en_US |
| dc.subject (關鍵詞) | 資料採礦;信用評等;違約機率;貸後風險;Data mininig;breach of the credit rating loan risk;probability | en_US |
| dc.title (題名) | 中小企業信用評等暨違約風險模型之評估 | zh_TW |
| dc.title.alternative (其他題名) | SME Credit Rating-Cum-Evaluation of Default Risk Model | en_US |
| dc.type (資料類型) | article | en |
