由於重大金融危機的發生，市場參與者開始重視伴隨著高獲利而來的高風險，投資標的逐漸轉向風險與報酬較穩定之主權債券，使得作為主權債券償債能力衡量指標的國家主權信用評等相關訊息之需求與重要性與日俱增。以往國家主權信用評等之相關研究主要著重於探討影響評等之因素以及評等等級對經濟市場之影響，對於建立分類模式之研究較少，因此本文將運用機器學習技術建立二階段多類別分類模式以克服傳統統計上之諸多限制。第一階段以逐步迴歸法及約略集合理論進行變數篩選，爾後運用最小平方法支持向量機、約略集合理論、倒傳遞類神經網路與決策樹C5.0做為第二階段之分類方法，並比較分類模式之分類績效。根據研究結果顯示，整合約略集合理論與最小平方法支持向量機分類模式之分類績效最佳，另外，在評等等級BBB+、BBB與BBB-的樣本中存在著15%被高估的機率，被高估的樣本將有承擔三倍違約率的可能性，市場參與者進行投資時須特別注意。

There have been happened many serious international financial crises, the high risk that has already been taken seriously by market participants is accompanied by the high profit, and the investment targets change into sovereign bonds. The sovereign credit rating is a indicator of the debt-paying ability about sovereign bonds publishers, and the demand of information about sovereign credit rating have become more important. Past studies about sovereign credit rating focused on variables that affect the sovereign credit rating, and how sovereign credit rating affect markets. However, studies focused on classification models are not many. Consequently, we will apply machine learning techniques to build a two-step multiclass classification models. In the first step, we use stepwise regression and rough set theory to select variables, and apply least squares support vector machine (LS-SVM), rough set theory (RST), back-propagation neural network (BPN) and C5.0 for classification in the second step, and the result indicates that the classification performance of RST+LS-SVM is the best.

中文摘要　．．．．．．．．．．．．．．．．．．．．． iii
英文摘要　．．．．．．．．．．．．．．．．．．．．． iv
誌謝辭　　．．．．．．．．．．．．．．．．．．．．． v
內容目錄　．．．．．．．．．．．．．．．．．．．．． vii
表目錄　　．．．．．．．．．．．．．．．．．．．．． ix
圖目錄　　．．．．．．．．．．．．．．．．．．．．． x
第一章　　緒論．．．．．．．．．．．．．．．．．．． 1
　　第一節　　研究背景．．．．．．．．．．．．．．． 1
　　第二節　　研究動機．．．．．．．．．．．．．．． 3
　　第三節　　研究目的．．．．．．．．．．．．．．． 5
　　第四節　　研究流程．．．．．．．．．．．．．．． 6
　　第五節　　論文架構．．．．．．．．．．．．．．． 7
第二章　　文獻探討．．．．．．．．．．．．．．．．． 8
　　第一節　　國家主權信用評等．．．．．．．．．．． 8
　　第二節　　最小平方法支持向量機．．．．．．．．． 13
　　第三節　　約略集合理論．．．．．．．．．．．．． 16
　　第四節　　類神經網路．．．．．．．．．．．．．． 19
　　第五節　　決策樹．．．．．．．．．．．．．．．． 22
第三章　　研究方法．．．．．．．．．．．．．．．．． 24
　　第一節　　最小平方法支持向量機．．．．．．．．． 24
　　第二節　　約略集合理論．．．．．．．．．．．．． 32
　　第三節　　倒傳遞類神經網路．．．．．．．．．．． 36
　　第四節　　決策樹．．．．．．．．．．．．．．．． 39
第四章　　研究設計．．．．．．．．．．．．．．．．． 42
　　第一節　　研究架構．．．．．．．．．．．．．．． 42
　　第二節　　資料來源．．．．．．．．．．．．．．． 45
第五章　　實證結果．．．．．．．．．．．．．．．．． 56
　　第一節　　第一階段：變數篩選．．．．．．．．．． 56
　　第二節　　第二階段：建立分類預測模式．．．．．． 58
　　第三節　　分類模式比較．．．．．．．．．．．．． 64
第六章　　研究結與建議．．．．．．．．．．．．．．． 68
　　第一節　　研究結論．．．．．．．．．．．．．．． 68
　　第二節　　研究限制與建議．．．．．．．．．．．． 69
參考文獻　．．．．．．．．．．．．．．．．．．．．． 71
附錄A　　．．．．．．．．．．．．．．．．．．．．． 81
附錄B　　．．．．．．．．．．．．．．．．．．．．． 86

一、中文部分

何秉劼(2012)，運用支援向量機於企業財務危機之研究，中國文化大學會計學系未出版之碩士論文。

呂理邦(2012)，捐款人流失預測之決策系統，東華大學企業管理學系未出版之博士論文。

李宜致(2008)，資料探勘手術後減重效果分類模式之建構，輔仁大學商學研究所未出版之博士論文。

林文修(2000)，演化式類神經網路為基底的企業危機診斷模型︰智慧資本之應用，中央大學資訊管理研究所未出版之博士論文。

林俊宏(2006)，傳記式資料非線性甄選模式之建構-運用類神經網路並以櫃檯行員為例，中央大學人力資源管理研究所未出版之博士論文。

林建宏(2008)，利用LS-SVM 抑制NLOS 誤差之超寬頻TOA 無線定位，中山大學電機工程學系研究所未出版之碩士論文。

林春霞(2012)，資料探勘證劵業投資風險屬性分類模式之應用－以國內某證券公司為例，輔仁大學企業管理學系管理學碩士在職專班未出版之碩士論文。

林恩汝(2007)，應用二階段分類法提升Least square-support vector machine(LS-SVM)技術之分類正確率，勤益科技大學工業工程與管理系未出版之碩士論文。

林琦珍(2011)，舞弊偵測透過舞弊三角風險因子及資料探勘工具，中正大學會計與資訊科技研究所未出版之博士論文。

施人英，陳文華，吳壽山(2007)，探討支持向量機器在發行人信用評等分類模式之應用，資訊管理學報，14(3)，155-178。

高淑珍(2004)，應用資料探勘於顧客回應模式之研究¬－以國內A壽險公司為例，成功大學企業管理學系博士班未出版之博士論文。

陳瑞陽(2006)，模糊客訴問題的原因分析及回饋系統，清華大學工業工程與工程管理學系博士班未出版之博士論文。

陳承昌，史天元(2007)，粗糙集方法應用於水稻田辨識之研究，航測及遙測學刊，12(2)，121-131。

郭秋榮(2009)，全球金融風暴之成因、對我國影響及因應對策之探討，經濟研究，9，59-89。

黃承龍，李德勝，彭定國，施政男(2004)，運用約略集合理論建立電磁干擾診斷系統，管理與系統，11(3)，367-385。

黃雅涵(2010)，資料探勘投資風險屬性分類模式－以國內某銀行財富管理業務為例，輔仁大學管理學研究所未出版之碩士論文。

曾立凱(2012)，身心障礙者電腦輔具選用決策樹，臺灣科技大學工業管理系未出版之博士論文。

葉宗翰(2010)，運用支援向量機於按成本設計(DTC)預測系統之研究－以飛機結構系統研發為例，國防大學中正理工學院國防科學研究所未出版之博士論文。

葉振山，鄭景俗(2005)，強化粗糙集應用於闌尾炎之分類，醫療資訊雜誌，14(2)，1-16。

溫坤禮，永井正武，張廷政，溫惠筑(2008)，粗糙集入門級應用，台北，五南出版股份有限公司。

賴建成(2012)，小波轉換結合類神經網路於股價預測及價格發現之研究－以香港及中國指數現貨與期貨對台港兩地掛牌ETF為例，高雄第一科技大學財務金融研究所未出版之博士論文。

二、英文部分

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Borensztein, E., Cowan, K., & Valenzuela, P. (2006). The sovereign ceiling lite and bank credit ratings in emerging markets economies. Inter-American Development Bank Working pa-per.

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Borio, C., & Packer, F. (2004). Assessing new perspectives on country risk. BIS Quarterly Review, December, 47-65.

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Chiang, T. C., Jeon, B. N., & Li, H. (2007). Dynamic correlation analysis of financial contagion: Evidence from Asian markets. Journal of International Money and Finance, 26(7), 1206-122 8.

Christopher, R., Kim, S. J., & Wu, E. (2012). Do sovereign credit ratings influence regional stock and bond market interdependencies in emerging countries? Journal of International Financial Markets, Institutions & Money, 22(4), 1070-1089.

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