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.
