This paper studies the effectiveness of estimating credit rating transition matrices using sequence-based clustering on historical credit rating sequences. The data set used in this study consisted of monthly credit rating sequences from Korean companies from 1986 to 2018. The credit rating sequences were converted to sequence matrices and was clustered using PCA-guided K-means. Representative transition matrices of the resulting clusters were then generated to be used in the classification process. The proposed clustering model is evaluated under the 3 different long-term classification scenarios; 7 class credit rating prediction, credit rating transition direction (upgrade, stay, or downgrade) prediction, and default behaviour prediction. All three classification scenarios produced promising results suggesting that the representative transition matrix of the $K$ clusters better describes future credit rating behaviour than a single transition matrix.

本文研究了基于历史信用等级序列的基于序列的聚类估计信用等级转换矩阵的有效性。本研究中使用的数据集由1986年至2018年韩国公司的每月信用评级序列组成。信用评级序列转换为序列矩阵，并使用PCA指导的K均值进行聚类。然后生成所得簇的代表性转移矩阵，以用于分类过程。建议的聚类模型是在3种不同的长期分类方案下进行评估的；7类信用评级预测，信用评级过渡方向（升级，停留或降级）预测和默认行为预测。$ķ$ 集群比单个过渡矩阵更好地描述了未来的信用评级行为。