Sequence-based clustering applied to long-term credit risk assessment
作者：
Highlights：
• Clustering historical sequences yields reliable estimation of future credit rating.
• Sequence matrices present a means of grouping firms with similar transition behaviour.
• Credit risk assessment is made by using the representative transition matrices of the clusters.
• The proposed clustering model is evaluated under three classification scenarios.
摘要
•Clustering historical sequences yields reliable estimation of future credit rating.•Sequence matrices present a means of grouping firms with similar transition behaviour.•Credit risk assessment is made by using the representative transition matrices of the clusters.•The proposed clustering model is evaluated under three classification scenarios.
论文关键词：Credit rating,Clustering,Sequence matrix,Transition matrix,Default behaviour
论文评审过程：Received 19 February 2020, Revised 26 August 2020, Accepted 27 August 2020, Available online 2 September 2020, Version of Record 11 September 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113940
