Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323470 
Year of Publication: 
2024
Citation: 
[Journal:] Journal of Business Economics [ISSN:] 1861-8928 [Volume:] 95 [Issue:] 4 [Publisher:] Springer Berlin Heidelberg [Place:] Berlin/Heidelberg [Year:] 2024 [Pages:] 617-652
Publisher: 
Springer Berlin Heidelberg, Berlin/Heidelberg
Abstract: 
Abstract Several studies have demonstrated the high prediction accuracy of clustered credit risk modeling. In clustered modeling, borrowers are segmented based on their similarities through cluster analysis, and a separate predictive model is developed for each cluster, resulting in increased predictive accuracy. Unambiguously, its effectiveness depends on the quality of the segmentation, which in turn depends primarily on the choice of variables used in the cluster analysis. However, appropriate variable selection for clustering is a major challenge, particularly for high-dimensional data. In the present study, we propose a machine learning-based variable selection method based on theoretical and regulatory considerations. Formally, the most influential risk drivers from a best-in-class machine learning model are identified using Shapley values and employed as clustering variables. Thus, the information of the explanatory variables crucial for the prediction of the dependent variable is already processed during data segmentation, making each individual predictive model more effective. Through a comparative analysis using two real-world credit default datasets, we show that our proposed approach to clustered modeling leads to the highest prediction accuracy among various clustering models.
Subjects: 
Credit risk
Forecasting
Clustering
Machine learning
Global credit data
Persistent Identifier of the first edition: 
Additional Information: 
C38;C45;C52;C53;G21
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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