Predicting serial credit rating downgrades
Year: 2025
Type: article
Abstract: This paper examines the predictability of sequential credit rating downgrades under the through-the-cycle methodology used by credit rating agencies. While the lagging nature of ratings is well documented, we use a dataset of 28,847 firm-year observations for North American corporates to show that interim ratings assigned during downgrade sequences systematically understate credit risk relative to non-downgraded peers with the same rating. This understatement enables accurate prediction of further downgrades, with machine learning models achieving up to 75% accuracy. The findings have practical relevance for investors and risk managers seeking to anticipate formal rating actions before they occur.
Source: Global Finance Journal
Institution: The University of Western Australia
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,860
OpenAlex ID: W4416580352