Crypto adoption, legalization, and sovereign credit ratings: evidence from ridge regression

This study examines whether cryptocurrency adoption and legality affect sovereign credit ratings across 137 countries (2021–2023). Using cross-sectional data, we apply ridge regression and automated machine learning (AML) to address multicollinearity and identify key predictors. While economic freedom emerges as the strongest determinant, both crypto adoption and legality have significant, positive, and comparable effects on sovereign ratings. Our findings highlight the relevance of combining formal and informal institutions in assessing sovereign risk and showcase the utility of AML for improving model robustness in credit rating prediction.