Predicting credit rating downgrades in emerging markets: A comparative analysis of artificial neural networks and traditional binary classification models

Abstract

This research compares the effectiveness of machine-learning and traditional statistical techniques in predicting annual credit rating downgrades for Thai non-financial firms listed on the Stock Exchange of Thailand during 2018–2023, using a time-ordered train-validation-test framework for predictive model evaluation. The machine-learning models are the artificial neural network (ANN), Random Forest, and XGBoost. The traditional techniques are the linear probability model, logistic regression, and probit regression. Model performance is evaluated using threshold-free PR-AUC and threshold-based metrics because downgrade events occur infrequently. The findings show that logistic regression is more useful for risk ranking, while Random Forest is more useful for downgrade screening. Under threshold-free metrics, logistic regression has the highest PR-AUC of 0.214. Under threshold-based metrics, Random Forest performs best, with a recall of 0.455 and an F1 score of 0.233, while the ANN is second-ranked. Overall, the results show that the preferred model depends on the purpose of use. Logistic regression is more suitable when the objective is to rank firms by downgrade risk, while Random Forest is more suitable when the objective is to screen firms for possible downgrade. Given the limited number of downgrade events in the test sample, the model comparisons should be interpreted as indicative rather than definitive.