Forecasting models based on machine learning (ML) algorithms have been shown to outperform traditional models in several applications. The lack of an easily interpretable functional form, however, is a major challenge for their adoption, especially when a knowledge of the estimated relationships...

As recent studies have begun to pay increasing attention to financial distress prediction (FDP), this study compares the performance of static, dynamic and machine learning (ML) models in predicting the financial distress of firms. Balanced and imbalanced datasets of Chinese listed firms that...

Credit risk evaluation has a relevant role to financial institutions, since lending may result in real and immediate losses. In particular, default prediction is one of the most challenging activities for managing credit risk. This study analyzes the adequacy of borrower's classification models...