Corporate failure prediction in crisis periods: the case of Visegrad Four large corporates

Abstract This article demonstrates that crisis conditions significantly impact the efficacy of corporate bankruptcy prediction models developed with pre-crisis data pertaining to large corporates in the Visegrad Four (V4) countries. Empirical research includes 245,974 firm-year observations and 3091 failure occurrences. Model development was accomplished using a combination of chi-squared automatic interaction detection (CHAID) decision trees and logistic regression (LR) methods, constituting a novel technique in V4-level bankruptcy prediction. Model performance was evaluated by area under the ROC curve (AUROC) analysis. Evidence from V4 large corporates indicates that the classification accuracy of the corporate failure prediction model developed in the pre-crisis period substantially declines during a crisis; thus, it was essential to create a new point-in-time model based only on crisis data. The results indicate that the model design underwent substantial alterations compared to the pre-crisis model. The current ratio is the strongest predictor; however, country and sector classifications also significantly contribute to elucidating corporate failure throughout the crisis era.