Minimalistic Logit Model as an Effective Tool for Predicting the Risk of Financial Distress in the Visegrad Group

Predicting financial distress is one of the most well-known issues in corporate finance. Investors and other stakeholders often use prediction models as relevant tools for identifying weaknesses to eliminate potential threats business partners. This paper aims present an effective logistic regression model a one-year-ahead with minimum set predictors part risk management. The motivated by various works dealing curse dimensionality phenomenon observation that increasing number logit-model does not improve prediction—on contrary. Monitoring significance improvement stepwise growth predictor used identify minimal set. Logistic cross-validation involved modelling process. proposed compared logit-based regionally or globally on same large dataset, which underlines validity robustness. logit contains only two significant achieves excellent performance metrics models. added value article lies simple application managers, investors, creditors, institutions, others reliable classification companies into healthy unhealthy company groups.