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

Michal Pavlicko and Jaroslav Mazanec
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Michal Pavlicko: Department of Quantitative Methods and Economic Informatics, The Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia
Jaroslav Mazanec: Department of Quantitative Methods and Economic Informatics, The Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia

Mathematics, 2022, vol. 10, issue 8, 1-22

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

Keywords: financial distress; logit model; prediction model; Visegrad group; curse of dimensionality; risk management (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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