## 1 Introduction
### Literature Review  
Predicting companies' financial condition is a widespread area of economic research. Since [PERSON] published the first study on this topic in 1932, bankruptcy prediction has become a subject of interest for various researchers and industry professionals. Bankruptcy prediction models are especially prevalent in economically advanced Western countries. Models developed in the latter half of the 20 th century are still used today. [PERSON] [9] introduced univariate analysis, becoming a pioneer in the field of bankruptcy prediction. Building on this research, [PERSON] [10] applied multivariate discriminant analysis to create a model for predicting bankruptcy. Later, in 1980, [PERSON] developed a new model based on logistic regression [11]. In 1984, [PERSON] proposed the application of a probit model for predicting company bankruptcy [12]. [PERSON]'s model, as well as [PERSON]'s logit model, were developed within the context of the US economy. Since then, numerous other models have been created across various countries worldwide. In addition to modern machine learning techniques, traditional methods such as logistic regression, multivariate discriminant analysis, and classification trees remain commonly used [13; 14; 15]. [PERSON] et al. [16] compared the performance of logistic regression and discriminant analysis models using neural networks. Their research found that the performance of neural networks was higher than the other two mentioned methods. However, the authors also noted that the performance of neural networks was not significantly higher than that of the other models. [PERSON] et al. [17] conducted a comparative study on bankruptcy prediction for restaurant firms using multivariate discriminant analysis (MDA) and logistic regression models. The study revealed that while both models were effective, logistic regression provided more accurate predictions, especially in volatile industries like hospitality. [PERSON] et al. [18] applied artificial neural networks (ANN), logistic regression, and multivariate discriminant analysis to predict the bankruptcy of companies in Pakistan. The study compared the predictive power of these techniques, highlighting the performance of ANNs over classical statistical methods. While still effective, logistic regression and discriminant analysis showed limitations in handling non-linear relationships. The first ex-ante analysis in Slovakia was published by [PERSON] [19]. The so-called CH-index model was designed specifically for Slovak agricultural enterprises based on discriminant analysis. Another well-known Slovak model is the G-index, developed using a discriminant analysis for agricultural enterprises [20]. Since then, several authors have created new models or examined the applicability of existing models within the Slovak context. [PERSON] et al. [21] explored the accuracy of various bankruptcy prediction models within the Slovak business environment. The study highlighted that traditional models like [PERSON]'s Z-score required customisation for specific regions, as macroeconomic variables such as inflation and interest rates affected the models' predictive accuracy. This research underlines the need to adapt prediction models to local economic conditions to enhance their utility [21]. [PERSON] and [PERSON] [22] conducted a comparative analysis of neural networks and classical discriminant analysis in predicting bankruptcy. The study demonstrated that neural networks offered greater accuracy than discriminant analysis. However, the researchers acknowledged the continued relevance of classical methods like discriminant analysis for simpler datasets and interpretability. In addition, several new models have been developed by Slovak researchers. The V4 financial distress prediction model was developed by [PERSON] et al. [1] based on the data on enterprises from V4 countries during the periods of 2015 and 2016 using the multiple discriminant analysis by the authors [1; 23]. [PERSON] et al.