## 4 Discussion
For example, while the sensitivity of certain models, such as the Zmijevski model, dropped significantly during the pandemic, recalibration substantially enhanced their predictive capabilities. This observation is consistent with the findings of [PERSON] et al. [43], who noted similar performance improvements following model adjustments in the Indonesian banking sector. Despite its contributions, this study has several limitations. First, its focus on the automotive sector within Slovakia makes it country- and sector-specific, which may limit the generalizability of its findings to other industries or regions with different economic structures. Nevertheless, this study holds significant value also for an international audience as it offers a detailed methodology for adapting financial prediction models to extraordinary economic disruptions, such as the COVID-19 pandemic. By demonstrating the process of coefficient re-estimation and its impact on model performance, the study provides a replicable framework that can be applied to diverse contexts, making it a valuable reference for researchers and practitioners. Second, the country-specific condition for distinguishing the companies in good financial conditions and in crisis was used in the study. Indeed, the findings most directly apply to Slovakia or countries with similar legislative frameworks and economic structures. Nonetheless, the methodological approach and analytical procedures employed in the study remain robust and could be adapted to other contexts with appropriate adjustments to the distress criteria. We must admit the potential heterogeneity introduced by using models based on different definitions of financial distress. However, some studies used a similar approach. For instance, [PERSON] and [PERSON] [57] applied models developed in the US to Japanese, Korean, and Chinese companies, adapting the models to local financial environments while recognising differences in bankruptcy definitions. [PERSON] et al. [58] conducted research on global bankruptcy prediction models and verified their applicability across different regions, acknowledging the challenges of differing legal and economic contexts. Despite the mentioned potential heterogeneity of the results caused by applying country-specific conditions for companies in crisis, the study results have the potential for future comparative research. By examining financial distress within a specific legislative context, our study contributes to the broader understanding of how local economic and legal conditions influence the prediction of the financial health of the companies. This can inspire future research to explore cross-country comparisons or adaptations of the model to other jurisdictions, thus contributing to the global body of knowledge on financial distress prediction. Then, the study relies exclusively on financial data, omitting potentially significant non-financial variables such as management decisions or macroeconomic policies, which could further refine the models. However, our approach assumes that any changed macroeconomic conditions would be reflected in a change in the companies' financial indicators. The primary objective was to maintain the same variables in the models established by previous authors, re-estimating only their parameters. This approach was crucial for comparing the newly estimated coefficients with the original ones and deriving the economic interpretation of any identified changes. Introducing new variables would lead to entirely different models, making it impossible to compare them with the original models regarding the economic implications of the coefficient changes. Then, some of the models used in the study were created by their authors as sector-specific, thus effectively predicting the financial state of the companies in a particular sector of economic activity. Some of these models (Karas-Srbova, IN 05, Altman) did not demonstrate high prediction accuracy even in the pre-crisis period and, therefore, were not selected for further analysis.