## 3 Results
### Re-Estimation of Models for Pandemic and Post-Pandemic Period

Table 11: Prediction performance of selected five models in pandemic and post-pandemic period. Compared to the period before the pandemic, we can say that the prediction accuracy of some of the selected high-performance models dropped significantly (the Polish model of [PERSON]) or stayed at the same average level (the other models). Regarding the sensitivity, the models' performance dropped significantly (Taffler-Tisshaw benchmark model, [PERSON] Slovak model, and [PERSON] Polish model) or even rose significantly during the pandemic and post-pandemic period (IN01 Czech model and Virag-Hajdu Hungarian model). Comparing the pandemic years 2020 and 2021 with the post-pandemic year 2022, we can say that with some exceptions, the models showed lowered accuracy and sensitivity levels during the pandemic. Table 13 presents the newly estimated coefficients of the selected five models valid during the pandemic and post-pandemic period. The table contains only the variables included in at least one model; the other variables were omitted. The coefficients had to change during and after the pandemic because financial indicators and risk factors took on new meaning in the context of unprecedented economic conditions. These changes reflect the models' efforts to better capture current realities and predict financial distress risk in a highly uncertain environment. We also listed the original coefficients for comparison with their changes during the pandemic year 2021 and the post-pandemic year 2022. During and after the COVID-19 pandemic, the coefficients of prediction models may have changed for several reasons. There are two primary factors of these changes. (i) Changes in the economic environment: the pandemic caused a global economic crisis, a sharp decrease in demand in some sectors and increased uncertainty, which led to a higher risk of financial distress for many companies and thus affected the weight of individual factors in the prediction models. (ii) Increased corporate indebtedness: many companies had to take out loans to survive the period of lower income. Table 14 focuses on the changes in the newly estimated coefficient signs from positive to negative or vice versa compared to those in the original models. The last two columns of the table offer a mathematical and possible economic interpretation of the altered coefficient signs regarding its impact on the value of the discriminant score, thus on the classification of the company in the group of healthy and in-crisis. However, all these interpretations hold true only if the other variables in the model remain constant. It should be noted that these are rather theoretical situations, as financial ratios often contain similar items from the financial statements; thus, the assumption of keeping them constant when one variable changes is purely theoretical. Nevertheless, the results underscore the impact of the pandemic period on the significance and influence of individual financial ratios on the value of the company's discriminant score. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **Year** & **[PERSON]–[PERSON]** & **[PERSON]–Adamko** & **IN01** & **[PERSON]** & **Virag–Hajdu** \\ \hline \multirow{4}{*}{**Accuracy in \%**} & **2020** & 82.4 & 89.9 & 54.7 & 82.5 & 45.0 \\ \cline{2-6}  & **2021** & 78.1 & 89.5 & 60.0 & 32.5 & 45.0 \\ \cline{2-6}  & **2022** & 82.9 & 90.0 & 68.2 & 30.0 & 48.8 \\ \cline{2-6}  & **avg** & 81.1 & 89.8 & 61.0 & 48.3 & 46.3 \\ \hline \multirow{4}{*}{**Sensitivity in \%**} & **2020** & 0.0 & 70.0 & 77.8 & 80.5 & 69.2 \\ \cline{2-6}  & **2021** & 20.0 & 66.7 & 90.0 & 0.0 & 38.5 \\ \cline{1-1} \cline{2-6}  & **2022** & 10.0 & 54.6 & 90.5 & 0.0 & 72.7 \\ \cline{1-1} \cline{2-6}  & **avg** & 10.0 & 63.7 & 86.1 & 26.8 & 60.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Summary of evaluation metrics for selected five models and their average values in pandemic and post-pandemic period.