## 4 Discussion  
This study highlights the necessity of updating financial distress prediction models to address pandemic-induced economic disruptions. It also contributes to the broader understanding of how traditional models can be adapted for unprecedented circumstances. The findings highlight the necessity of re-estimating the models, maintaining their original variables chosen by their authors, to enhance their applicability in diverse economic conditions. The findings of this study align with previous research that highlights the limitations of traditional financial distress prediction models during the COVID-19 pandemic and underscores the necessity of adapting these models to incorporate pandemic-specific economic factors. Several studies cited in the Literature Review reached similar conclusions, recommending adjustments to model coefficients or the inclusion of external variables to enhance predictive performance. For instance, [PERSON] and [PERSON] [31] evaluated the Altman Z-score model and found that significant adjustments were required to maintain its predictive accuracy during the pandemic. Their findings emphasised that financial variables alone were insufficient for reliable predictions in the volatile economic environment caused by COVID-19 and highlighted the importance of including external factors such as government policies and market shocks. Similarly, [PERSON] [32] found that traditional models like the Springate and Altman Z-scores underperformed when applied to service companies, as these models failed to account for critical non-financial variables that emerged during the pandemic. Our study corroborates these conclusions, demonstrating that the original coefficients of the selected models developed by V4 authors were inadequate in the pandemic context and re-estimating the coefficients significantly improved model performance. However, the authors recommended model adjustments by incorporating macroeconomic indicators to enhance the prediction performance in predicting bankruptcies during this period. [PERSON] and [PERSON] [34] also advocated integrating external factors, such as government interventions and market conditions, into prediction models. This approach aligns with \begin{table}
\begin{tabular}{c c c} \hline
**Period** & **2021** & **2022** \\ \hline Formula for discriminant score & \(DS_{2021}=6.320X5-0.652X18\ -1.304\) & \(DS_{2022}=5.260X5-0.493X18\ -1.096\) \\ \hline Safe zone & \(DS_{2021}>0\) & \(DS_{2022}>0\) \\ \hline Red zone & \(DS_{2021}<0\) & \(DS_{2022}<0\) \\ \hline Model’s accuracy & 91.4\% & 91.4\% \\ \hline Model’s sensitivity & 92.3\% & 81.8\% \\ \hline \end{tabular}
\end{table} 
Table 17: New models for pandemic and post-pandemic period. the recommendations of [PERSON] and [PERSON] [33], who found that incorporating real-time data and adjusting existing models improved predictions for distressed insurance companies in Oman during the pandemic period. Similarly, the research by [PERSON] et al. [43] analysed Indonesian construction companies and highlighted the necessity of model adjustments to account for government policies and market fluctuations during the pandemic. [PERSON] et al. [42] conducted a similar assessment of traditional models like Altman, Springate, and Fulmer in Bulgarian agricultural enterprises. They observed that these models required sector-specific adjustments to improve their predictive accuracy during the pandemic. These findings resonate with our study, where the models tailored for pre-pandemic conditions failed to deliver reliable predictions until their coefficients were recalibrated. The coefficient re-estimation was pivotal in restoring the accuracy of the selected models. However, our study did not explicitly include external factors; the re-estimation of coefficients implicitly accounted for the shifts in financial ratios caused by such external influences. Our results also highlight significant variations in the performance of adjusted models compared to their original versions.