## 1 Introduction  
The ability to predict financial distress in companies is critical in economic forecasting, enabling stakeholders to mitigate risks, allocate resources effectively, and prevent severe financial repercussions. Accurate financial distress prediction is especially vital in sectorssuch as the automotive industry, which is highly susceptible to global economic fluctuations. Over recent decades, numerous models have been developed and utilised to assess companies' financial stability, with notable contributions from researchers in Central and Eastern Europe, particularly from the Visegrad countries (the Czech Republic, Hungary, Poland, and Slovakia) [1; 2; 3]. These models have traditionally relied on financial indicators and statistical methods that reflect the economic conditions in the region, providing a region-specific foundation for evaluating company health [4; 5]. However, the COVID-19 pandemic introduced unprecedented disruptions to global economies, significantly affecting businesses' operational and financial performance across all sectors [6; 7]. These new and non-standard factors can significantly impact companies' financial stability and affect the effectiveness of existing prediction models. Although existing prediction models may be built on robust mathematical and statistical principles, their ability to predict financial distress during a pandemic may be limited [8]. New and unexpected factors such as government stimulus measures, moratoriums on loan repayments, temporary closures of businesses, and fluctuations in markets can affect the performance of these models. In this context, models that were once reliable under stable economic conditions may exhibit reduced accuracy in times of crisis. This raises a critical question about the robustness and adaptability of traditional financial distress prediction models when applied to post-pandemic conditions. Therefore, much attention is paid to analysing and updating existing bankruptcy prediction models to consider new factors and improve their accuracy in a pandemic environment. This may include adjusting weighting factors, adding new predictive variables, or modifying mathematical algorithms based on new data and experience gained during the pandemic. Although the automotive industry faced particular challenges, including supply chain disruptions and shifts in consumer demand, research exploring the pandemic's impact on model performance remains limited. To address these gaps, this study evaluates the effectiveness of financial distress prediction models originally developed by authors from Visegrad countries when applied to Slovak automotive companies' financial data across two periods. The analysis first assesses the models' performance during the pre-pandemic period (2017-2019), identifying the models with the highest prediction performance. Subsequently, the top-performing models are selected and applied to data from the pandemic period (2020-2022) to examine any changes in predictive performance. Observing a notable decline in performance during the pandemic, we proceeded to re-estimate the coefficients within each model. This re-estimation approach preserves the original variables and statistical methods, adapting the models to reflect the changed economic conditions better. By examining financial distress prediction models under varying economic conditions, this study offers valuable insights into the limitations of traditional models under unprecedented challenges like the COVID-19 pandemic. The findings underscore the importance of continuous evaluation of predictive models' performance to ensure their reliability during economic instability. The study provides a framework for enhancing financial risk assessment tools for practitioners and policymakers, especially within sectors like automotive sensitive to global disruptions. For researchers, the study offers a methodology for evaluating and refining existing models. The following sections of this paper are organised as follows.