Impacts of crisis on SME bankruptcy prediction models' performance

• Decrease SME bankruptcy prediction models' performance for crisis periods. • No significant impacts of quantitative indicators for SME bankruptcy prediction. • High performance with gradient boosting algorithm on imbalanced data. • High performance with gradient boosting algorithm for three years predictions. Turbulent economic situation, changes in financial reporting, swift legislative changes, or companies' earnings management during the COVID-19 crisis might have impacted the performance of SME bankruptcy prediction models. Due to these circumstances, the performance of existing bankruptcy prediction models might have worsened. The main aim of this study is to analyse the impact of the crisis on the performance of bankruptcy prediction models. Data from 2015 to 2019 was collected for more than 90 000 SMEs to develop prediction models for three periods – two non-crisis periods and one crisis period. One-year, two-year and three-year predictions were made for these three periods via CatBoost, LightGBM and XGBoost methods. The results of this manuscript indicate that the performance of prediction models was significantly weaker during crisis periods than the performance during non-crisis periods. The weaker performance was the most evident for one-year predictions (6.5%). The difference was slightly lower for two-year predictions (4.8%) and three-year predictions (4.1%). Since lower sensitivity levels caused worse performance during crisis periods, it can be assumed that these bankruptcies were unexpected and most probably caused by the crisis. Once the COVID-19 crisis is over, existing bankruptcy models will need to be revalidated and recalibrated. [ FROM AUTHOR]

Impacts Of Crisis On Sme Bankruptcy Prediction Models’ Performance

Turbulent economic situation, changes in financial reporting, swift legislative changes, or companies’ earnings management during the COVID-19 crisis might have impacted the performance of SME bankruptcy prediction models. Due to these circumstances, the performance of existing bankruptcy prediction models might have worsened. The main aim of this study is to analyse the impact of the crisis on the performance of bankruptcy prediction models. Data from 2015 to 2019 was collected for more than 90 000 SMEs to develop prediction models for three periods – two non-crisis periods and one crisis period. One-year, two-year and three-year predictions were made for these three periods via CatBoost, LightGBM and XGBoost methods. The results of this manuscript indicate that the performance of prediction models was significantly weaker during crisis periods than the performance during non-crisis periods. The weaker performance was the most evident for one-year predictions (6.5%). The difference was slightly lower for two-year predictions (4.8%) and three-year predictions (4.1%). Since lower sensitivity levels caused worse performance during crisis periods, it can be assumed that these bankruptcies were unexpected and most probably caused by the crisis. Once the COVID-19 crisis is over, existing bankruptcy models will need to be revalidated and recalibrated.