A New Approach for Risk of Corporate Bankruptcy Assessment during the COVID-19 Pandemic

The consequences of COVID-19 will aggravate existing multidimensional risks and reveal new ones. The research gap allows contributing to recognizing the exogenous risk factors of corporate bankruptcy during the COVID-19 pandemic in EU countries. This study aims at revealing how to evaluate the risk of corporate bankruptcy phenomenon in the COVID-19 times. The question arises as to whether Schumpeter’s creative destruction approach is still accurate. The article concentrates on implementing the fsQCA (fuzzy set Qualitative Comparative Analysis) method to identify and evaluate the main exogenous drivers of corporate bankruptcy in EU countries based on Fragile States Index data. This new approach focuses on fuzzy sets theory. The fsQCA method is a globally recognized alternative to quantitative analysis (in which the causal complexity is ignored) and qualitative methods for examining individual cases (which do not have the tools to generalize on their basis). The research indicates and examines the main external factors that would increase the risk of corporate bankruptcy in EU countries: namely, economic decline, uneven economic development, unemployment rate, demographic pressure, and government debt. The study discusses the influence of zombie companies on economies during the COVID-19 pandemic. Identifying risk factors that determine the threat of corporate bankruptcy may constitute practical recommendations for business and restructuring practitioners, financial institutions, and banking and public sector representatives in creating warning and recovery measures during the COVID-19 pandemic.

The instrumental machine learning methods for corporate bankruptcy prediction

Subject. It is very important for corporate governance and a choice of partners to evaluate the company’s position. Therefore, bankruptcy forecast methods have been actively studied in theoretical and practical proceedings for a long time. Recurring crises and high market dynamics make the subject especially relevant. Objectives. I develop the instrumental method based on machine learning to predict corporate bankruptcy. The study also reviews data sources, the potential of forecasting models, and chooses inputs for company analysis. Methods. I applied methods of analysis and synthesis, and the systematization, formalization, comparative analysis. I referred to theoretical and methodological principles set forth in national and foreign proceedings on the company analysis and bankruptcy prediction. I investigate issues of data compilation, and building the artificial neural network for teaching the model. Results. I proposed and tested the instrumental method to predict bankruptcy. I suggest using my own sets of indicators for forecasting, which I selected by analyzing key indicators of financial sustainability, efficacy, and key external factors influencing market actors. The article presents a data sample for teaching purposes, which includes both the Russian and foreign companies, thus expanding its size. I devised machine learning models generating high-precision forecasts. Conclusions and Relevance. The findings contribute to bankruptcy prediction methods and can be used for administrative decision-making to automate their own analysis or analyze other entities, which the company cooperates with.