Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 13, 2022 erstellt
Description:
Research background: Visegrad Four (V4)-level corporate failure modeling is a relatively new research topic. Three empirical studies have been published in scientific journals since 2018 by Slovak researchers in this field. As COVID-19 has affected financial positions and failure behavior patterns of V4 companies, it has become necessary to examine the impact of COVID-19 on V4-level corporate failure prediction, and to reconsider circumstances for modeling.Purpose of the article: This article presents a novel V4-level corporate failure prediction model concentrating on V4 large corporates using long-term historical data. It considers financial data between 2012 and 2020 and failure events occur-ring between 2013 and 2021. Incorporation of COVID-19 financial data and failure experience is a novel research object, as financial data from 2020 and failure events from 2021 have only recently become available.Methods: Model development is accomplished by using a combination of Chi-squared Automatic Interaction Detection (CHAID) and Logistic Regression (logit) methods. Results are evaluated by the Area Under ROC (AUROC) analysis meth-od. A total of 245,974 firm-year observations and 3,091 failure events comprise the modeling database collected from the Moody’s Analytics Orbis Europe data source. Findings & value added: Testing of the pre-COVID-19 model with actual data reveals that results are favorable until 2019 financial data and 2020 failures are included, whereupon predictive power becomes substantially lower for 2020 financial data and 2021 failures. It is thus essential to prepare a new point-in-time model considering only very recent data to lead to a different model design. Testing of this model is successful in that COVID-19 failures indicate that sector classification is significant in predicting overall corporate failure. Results demonstrate that in the current turbulent economic environment failure prediction models must be reviewed at least on an annual basis