Abstract
Assessment and estimation of bankruptcy risk is important for managers in decision making for improving a firm’s financial performance, but also important for investors that consider it prior to making investment decision in equity or bonds, creditors and company itself. The aim of this paper is to improve the knowledge of bankruptcy prediction of companies and to analyse the predictive capacity of factor analysis using as basis the discriminant analysis and the following five models for assessing bankruptcy risk: Altman, Conan and Holder, Tafler, Springate and Zmijewski. Stata software was used for studying the effect of performance over risk and bankruptcy scores were obtained by year of analysis and country. Data used for non-financial large companies from European Union were provided by Amadeus database for the period 2006–2015. In order to analyse the effects of risk score over firm performance, we have applied a dynamic panel-data estimation model, with Generalized Method of Moments (GMM) estimators to regress firm performance indicator over risk by year and we have used Tobit models to infer about the influence of company performance measures over general bankruptcy risk scores. The results show that the Principal Component Analysis (PCA) used to build a bankruptcy risk scored based on discriminant analysis indices is effective for determining the influence of corporate performance over risk.
Highlights
IntroductionIn Western Europe, a downside trend in insolvencies was recorded from 2014 to 2017, the increase mentioned by 2% in 2018 compared to 2017 was determined by different evolution by other countries: a noticeable upturn of 12% in the UK due to the Brexit-related uncertainties that added headwinds on businesses; a stabilization of insolvencies can be seen in France, Spain and Belgium, in France in 2018, 54,751 companies went bankrupt, corresponding to a fairly high 1.3% of the active business universe (Dun & Bradstreet 2019); an increase in the Nordic countries of 10% in Sweden, 3% in Norway, 19% in Finland and 25% in Denmark
In this paper we proposed to use Principal Component Analysis (PCA) based on the five discriminant analysis measures, i.e., Z-score determined by the following models: revised Z-score Altman, Conan and Holder, Tafler, Springate and Zmijewski in order to test the efficiency in predicting the risk of bankruptcy
In order to analyse the effects of risk scores over firm performance, we applied a dynamic panel-data estimation model, with Generalized Method of Moments (GMM) estimators to regress earnings before interest and taxes to total assets over risk by year
Summary
In Western Europe, a downside trend in insolvencies was recorded from 2014 to 2017, the increase mentioned by 2% in 2018 compared to 2017 was determined by different evolution by other countries: a noticeable upturn of 12% in the UK due to the Brexit-related uncertainties that added headwinds on businesses; a stabilization of insolvencies can be seen in France, Spain and Belgium, in France in 2018, 54,751 companies went bankrupt, corresponding to a fairly high 1.3% of the active business universe (Dun & Bradstreet 2019); an increase in the Nordic countries of 10% in Sweden, 3% in Norway, 19% in Finland and 25% in Denmark. Other countries continued to rise in insolvencies: 3% for Bulgaria in 2019 where the changes in the Insolvency law done in 2017 kept on boosting the bankruptcies of sole proprietorships, Slovakia of 16%, Poland of 5% where businesses have a structural problem of profitability and will face a noticeable deceleration of the economy
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
