Abstract
The paper deals with the issue of analyzing the financial failure of businesses. The aim was to select key performance indicators entering the DEA model. The research was carried out on a sample of 343 Slovak heat management companies. When addressing the research problem, we made use of multidimensional scaling (MDS) and principal component analysis (PCA), which pointed out the areas of financial health of companies that may predict their financial failure. The core of our interest and research was the data envelopment analysis (DEA) method, which represents a more exact approach to the assessment of financial health. The important finding is that the statistical graphical methods—PCA and MDS—are very helpful in identifying outliers and selecting key performance indicators entering the DEA model. The benefit of the paper is the identification of companies that are at risk of bankruptcy using the DEA method. The originality is the selection of key inputs and outputs to the DEA model by the PCA method.
Highlights
Introduction and FinancialManagement 14 220.At present, the importance of predicting the risk of business failure is increasing.There are a number of methods, both theoretical and practical, that measure the financial health of companies, reveal the likelihood of their possible financial failure, and propose solutions to avoid bankruptcy
The output of the application of these methods was a set of financial indicators, which represented the input to the data envelopment analysis (DEA) model
The projection of cases processed by the principal component analysis (PCA) method indicates that the entire analyzed sample of companies creates a significant cluster in the space around the starting point of the coordinate system
Summary
The importance of predicting the risk of business failure is increasing. There are a number of methods, both theoretical and practical, that measure the financial health of companies, reveal the likelihood of their possible financial failure, and propose solutions to avoid bankruptcy. Several of these methods are based on mathematical and statistical methods, most of which involve the application of regression models and models of discriminant analysis. Authors researching this issue classify these methods and models from different perspectives.
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