**1. Introduction**

Determining the probability of bankruptcy is becoming one of the most important risk management tasks. We pay close attention to predictions of bankruptcy due to the fact that it is important from the point of view of creditors, employees, and other entities around the affected company that would feel the effect the bankruptcy brings (Štefko et al. 2012). Bankruptcy prediction methods can, with a certain degree of probability, alert a company to a negative situation. By taking early remedy measures, businesses can prevent future bankruptcy events (Gundová 2015). To date, empirical studies have found that inefficiency, high corporate indebtedness, and solvency problems are a prerequisite for bankruptcy (Altman 1968). According to Achim et al. (2012) the risk of bankruptcy of an enterprise is closely linked to economic and financial risks. The financial risk is determined by the level of indebtedness, while the economic risk depends on the ratio of fixed and variable costs. In general, knowing these risks allows us to quantify the risk of bankruptcy. Although there is no uniform definition of the concept of bankruptcy, it is advisable to follow the definition of Dimitras et al. (1996), according to whom bankruptcy is a situation where an enterprise is unable to repay its creditors' obligations and meet obligations towards shareholders or suppliers, or where bankruptcy proceedings were commenced under applicable law. Ding et al. (2008) described bankruptcy in a similar way: Bankruptcy is a situation where a firm could not pay lenders, preferred stock shareholders, suppliers, etc., or a bill was overdrawn, or a firm was bankrupt. Trahms et al. (2013) have contributed the most to the current research on finding the causes of bankruptcy. They state that more complex indicators of business performance decline should be identified in any research on bankruptcy. Scarlat and Delcea (2011) introduced a new concept, a bankruptcy syndrome, which they define as a summary of related symptoms that characterise a particular situation that can lead to bankruptcy. Many authors devote their attention to financial symptoms of bankruptcy, which are expressed by financial indicators. These authors include, among others, Beaver (1966), Altman (1968), Altman et al. (1977), Geng et al. (2014), and Ding et al. (2008) who attributed the risk of bankruptcy to reduced company profitability. One of the most common aspects involved in assessing the cause of bankruptcy is either financial or non-financial causes. Chen et al. (2009) determined that non-financial causes of corporate bankruptcy include: customer relationship levels, IT infrastructure, innovation potential, employee professionalism, and other factors. Martin et al. (2012) referred to other parameters of bankruptcy such as operational risk, competitiveness, credibility analysis, training of employees, quality, etc. The loss of competitiveness as a cause of bankruptcy was also examined by Suhányi and Suhányiová (2017). In addition to the previous classifications, research studies often classify the causes of bankruptcy based on the environment they come from through external and internal causes (Blazy et al. 2008). Despite the existence of various other causes of bankruptcy, our empirical study prioritises the financial causes of bankruptcy.

The next part of the text is structured as follows: The second chapter outlines the theoretical basis of the studied problem. This part of the paper lists various methods and models used for predicting the risk of bankruptcy. A special part is devoted to the summary of the theoretical knowledge about the Data Envelopment Analysis (DEA) method. At the end of the theoretical part, a research problem and goal are formulated. The aim of the paper is to find out whether businesses from the analysed sample have been faced the threat of bankruptcy due to financial difficulties. The third chapter describes the data, the analysed sample of companies, and the processing methods. When addressing the research problem, we made use of selected financial indicators, a correlation matrix, the Additive DEA model (ADD model) model, and the Logit model. We formulated the ADD non-oriented model with variable returns to scale that were solved with the use of Efficiency Measurement Systems (EMS) software. The fourth chapter includes results and discussion of the results achieved. This chapter lists and compares the results of bankruptcy prediction with the use of the DEA and Logit models. The final part of the paper is the conclusion in which the essential conclusions resulting from the research problem are addressed.
