Dominika Gajdosikova

Research purpose. Financial distress being a global phenomenon makes it impact firms in all sectors of the economy and predicting corporate bankruptcy has become a crucial issue in economics. At the beginning of the last century, the first studies aimed to predict corporate bankruptcy were published. In Slovakia, however, several prediction models were developed with a significant delay. The main aim of this paper is to develop a model for predicting bankruptcy based on the financial information of 3,783 Slovak enterprises operating in the manufacturing and construction sectors in 2020 and 2021. Design / Methodology / Approach. A prediction model that uses the appropriate financial indicators as predictors may be developed using multiple discriminant analysis. Multiple discriminant analysis is currently used in prediction model development. In this case, financial health is assessed using several variables that are weighted in order to maximise the difference between the average value calculated in the group of prosperous and non-prosperous firms. When developing a bankruptcy prediction model based on multiple discriminant analysis, it is crucial to determine the independent variables used as primary financial health predictors. Findings. Due to the discriminant analysis results, the corporate debt level of the monitored firms may be regarded as appropriate. Despite the fact that the model identified 215 firms in financial distress due to an insufficient debt level, 3,568 out of 3,783 Slovak enterprises operating in the manufacturing and construction sectors did not have any problems with financing their debts. The self-financing ratio was identified in the developed model as the variable with the highest accuracy. Based on the results, the developed model has an overall discriminant ability of 93% since bankruptcy prediction models require strong discriminating abilities to be used in practice. Originality / Value / Practical implications. The principal contribution of the paper is its application of the latest available data, which could help in more accurate financial stability predictions for firms during the current difficult period. Additionally, this is a ground-breaking research study in Slovakia that models the financial health of enterprises in the post-pandemic period.

Researchers from all over the world have become more interested in bankruptcy prediction during the past 50 years. The prediction of corporate financial difficulties has been the subject of numerous studies ever since Altman revealed the breakthrough bankruptcy prediction model in 1968. The main aim of this research paper is to describe the fundamental concepts associated with the subject of corporate bankruptcy prediction. By identifying the most relevant research papers, nations, and authors in the Web of Science database, an in-depth review of the publications was performed before the analysis. The bibliometric map was created in the VOS Viewer program using the final search result with all available information. The results of the bibliometric analysis reveal that the keywords bankruptcy prediction and classification are the most closely related keywords using the analysis of citations that frequently occur, and that the USA and China developed the most significant international co-author relationships.

Various models have been created all around the world to identify enterprises that manipulate their earnings. These earnings management techniques aid businesses in enhancing their financial performance or gaining some competitive advantages. The primary goal of this article was to identify the firm-specific characteristics that affect how businesses manage their earnings using a sample of 15,716 businesses from various economic sectors in the Slovak environment during a 3 year period. The level of earnings management was measured by discretionary accruals using the Kasznik model. In this paper, a correspondence analysis using the chi-square distance measure was applied to find the dependence between the earnings management practices and firm-specific features (firm size, legal form, and sectoral classification). The results of the study indicate that aggressive (income-increasing) earnings management practices are typical of small enterprises with a public limited ownership structure, mostly in sectors R and M (using the NACE sectoral classification). Conservative (income decreasing) practices can be observed in enterprises in the sectors J or F, and they are also used by medium-sized enterprises and those with private limited ownership structure. The results revealed that large enterprises do not tend to manipulate their earnings, as well as enterprises operating in sector K. The insights of this study may provide important and useful information for shareholders and regulators in evaluating determinants that are effective in mitigating earnings management practices. Authorities, regulators, analysts, and auditors may find the importance of the discovered variances helpful in identifying various strategies and techniques for earnings manipulation that may differ among industries according to their typical characteristics.

Several different models have been developed worldwide to detect manipulative financial reporting in en-terprises. These earnings management practices help enterprises improve their financial performance or gain some advantages based on window dressing techniques. Moreover, there are several firm-specific factors and indicators that can influence the earnings management behavior of enterprises. The purpose of this paper is to test the relationship between corporate debt and earnings management in a sample of 15,716 Slovak firms over a 5-year period. The level of earnings management is measured by discretionary accruals using the Kasznik model, the debt of enterprises is quan-tified by several indicators (total indebtedness ratio, self-financing ratio, current and non-current indebtedness ratios, equity leverage ratio, and insolvency ratio). In this paper, a correlation analysis and an ANOVA method were applied to show if there is any statistically significant dependence between the level of discretionary accruals and corporate debt. The results indicate a positive relationship between the level of discretionary accruals and total indebtedness, non-current indebtedness, and insolvency ratios, while a negative relationship was revealed for self-financing and cur-rent indebtedness ratios.