Research background: Deteriorating economic conditions and a negative outlook increase the pressure on financial management and the need to show high financial performance. According to Positive Accounting Theory, the growing risk of bankruptcy is associated with the phenomenon of earnings management. Bankruptcy risk and the quality of reported profits, along with other aspects of financial performance, vary throughout the company's life cycle. Nevertheless, these factors or their interactions are investigated only to a very small extent. Purpose of the article: The aim of this study is to clarify the impact of corporate life cycle and bankruptcy on earnings management, in order to describe behaviour of companies at different stages of corporate life cycle. Methods: A hierarchical mixed model with a random time and industry effect was chosen as appropriate because it allows the investigation of multilevel data that is not independent. The sample covers the financial indicators of more than 33,000 Central European companies from 2015?2019. The non-sequential Dickinson model, company age, and three models of accrual earnings management were used as proxies for the company's life cycle and quality of reported profit. Findings & value added: Earnings management and bankruptcy risk have a U-shape, indicating that financially distressed firms reduce reported accounting profit at the Introduction, Decline and, to a lesser extent, at the Growth stage. Slovak and Czech companies manipulate profits to a similar extent, Hungarian companies increase accounting profit to a greatest extent than the surveyed countries by controlling bankruptcy ? life cycle effect; however, the variability of accounting manipulations across industries has not been demonstrated. These findings imply that start-ups and declining businesses provide crooked financial statements to obtain more favourable debt covenants, and estimating discretionary accruals using life-cycle subsamples can improve the predictive power of accrual earnings management models.

Predicting the risk of financial distress of enterprises is an inseparable part of financial-economic analysis, helping investors and creditors reveal the performance stability of any enterprise. The acceptance of national conditions, proper use of financial predictors and statistical methods enable achieving relevant results and predicting the future development of enterprises as accurately as possible. The aim of the paper is to compare models developed by using three different methods (logistic regression, random forest and neural network models) in order to identify a model with the highest predictive accuracy of financial distress when it comes to industrial enterprises operating in the specific Slovak environment. The results indicate that all models demonstrated high discrimination accuracy and similar performance; neural network models yielded better results measured by all performance characteristics. The outputs of the comparison may contribute to the development of a reputable prediction model for industrial enterprises, which has not been developed yet in the country, which is one of the world’s largest car producers.

Although the bankruptcy prediction models can be a stabilizing element on both macro and microeconomic levels, they are rather a domain of academic research than an instrument, widely applied in a business practice. It is especially true if the models are reflecting the conditions of countries of their origin, rather than countries of their intended uses. Besides, few of the models contain inherent flaws, including the absence of a methodical approach addressing this problem of the severely imbalanced representation of bankrupt companies in financial datasets. The article is focused on the use of oversampling with SMOTE (Synthetic Minority Oversampling Technique) algorithm under the condition of extremely imbalanced data sets of Slovak companies. While the model does not provide a single answer in many (if not most) of the situations, it still could be used for the selection of companies for which the more detailed (and expensive) analysis is not required.

Earnings management is a legal and widely preferred phenomenon of business finance that financial managers use to maintain and improve the enterprise’s competitiveness. Managers purposely manipulate business earnings to achieve the required status of the enterprise. The consequence of these activities is to provide a positive perspective for the owners, encourage the profitability for the creditor and the investors as well as demonstrate economic strengths to competitors. This article aims to identify parallels and differences in earnings management of enterprises in the Visegrad Four and the Baltics in terms of competitiveness for the nineyear period 2010-2018. The research uses a final sample of 4,543 observations from the EBITs of Slovak, Czech, Hungarian and Polish enterprises as well as 1,633 observations from the EBITs of Latvian, Lithuanian and Estonian enterprises. Time-series methods with all necessary assumptions have been run for the analyzed financial dataset. The results of the econometric modeling of unit roots show significant parallels in these groups of countries. The enterprises from the Visegrad group and the Baltics group use the apparatus of earnings management to be competitive. The obtained results confirm the systematic but legal manipulation from the side of management. A quantitative analysis of homogeneity tests using 1,000,000 Monte Carlo simulations indicates significant time differences of manipulation in these emerging countries. The year 2014 signaled a radical “accelerando” in earnings management for the V4, and the year 2016 is highlighted for the Baltics.

To make the accounting data internationally comparable, the International Financial Reporting Standards (IFRS) have been adopted by companies across the world, including those in the member states of the European Union. In 2017, the new international financial reporting standard (IFRS 16, “Leases”) has introduced mandatory recognition, in the statements of the financial position, of some of the former off-balance-sheet assets and liabilities arising from operating leases. Since 2019, the IFRS 16 has become binding for all financial statements prepared by the IFRS adopters. By the end of the transition period, we have collected the financial statements of 155 Slovak IFRS adopters, extracted the related data, and classify them into four categories. The first three of those categories are rather auxiliary – general information about the company and its financial statements, underlying information about the transition process from formerly applied standard IAS 17 to IFRS 16, and the disclosure of the related information in financial statements. The fourth one, the impact of the adoption of the IFRS 16 on the statements of the financial position as of the date of transition, contains information about the financial impact of the IFRS 16 on Slovak first-time adopters. To enable further analyses, we compile and make available the list of direct links to 761 financial statements of Slovak IFRS adopters, covering years 2015-2019.