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.

Prediction of the financial difficulties of companies has been dealt with over the last years by scientists and economists worldwide. Several prediction models mostly focused on a particular sector of the national economy, have been created also in Slovakia. The main purpose of this paper is to create new prediction models for small and medium-sized companies in Slovakia, based on real data from the Amadeus database from the years 2016–2018. We created prediction models of financial difficulties of companies for 1 year in advance and also a model for 2 years prediction. These models are based on the combination of two methods, discriminant analysis and logistic regression that belong, among others, to the group of the most commonly used methods to derive prediction models of financial difficulties of the companies. The overall prediction powers of the combined model are 90.6%, 93.8% and 90.4%. The results of this analysis can be used for early prediction of the financial difficulties of the company, that could be very useful for all the stakeholders.

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.

Financial managers intentionally use the legal manipulation of earnings to the business development. Earnings management is a widespread business finance phenomenon. The approaches and the ways of this practice are marked by many parallels. The main target of the article is to show the clusters with the close approach of the enterprises to the earnings manipulation and demonstrate hidden relations of the earnings management between sectors in the Visegrad Group. The study estimates the earnings management by the Modified Jones model. 52,545 discretionary accruals are computed for Slovak, Czech, Hungarian, and Polish enterprises for 5-year horizon in 19 sectors classified by the NACE. The clusters of close practices of the earnings management are identified based on principal component analysis and cluster analysis in every country of Visegrad Four. Moreover, the results detect the dependences between sectors in the region and the specificity of the sectors.

Capital structure is one of the most frequently discussed issues within Corporate Finance Theory. Optimizing the capital structure and value of the tax shield through the evaluation of its interests can lead to the increasing value of the enterprise, followed by the rising competitiveness and flexibility. The aim of this study is to provide a novel look at the value of the interest tax shield and its determinants in the emerging economies of the Visegrad Four. The model was created on a net sample of nearly 7,000 profitable enterprises between 2015 and 2019 using a one-way fixed effects model of panel data. Regional model results show five main determinants of the debt tax shield (Tangibility, Current Ratio, Gearing, Cost of debt and Size). Others, such as non-debt tax shield, business growth or profitability, are regional, and their impact varies depending on the economic conditions of the countries. The direction of influence of the main determinants indicates that, contrary to the assumed trade-off theory, profitable companies manage the capital structure and the value of tax shield according to the pecking order or modified pecking order theory. The tax shield is made up mostly of interest on short-term loans, which increases the risk of financial distress. There is a hierarchy of funding sources, from trade credit, through shortterm loans, to long-term loans, which are used in the analysed firms to the smallest extent. The structure of liabilities may be considered another determinant of the debt tax shield.

Businesses are influenced by the cyclical nature of economic development and distinct stages in the corporate life cycle. Accurate early-warning mechanisms are crucial to mitigating bankruptcy risk, enabling timely rescue measures. This article analyses the reliability of various bankruptcy prediction models, including those by Kliestik et al., Poznanski, the modified Zmijewski, Jakubik–Teply, and Virag–Hajdu, across corporate life cycle stages. Reliability was assessed using five metrics: accuracy, balanced accuracy, F1 and F2 scores, and the Matthews correlation coefficient (MCC). The sample included over 5000 SMEs from Central Europe, with financial data from 2022. The findings reveal a U-shaped trend in financial distress risk, with start-ups and declining enterprises facing the highest risks. The results indicate that the Kliestik et al. model shows consistent reliability across all life cycle stages, while the Poznanski model shows more variability. Conversely, the Virag–Hajdu model exhibits significant variability in reliability, with its best performance observed during the Decline stage. The modified Zmijewski and Jakubik–Teply models show lower MCC values overall, with the modified Zmijewski model performing better at predicting the financial distress of mature shake-out firms compared to other stages.