The scientific periodical journal (indexed on Web of Science) published by the Tomas Bata University in Zlin offers results of basic and applied economic research in the English language.

Research background: Financial risk management is the task of monitoring financial risks and managing their impact. Financial risk is often perceived as the risk that a company may default on its debt payments. The issue of the debt, default or prosperity of the company are presented in the article as one of the ways of the risk management. A prediction of corporate default is an inseparable element of the risk management. Mainly the consequences of risk are the engine of research and development of methods and models, which enable to predict economic and financial situation in specific conditions of global economies. Purpose of the article: The main aim of the presented article is to assess financial risks of Slovak entities, realized by the identification of significant factors and determinants affecting the prosperity of Slovak companies. Methods: To conduct the research we have used the data of Slovak enterprises, obtained from annual financial reports covering the year 2015 and the calculated financial ratios of profitability, activity, liquidity and indebtedness that may affect the financial health of the company were applied in the regression analysis. Realizing the multiple regression analysis, the statistically significant determinants that affect the future financial development of the company are identified, as well as the regression model of the bankruptcy prediction. Findings & Value added: In the research aimed at the management of financial risks in Slovak enterprises, we focused on the revelation of significant economic risk factors using multiple regression. The results suggest that the most significant predictors are net return on capital, cash ratio, quick ratio, current ratio, net working capital, RE/TA ratio, current debt ratio, financial debt ratio and current assets turnover based on which the decision about the future company default can be made. These factors are significant enough to manage financial risks and to affect the profitability and prosperity of the company.

Risk management is one of the most important internal process, not only in large companies but also in small and medium-sized enterprises (SMEs). To identify the source of risk can be crucial in all companies. The primary objective of this study is to analyze and compare the economic and financial risk sources in SMEs of the V4 (Visegrad Group: Czech Republic, Hungary, Poland and Slovakia) and Serbia, in the context of the business environment of the countries analyzed. To achieve this goal, a questionnaire-based survey was carried out involving 2110 SMEs from Hungary, Poland, Slovakia, the Czech Republic, and Serbia. The questionnaire included questions about the importance of risks and the concept of risk management in the company. To test the formulated hypotheses, the following statistical tools were used: contingency tables, a Z-value, and a general non-hierarchical log-linear model with three categorical variables and a continuous covariate. Finally, the differences among V4 countries and Serbia were identified. Serbia is more vulnerable to the financial risk sources studied than the V4 countries. The result of the research shows that insufficient profit is more hazardous compared to the other risk sources and all countries are more vulnerable in in this issue. The article concludes with a discussion and a comparison with previous international researches.

Research background: Enterprises manage earnings in an effort to balance their profit fluctuations to provide increasingly consistent earnings in every reporting period. Earnings management is legal and very effective method of accounting techniques and may be used to obtain specific objectives of the enterprises involving the manipulation of accruals. Therefore, there is a need to analyze it in the context of group of countries, while the issue of their detection in the new ways appears. Purpose of the article: The analysis of annual earnings before interest and taxes (EBIT) of 5,640 enterprises from the Visegrad Four during the period 2009?2018 confirms that the development of earnings management in these countries is not a randomness. Thus, the aim of this article is to determine the existence of positive trend in earnings management and to detect the change-point in its development for each Visegrad country. Methods: Grubbs test, Mann-Kendall trend test and Buishand test were used as appropriate statistical methods. Mann-Kendall test identifies significant monotonic trend occurrence in earnings manipulation in every country. Buishand test indicates significant years, which divides the development of EBIT into two homogenous groups with individual central lines. Findings & Value added: Based on the statistical analysis applied, we rejected randomness in the managing of earning, but we determined the trend of its increasing. The positive earnings manipulation was not homogenous in the analyzed period, however, a change-point was defined. Year 2014 was identified as a break-point for Slovak, Polish and Hungarian enterprises considering the earnings manipulation. Year 2013 was detected as a change-point in Czech enterprises. The methodical approach used may be very helpful for researchers from other countries to determine, detect and understand earnings management as well as for the investors to make decisions based on a specificities of an individual country.

Research background: Prediction of bankruptcy is an issue of interest of various researchers and practitioners since the first study dedicated to this topic was published in 1932. Finding the suitable bankruptcy prediction model is the task for economists and analysts from all over the world. forecasting model using. Despite a large number of various models, which have been created by using different methods with the aim to achieve the best results, it is still challenging to predict bankruptcy risk, as corporations have become more global and more complex. Purpose of the article: The aim of the presented study is to construct, via an empirical study of relevant literature and application of suitable chosen mathematical statistical methods, models for bankruptcy prediction of Slovak companies and provide the comparison of overall prediction ability of the two developed models. Methods: The research was conducted on the data set of Slovak corporations covering the period of the year 2015, and two mathematical statistical methods were applied. The methods are logit and probit, which are both symmetric binary choice models, also known as conditional probability models. On the other hand, these methods show some significant differences in process of model formation, as well as in achieved results. Findings & Value added: Given the fact that mostly discriminant analysis and logistic regression are used for the construction of bankruptcy prediction models, we have focused our attention on the development bankruptcy prediction model in the Slovak Republic via logistic regression and probit. The results of the study suggest that the model based on a logit functions slightly outperforms the classification accuracy of probit model. Differences were obtained also in the detection of the most significant predictors of bankruptcy prediction in these types of models constructed in Slovak companies.

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.