The current COVID-19 pandemic has affected every aspect of consumer behavior—their expenses, investments, and financial reserves, as well as their financial and social wellbeing. As a consequence of different restrictions, consumers and their shopping patterns have changed significantly; thus, the factors that influence new purchase patterns need to be identified to help traders, retailers, and marketers develop appropriate strategies to respond to crucial consumer changes in the market. A categorical analysis (Pearson’s chi-square test) and correspondence analysis (simple and multivariate) were applied to a sample of 425 Slovak respondents to reveal the most important factors impacting consumers’ financial situations, as well as the effects on the maintenance of new shopping habits established during the pandemic period. The results revealed that consumers’ income, age, and sector of occupation play important roles in the context of new shopping patterns. These findings are in agreement with other global studies, confirming both the worldwide impact of the pandemic on consumer behavior and the importance of national studies on consumer shopping behavior in order for state authorities, traders, marketers, and entrepreneurs to be able to take necessary measures.

The issue of the debt, bankruptcy or non-bankruptcy of a company is presented in this article as one of the ways of conceiving risk management. We use the Amadeus database to obtain the financial and accounting data of Slovak enterprises from 2015 and 2016 to calculate the most important financial ratios that may affect the financial health of the company. The main aim of the article is to reveal financial risks of Slovak entities and to form a prediction model, which is done by the identification of significant predictors having an impact on the health of Slovak companies and their future prosperity. Realizing the multiple regression analysis, we identified the significant predictors in conditions of the specific economic environment to estimate the corporate prosperity and profitability. The results gained in the research are extra important for companies themselves, but also for their business partners, suppliers and creditors to eliminate financial and other corporate risks related to the unhealthy or unfavorable financial situation of the company.

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.

Research background: The paper investigates the earnings management phenomenon in the context of Central European countries, attempting to identify the factors and incentives that can influence earnings management behavior on a sample of 8,156 enterprises from Slovakia, the Czech Republic, Hungary, and Poland. Purpose of the article: The main purpose of the manuscript is to prove that there are significant differences in earnings management practices (measured by discretionary accruals) across the countries and to find the firm-specific features that influence the way enterprises manage their earnings. Methods: The modified Jones model was used to calculate the discretionary accruals, which are further analyzed across the countries. The statistically significant differences were confirmed across the countries. Thus, the impact of the economic sector, firm size, firm age, legal form, and ownership structure on earnings management behavior is studied by the Kruskal-Wallis test. The Dunn-Bonferroni post hoc tests then revealed the significant differences across the categories of the investigated earnings management determinants. To find the association between the particular earnings management practice (income-increasing or income-decreasing manipulation), correspondence analysis was used to visualize the mutual relations. Findings & value added: The results of the realized investigation revealed that the economic sector is one of the most important earnings management determinants, as its statistical significance was confirmed in each analyzed country. The correspondence analysis determined specific sectors, where income-increasing manipulation with earnings is practiced (NACE codes F, J, K, M, N), and vice versa, income-decreasing earnings management is characteristic for enterprises in sectors A, C, D, G or L. In specific economic conditions, firm size is also a relevant indicator (Hungary), or firm age and legal form and ownership structure (Poland). The recognition of crucial earnings management incentives may be helpful for authorities, policymakers, analysts and auditors when identifying various techniques and practices of earnings manipulation which could vary across the sectors and taking necessary measures to mitigate potential financial risks.

The risk of corporate financial distress negatively affects the operation of the enterprise itself and can change the financial performance of all other partners that come into close or wider contact. To identify these risks, business entities use early warning systems, prediction models, which help identify the level of corporate financial health. Despite the fact that the relevant financial analyses and financial health predictions are crucial to mitigate or eliminate the potential risks of bankruptcy, the modeling of financial health in emerging countries is mostly based on models which were developed in different economic sectors and countries. However, several prediction models have been introduced in emerging countries (also in Slovakia) in the last few years. Thus, the main purpose of the paper is to verify the predictive ability of the bankruptcy models formed in conditions of the Slovak economy in the sector of agriculture. To compare their predictive accuracy the confusion matrix (cross tables) and the receiver operating characteristic curve are used, which allow more detailed analysis than the mere proportion of correct classifications (predictive accuracy). The results indicate that the models developed in the specific economic sector highly outperform the prediction ability of other models either developed in the same country or abroad, usage of which is then questionable considering the issue of prediction accuracy. The research findings confirm that the highest predictive ability of the bankruptcy prediction models is achieved provided that they are used in the same economic conditions and industrial sector in which they were primarily developed.

Credit risk or default risk involves inability or unwillingness of a customer or counterparty to meet commitments in relation to lending, trading, hedging, settlement and other financial transactions. The Credit Risk is generally made up of transaction risk or default risk and portfolio risk. The portfolio risk in turn comprises intrinsic and concentration risk. The credit risk of a bank's portfolio depends on both external and internal factors. The external factors are the state of the economy, wide swings in commodity/equity prices, foreign exchange rates and interest rates, trade restrictions, economic sanctions, Government policies, etc. The internal factors are deficiencies in loan policies/administration, absence of prudential credit concentration limits, inadequately defined lending limits for Loan Officers/Credit Committees, deficiencies in appraisal of borrowers’ financial position, excessive dependence on collaterals and inadequate risk pricing, absence of loan review mechanism and post sanction surveillance, etc. This paper points out the measurement, hedging and monitoring of the credit risk