Research background: In a modern economy, full of complexities, ensuring a business' financial stability, and increasing its financial performance and competitiveness, has become especially difficult. Then, monitoring the company's financial situation and predicting its future development becomes important. Assessing the financial health of business entities using various models is an important area in not only scientific research, but also business practice. Purpose of the article: This study aims to predict the bankruptcy of companies in the engineering and automotive industries of the Slovak Republic using a multilayer neural network and logistic regression. Importantly, we develop a novel an early warning model for the Slovak engineering and automotive industries, which can be applied in countries with undeveloped capital markets. Methods: Data on the financial ratios of 2,384 companies were used. We used a logistic regression to analyse the data for the year 2019 and designed a logistic model. Meanwhile, the data for the years 2018 and 2019 were analysed using the neural network. In the prediction model, we analysed the predictive performance of several combinations of factors based on the industry sector, use of the scaling technique, activation function, and ratio of the sample distribution to the test and training parts. Findings & value added: The financial indicators ROS, QR, NWC/A, and PC/S reduce the likelihood of bankruptcy. Regarding the value of this work, we constructed an optimal network for the automotive and engineering industries using nine financial indicators on the input layer in combination with one hidden layer. Moreover, we developed a novel prediction model for bankruptcy using six of these indicators. Almost all sampled industries are privatised, and most companies are foreign owned. Hence, international companies as well as researchers can apply our models to understand their financial health and sustainability. Moreover, they can conduct comparative analyses of their own model with ours to reveal areas of model improvements.

Slovak spa companies significantly contribute to tourism support and to health tourism development. The paper aims to evaluate the financial situation and financial position of 21 Slovak spa companies, and the Slovak spa industry within NACE 86 - Human Health Activities and NACE 869 - Other Human Health Activities. In terms of legal form, the research sample consists of 16 joint-stock companies (Inc. (a.s.)), 3 limited liability companies (Ltd. (s.r.o.)), and 2 state-owned companies (s.o.). Data of given spa companies are obtained from their Financial Statements in Register of Financial Statements of the Ministry of Finance of the Slovak Republic. Data on mean values of the financial indicators for NACE 86 and NACE 869 were obtained from the company CRIF - Slovak Credit Bureau. We cover the period 2007-2018. Our results include an evaluation of the criterion of economic efficiency of the industry NACE 86, the sub-industry NACE 869, and individual spa companies using methods of quantifying the impact of determining factors. At the same time, we present the factors that influence development most. We determine the position of spa companies by using multidimensional scaling (MDS). Results show that the total assets turnover ratio and return on sales are contributing factors to the change of ROE to the greatest extent. MDS quantifies that Spa Bojice, a.s., has the best financial position in space, on the other hand, Specialized Medical Institute Marína, s.o., has the worst position.

Industry 4.0 and related automation and digitization have a significant impact on competition between companies. They have to deal with the lack of financial resources to apply digital solutions in their businesses. In Slovakia, Industry 4.0 plays an important role, especially in the mechanical engineering industry (MEI). This paper aims to identify the groups of financial ratios that can be used to measure the financial performance of the companies operating in the Slovak MEI. From the whole MEI, we selected the 236 largest non-financial corporations whose ranking we obtained according to the amount of generated revenues in 2017. Using factor analysis, from eleven traditional financial ratios, we extracted four independent factors that measure liquidity (equity to liabilities ratio, quick ratio, debt ratio, net working capital to assets ratio, current ratio), profitability (return on sales, return on investments), indebtedness (financial leverage, debt to equity ratio), and activity (assets turnover, current assets turnover) of the company. Our analysis is an essential prerequisite for developing a realistic financial plan for companies operating in the MEI, especially when considering investments in new technologies related to Industry 4.0.

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.

Coopetition has been the issue of various studies in different fields, but there is a research gap in examining coopetition within the tourism sector and destination management. This paper aims to determine whether there are internally homogeneous and externally heterogeneous groups of European Union countries regarding indicators of natural and cultural resources of the Travel and Tourism Competitiveness Index (TTCI), and thus subsequently identify the importance and possibilities of competition among countries within the tourism sector. Multidimensional scaling and cluster analysis are used to verify the research hypothesis, along with ten indicators of the fourth sub-index (Natural and Cultural Resources) of TTCI. The results of the cluster analysis led to a six-group solution. Italy, Spain, and France have the best position in terms of tourism competitiveness. The results show space for competition in the international tourism market. Even though EU countries are competitors at a global level, their cooperation could be beneficial to tourism development. The findings of this study can be helpful in planning and strategy development for tourism policymakers and destination management organizations but can also be used to develop various marketing strategies. Furthermore, cooperation between destinations will support the need for strategic flexibility in the tourism sector, as the diversity of tourism attractions will increase.

The aim of this paper is to estimate the probability of bankruptcy of the companies from the Slovak electrical engineering industry based on data obtained from financial statements. Parameters of the predictive model were estimated using binary logistic regression. This model is able to predict the probability of a company’s bankruptcy based on values of significant explanatory variables (accounts payable turnover ratio (APTR), return on sales (ROS), quick ratio (QR), financial leverage (FL), net working capital/assets (NWC/A)). The model is constructed using the financial data of a large sample of electrical engineering companies from 2017. Resulting estimated odds ratios show that, in the electrical engineering industry, ROS, QR, and NWC/A significantly reduce the likelihood of bankruptcy. In other words, if these financial indicators increase, the probability of bankruptcy decreases. Our results are also applicable to other industries connected with industrial production, especially the mechanical engineering industry.