Research background: The problem of bankruptcy prediction models has been a current issue for decades, especially in the era of strong competition in markets and a constantly growing number of crises. If a company wants to prosper and compete successfully in a market environment, it should carry out a regular financial analysis of its activities, evaluate successes and failures, and use the results to make strategic decisions about the future development of the business. Purpose of the article: The main aim of the paper is to develop a model to reveal the un-healthy development of the enterprises in V4 countries, which is done by the multiple discriminant analysis. Methods: To conduct the research, we use the Amadeus database providing necessary financial and statistical data of almost 450,000 enterprises, covering the year 2015 and 2016, operating in the countries of the Visegrad group. Realizing the multiple discriminant analysis, the most significant predictor and the best discriminants of the corporate prosperity are identified, as well as the prediction models for both individual V4 countries and complex Visegrad model. Findings & Value added: The results of the research reveal that the prediction models use the combination of same financial ratios to predict the future financial development of a company. However, the most significant predictors are current assets to current liabilities ratio, net income to total assets ratio, ratio of non-current liabilities and current liabilities to total assets, cash and cash equivalents to total assets ratio and return of equity. All developed models have more than 80 % classification ability, which indicates that models are formed in accordance with the economic and financial situation of the V4 countries. The research results are important for companies themselves, but also for their business partners, suppliers and creditors to eliminate financial and other corporate risks related to the un-healthy or unfavorable financial situation of the company.

A great number of researchers argue that the development of large swathes of the global economy and competitiveness are influenced by fluctuations in international oil prices. The aim of this article is to determine to what extent those fluctuations in oil prices influence the value of the Euro relative to the value of USD. Data on the EUR/USD exchange rate were used for the analysis, with the time series that follow based on the price of Brent Crude. Statistica software (version 13) was used for the data processing. The research was based on a unified procedure with gradual changes in one parameter, namely in the time series (1, 5, 10 and 30 days), following which a regression analysis using neural networks was performed based on 10,000 networks that were generated for each experimental combination. As a result, eight calculations and eight different outcomes were obtained. From each experiment, the 5 artificial neural networks that showed the best results were retained. The outcomes suggest that the EUR/USD exchange rate is strongly dependent on the international price of oil. The impact of fluctuations in the price of oil on the EUR/USD exchange rate can therefore be accurately predicted. These results imply that it is also possible to predict the impact of such fluctuations on the performance of national economies. These predictions can be used to enhance competitiveness, including that of companies actively operating in international markets.

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Abstract. Stock price forecasting is highly important for the entire market economy as well as the investors themselves. However, stock prices develop in a non-linear way. It is therefore rather complicated to accurately forecast their development. A number of authors are now trying to find a suitable tool for forecasting the stock prices. One of such tools is undoubtedly artificial neural network, which have a potential of accurate forecast based even on non-linear data. The objective of this contribution is to use neural networks for forecasting the development of the ČEZ, a. s. stock prices on the Prague Stock Exchange for the next 62 trading days. The data for the forecast have been obtained from the Prague Stock Exchange database. These are final prices at the end of each trading day when the company shares were traded, starting from the beginning of the year 2012 till September 2017. The data are processed by the Statistica software, generating multiple layer perceptron (MLP) and radial basis function (RBF) networks. In total, there are 10,000 neural network structures, out of which 5 with the best characteristics are retained. Using statistical interpretation of the results obtained, it was found that all retained networks are applicable in practice.

The objective of the contribution is to propose a new methodology for determining the optimal credit absorption capacity of an enterprise while maintaining the positive function of financial leverage, i.e., the maximum possible loan that would continuously bring benefit to the enterprise. The proposed methodology determines the credit absorption capacity of an enterprise according to EVA Equity and EVA Entity. Based on a theoretical analysis of both indicators, the possibility of applying the proposed methodology for this purpose was proved. To verify the theoretical assumptions, the optimal credit absorption capacity of enterprises operating in the agricultural sector of the CR was determined. The data used for the purposes of the contribution were obtained from the Albertina database for the years 2012–2018. The credit absorption capacity of the monitored enterprises ranged from CZK 6.88 million to CZK 9.6 million. The article also determines the optimal ratio of equity to debt capital.