Research background: Covid-19 has affected the global economy and has had an inevitable impact on capital markets. In the week of February 24?28, 2020, stock markets crashed. The index FTSE 100 decreased 13%, while the indices DJIA and S&P 500 fell 11?12%, the biggest drop since the 2007?2008 financial and economic crisis. It is therefore of interest to test the random walk hypothesis in developed capital markets, European and also non-European, in order to understand the different predictabilities between them. Purpose of the article: The aim is to analyze capital market efficiency, in its weak form, through the stock market indices of Belgium (index BEL 20), France (index CAC 40), Germany (index DAX 30), USA (index DOW JONES), Greece (index FTSE Athex 20), Spain (index IBEX 35), Ireland (index ISEQ), Portugal (index PSI 20) and China (index SSE) for the period from December 2019 to May 2020. Methods: Panel unit root tests of Breitung (2000), Levin et al. (2002) and Hadri (2002) were used to assess the time series stationarity. The test of Clemente et al. (1998) is used to detect structural breaks. The tests for the random walk hypothesis follows the variance ratio methodology proposed by Lo and MacKinlay (1988). Findings & Value added: In general, we found mixed confirmation about the EMH (efficient market hypothesis). Taking into account the conclusions of the rank variance test, the random walk hypothesis was rejected in the case of stock indices: Dow Jones, SSE and PSI 20, partially rejected in the case indices: BEL 20, CAC 40, FTSTE Athex 20 and DEX 30, but accepted for indices: IBEX 35 and ISEQ. The results also show that prices do not fully reflect the information available and that changes in prices are not independent and identically distributed. This situation has consequences for investors, since some returns can be expected, creating opportunities for arbitrage and for abnormal returns, contrary to the assumptions of random walk and information efficiency.

Business companies have many kinds of products that they sell to other businesses, consumers, etc. They are a driving force of economies, especially in developing countries. The aim of this article is to analyse business companies in the Czech Republic using artificial neural networks and subsequently to estimate the development of this branch of the national economy. An analysis is performed to create a significant number of clusters of businesses. An analysis of the most significant clusters is also carried out. The result can be generalized and we can predict the number of companies that will be creditworthy or bankrupt in the following period. This makes it possible to estimate not only the overall growth or decline of business companies in the Czech Republic, but also to estimate the structure of the companies in terms of their size, turnover or volume of sales.

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Abstract.Value generators mark factors that influence the given enterprise´s success most. Thus, they refer to activities and abilities that increase profitability, decrease risk, and support the company´s growth. The aim of this contribution is to identify value generators in a building enterprise. The main presumption, however, is that the enterprise value will be measured by the EVA Equity indicator (Economic Value Added for the shareholders -owners). Data to be analysed come from the Albertina database. They include complete financial statements of building enterprises that operated on the market between 2006 and 2015. The data is organized into a table the EVA Equity in each enterprise is calculated per each year of its operation on the market. The table is subsequently imported into the Statistica software which searches for the extent to which the EVA Equity indicator is dependent on the individual items of financial statements. The result is a created adequate methodology and identification of value generators in building industries from 2006 to 2015 in the Czech Republic. The following variables are marked as the most significant: economic growth per current accounting period, equity, bank loans and bailouts, trade receivables, and current assets.

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Private equity is medium to long-term finance provided in return for an equity stake in potentially high growth unquoted companies. Private equity is capital that is not listed on a public exchange. Private equity is composed of funds and investors that directly invest in private companies, or that engage in buyouts of public companies, resulting in the delisting of public equity. Institutional and retail investors provide the capital for private equity, and the capital can be utilized to fund new technology, make acquisitions, expand working capital, and to bolster and solidify a balance sheet. Private equity investment comes primarily from institutional investors and accredited investors, who can dedicate substantial sums of money for extended time periods. In most cases, considerably long holding periods are often required for private equity investments in order to ensure a turnaround for distressed companies or to enable liquidity events such as an initial public offering or a sale to a public company. Thus, the aim of the paper is to compare the usage of the private equity by small and medium-sized enterprises in V4 countries with a focus on the accessibility and preferences. The paper is divided into several parts. The first part is devoted to the literature review of theoretical resources of the private equity term, its distribution and its usage in the region. The second part is dedicated to theoretical definition of private equity issues and concepts which are connected with private equity investments. The paper is focused on the analytical research of issue. A comparative analysis is used to compare the use of the private equity in selected countries. The correlation and regression analyses, which determine factors of influence on country attractiveness for private equity investors, were realized.

Through time series analysis, it is possible to obtain significant statistics and other necessary data characteristics. Prediction of time series allows predicting future values based on previously observed values. The exact prognosis of the time series is very important for a number of different areas, such as transport, energy, finance, economics, etc. It is within the topic of economy that the analysis and prediction of time series can also be used for exchange rates. The exchange rate itself can greatly affect the whole foreign trade. The aim of this article is therefore to analyze the exchange rate development of two currencies by analyzing time series through artificial neural networks. Experimental results show that neural networks are potentially usable and effective for exchange rate prediction.

In this paper, four groups of countries were studied using Ward's hierarchical clustering method and considering the most informative and relevant indicators of socioeconomic development, which include countries with convergent socioeconomic trends. For each of the identified clusters, based on the panel data regression analysis, we have determined the relationships between