Research background: Small and medium-sized businesses are significant economic power and employer in the European Union. The modern globalized world, new technologies, and advanced connectivity bring SMEs a wide range of opportunities, but also threats. Increasing the stability and competitiveness of SMEs is one of the main goals of national governments and the EU. The research is based on personal research in SMEs, its experiences and backward testing of reached results. Purpose of the article: The aim of the paper is to analyze the possibilities the potential of using controlling as a managing tool of SMEs for increased competitiveness in the context of Industry 4.0 with an emphasis on innovations. Methods: The study is based on a detailed analysis of 341 SMEs from the Czech Republic obtained in the years 2017?2019. The data were analyzed using statistical methods such Pearson correlation, stepwise regression for the purpose of determining the relationship between the controlling management system of a company, its innovation potential, level of process maturity, number of employees, internal audit, financial stability and strategic plan. Statistical analysis confirmed the close relationship of the analyzed variables and backwards experimental testing of the statistical analysis conclusions defined critical factors in the area of people in an organization, usage of advanced information systems and Industry 4.0 technologies implementation. Findings & Value added: Those important areas were determined as essential for the successful development of SMEs, as well as the most significant threats in the Industry 4.0 environment. The information obtained is useful in practice and can be applied to a more in-depth analysis of the issues. The research findings are showing possible opportunities and treads for SMEs long term stability and development as well as ways to increase enterprise performance based on controlling management system.

Cryptocurrencies are a new field of investment opportunities that has experienced a significant growth in the last decade. The crypto market was capitalized at more than USD 3000 bn, having grown from USD 10 m over the period 2011–2021. Generating high returns, investments in cryptocurrencies have also shown high levels of price volatility. By comparing the performance of cryptocurrencies (measured by the crypto index) and standard equities (included in the S&P 500 index), we found that the former has outperformed the latter 14 times over the last two years. In the present paper, we analyzed the 2012–2022 global crypto market developments and main constituents. With a focus on the top 30 cryptocurrencies and their prices, as of 9 April 2022, covering data of the two major market stress events—outbreaks of the COVID-19 pandemic (February 2020) and the Russian invasion of Ukraine (February 2022). We applied the dynamic time warping method including barycentre averaging and k-Shape clustering of time series. The use of the dynamic time warping has been essential for the preparation of data for subsequent clustering and forecasting. In addition, we compared performance of cryptocurrencies and equities. Cryptocurrency time series are rather short, sometimes involving high levels of volatility and including multiple data gaps, whereas equity time series are much longer and well-established. Identifying similarities between them allows analysts to predict crypto prices by considering the evolution of similar equity instruments and their responses to historical events and stress periods. Moreover, we tested various forecasting methods on the 30 cryptocurrencies to compare traditional econometric methods with machine learning approaches.

This article focuses on analysing the link between the rigidity of labour markets, the youth unemployment rate and the number of emigrants in the 19-30 age group within the EU in the years 2009-2020. The calculations show a very strong link (0.91-0.95) between the EPL index and the youth unemployment rate during the observed period. The nexus between youth unemployment and youth migration is confirmed as well. The linear dependence fluctuated between 0.61 and 0.65 in the same period. Furthermore, the correlation between total unemployment and total emigration reached up to 0.85 in the post-crisis period. The labour market rigidity and the EPL strictness can have, therefore, several effects; in addition to youth unemployment and youth emigration, total emigration is stimulated as well.

This paper deals with modelling income distributions in the Czech Republic in 1992–2007. The net annual income per capita for Czech households is evaluated from data based on the microcensus and the EU-SILC 2005–2008. For all analysed years the distribution of incomes was estimated in the whole sample as well as in the subgroup of households, whose heads are physicists (or experts in related sciences), architects and engineers. Inthe paper the three-parametric lognormal distribution is used as a model. Unknownparameters are estimated with the use of four methods – those of maximum likelihood, quantiles, moments and L-moments.