What is the situation of the transport sector in the Czech Republic and what is its importance for the economy of the Czech Republic? How and to what extent do businesses operating in this sector influence the sector as such, and how many businesses in this sector have such influence? Additionally, what happens if the most important businesses in the transport sector go bankrupt, and which businesses are the most important ones? Searching for the answers to these questions is a subject of this contribution, focusing primarily on the cluster analysis using artificial neural networks (ANN), specifically with Kohonen networks, which represent the main method for processing a large volume of not only accounting data on transport companies. In this research, the dataset consists of the financial statements of transport companies for the years 2015–2018. The research part of the contribution deals mainly with the issue of the transport sector’s development in the years 2015–2018 with the companies operating in this sector and tries to identify the most important companies in terms of their importance for this sector. The results show that the whole transport sector is influenced mainly by the two largest companies, whose potential changes can affect companies themselves but to a great extent also the development of the whole transport sector. For the two companies, financial analysis is carried out using ratios, whose results show that despite the negative values of the important value generators of one of these companies, the company is still able to significantly influence the situation in the transport sector of the CR. This information is a clear guide for experts, development analysts, to determine the further development of the whole sector when focusing on the development of the two specific companies only. A question arises as to how the created model can be applied to other economic sectors, especially in other EU countries.

On the basis of the time series data, machine learning can also be used for predicting the future development of export in various states. It offers, of course, to measure trade between the world´s two largest economies – China and the USA, which has an impact on the global world economy. Therefore, the objective of this contribution is to predict the USA export to the People´s Republic of China in the context of mutual sanctions using machine learning. The data set contains monthly data on the development of the USA export to China between January 2000 and July 2019. Regression is carried out using neural networks. There are generated three sets of multilayer perceptron networks considering the time series lag of 1 month, 5 months, and 10 months. A total of 10,000 neural structures are generated, out of which 5 with the best characteristics are retained. Export values between August 2019 and December 2020 are predicted and subsequently, the results of all three experiments are compared. The result closes to the ideal one is with the time series lag of 10 months; the networks are also able to capture the trend and fluctuations of the time series. Yet, there is certain overfitting notable, mainly due to the gradation of mutual trade war between the USA and the PRC.

Social media platforms and channels are being increasingly used nowadays, influencing almost all types of businesses. Social media are mainly used as a source of information and means of dissemination of knowledge that may change the opinions of people. They can be considered very important business marketing strategies since they participate significantly in creating new business opportunities, building a stronger position in the market, or in the transformation of consumer behaviour. The main goal of the paper is to determine which social networks are most commonly used in business and their purpose. To achieve the goal, the method of questionnaire survey was used, in which a total of 237 respondents participated. The results of the survey show that business entities mostly use Instagram in their business activities, mainly for promoting their company, which was followed by Facebook, through which they sell their products or attract new employees. In addition, companies also use social networks as a communication channel with employees or customers.

The topic of optimizing capital structure is very important for a company to work efficiently and reliably. It is important for every company to optimize everything so that they have the highest possible efficiency. Entrepreneurs also try to make this optimization last them as long as possible. Therefore, the aim of this paper is to determine the optimal capital structure of an agricultural company operating in the Czech Republic. The base source of data is the closing data of individual agricultural companies from the Albertina database of Bisnode. More than 9,000 agricultural enterprises operating in the given sector of the national economy in the Czech Republic are recorded in this data set. The calculation of the WACC method and the level of debt of individual agricultural companies are used. Subsequently, the equation for calculating the cost of capital is determined using power regression. Here we also obtain the value of reliability, which in this particular case is not ideal, but still reasonable. A line chart is used to determine the optimal interval for the agricultural company. The optimal debt interval comes out to 20 to 25%, at which the cost of capital is declared to be from 22 to 24%. If agricultural companies had higher or lower debt ratios, it would be inefficient for the enterprise.

The issue of unemployment set in the context of economic development is a crucial issue for ensuring economic growth of a country. The aim of the text is to analyze the relationship between unemployment and basic macroeconomic indicators and to confirm the direct relationship between these variables. The research method used was the analysis of the values of the general unemployment rate, gross domestic product, i.e. GDP, and gross value added, i.e. GVA, and their subsequent comparison. Other research methods include correlation analysis with Pearson’s correlation coefficient, which confirmed a high degree of correlation between the general unemployment rate and the values of GDP and GVA. The research confirmed the existence of a correlation between the general unemployment rate and both GDP and GVA. GDP and GVA in the Moravian-Silesian Region were increasing in the sample years 2013-2019, while the general unemployment rate was decreasing. The analysis of the values shows that the values of GDP and GVA were inversely proportional to the values of the general unemployment rate. A limitation of the research was that economic development is not only influenced by the unemployment rate. As a result, it was not possible to establish a precise value that would define the extent of the impact of unemployment on the economic development of the region. The contribution of the paper is the confirmation of the existence of the correlation of unemployment with the mentioned development indicators, and the validity of Okun’s Law also on the territory of the Moravian-Silesian Region.