Research background: Covid-19 pandemic had a strong impact on the economy and capital market. In times of crisis, it is important for investors to be able to diversify their investment portfolio in order to mitigate risk. However, the growing trend towards capital market integration may make it ineffective. Research on financial integration, during the Covid-19 period, has started to develop, mainly in major global capital markets. It is, therefore, important to extend this research to other capital markets. The purpose of the article: This contribution aims to analyze financial integration in the stock indexes of the capital markets of Austria (ATX), Slovenia (SBITOP), Hungary (BUDAPEST SE), Lithuania (OMX VILNIUS), Poland (WIG), the Czech Republic (PX PRAGUE), Russia (MOEX) and Serbia (BELEX 15), in the context of the global pandemic (COVID-19). Methods: To measure the unit roots in the time series, we used ADF, PP, and KPSS tests, and Clemente et al. (1998) test to detect structural breaks. To ana-lyse financial integration, we applied the Gregory and Hansen integration test, and to validate the robustness of results, we use the impulse-response function (IRF) methodology, with Monte Carlo simulations, as they provide a dynamic analysis generated from the VAR model estimates. Findings & Value added: The results suggest very significant levels of integration, which decreases the chances of portfolio diversification in the long-term. Evidence shows 47 pairs of integrated stock market indexes (out of 56 possible). The stock indexes ATX, BUDAPESTE SE, BELEX 15 show financial integration with all other indexes. On the contrary, the index of OMX VILNIUS shows only 3 integrations. Results also show that most of the significant structural breaks occurred in March 2020. The analysis of the relationship between markets, in the short term, shows positive/negative co-movements, with statis-tical significance and with a persistence longer than one week.

Accurate stock price prediction is very difficult in today's economy. Accurate prediction plays an important role in helping investors improve return on equity. As a result, a number of new approaches and technologies have logically evolved in recent years to predict stock prices. One is also the method of artificial neural networks, which have many advantages over conventional methods. The aim of this paper is to compare a method of exponential time series alignment and time series alignment using artificial neural networks as tools for predicting future stock price developments on the example of the company Unipetrol. Time series alignment is performed using artificial neural networks, exponential alignment of time series, and then a comparison of time series of predictions of future stock price trends predicted using the most successful neural network and price prediction calculated by exponential time series alignment is performed. Predictions for 62 business days were obtained. The realistic picture of further possible development is surprisingly given based on the exponential alignment of time series.

Accurate prediction of stock market values is a challenging task for over decades. Prediction of stock prices is associated with numerous benefits including but not limited to helping investors make wise decisions to accumulate profits. The development of the share price is a dynamic and nonlinear process affected by several factors. What is interesting is the unpredictability of share prices due to the global financial crisis. However, classical methods are no longer sufficient for the application of share price development prediction.However, over-relying on prediction data can lead to losses in the case of software malfunction. This paper aims to innovate the prediction management when predicting the share price development over time by the use of neural networks. For the contribution, the data on the prices of CEZ, a.s. shares obtained from the Prague Stock Exchange database. The stock price data are available for the period 2012-2017. In the case of Statistica software, the multilayer perceptron networks (MLP) and the radial basis function networks (RBF) are generated. In the case of Matlab software, the Support Vector Regression (SVR) and the Back-Propagation Neural Network (BPNN) are generated. The networks with the best characteristics are retained and based on the statistical interpretation of the results, and all are applicable in practice. In all data sets, MLP networks show stable performance better than in the case of SVR and BPNN networks. As for the final assessment, the deviation of 2.26% occurs in the most significant differential of the maximal and the minimal prediction. It is not necessarily significant regarding the price of one stock. However, in the case of purchasing or selling a large number of stocks, the difference may seem significant. Therefore, in practice, the application of two networks is recommended: MLP 1-2-1 and MLP 1-5-1. The first network always represents a pessimistic, minimal prediction. The second one of the recommended networks is an optimistic, maximal prediction. The actual situation should correspond to the interval of the difference between the optimistic and pessimistic prediction. Keywords: Statistica software, Matlab software, stock price development, neural networks, prediction.

This paper focuses on working from home during the COVID-19 pandemic. It focuses mainly on the advantages and disadvantages of this way of working, as well as its impact on the psyche and performance of employees and, last but not least, its impact on a company's finances. It uses the synthesis of data found on the internet, from selected reliable sources dealing with the same or similar issues. From these sources it is evident that the effects of working from home are rather negative. The most frequently mentioned advantage is the saving of time, caused mainly by the absence of commuting to the office. The most frequently mentioned disadvantage is the absence of personal contact with co-workers. The disadvantage that a company could feel the most is the extension of the length of communication between employees, and thus the extension of work processes. At first glance, it might seem that a company will save money using this form of work, but the reality is that the company's costs may even increase. This is due to the fact that most companies have chosen to work from home only partly, so the costs of running the offices remain the same or slightly reduced at best, and legislation states that the company must reimburse workers for costs incurred by this form of work. It follows from this contribution that, if possible, workers and employers should avoid the practice of working from home, even though it has a positive effect on the pandemic. The potential for further research could be to compare the results of this work with the same research conducted outside of the pandemic.

Research background: Globalisation and the development of technology introduce new requirements for effective business management. Every business must constantly adapt to the environment, analyse and know its competitors and its customers’ requirements, and meet other stakeholders’ commitments. An unsuccessful business will go into liquidation. The intention of any business should be not only to avoid this situation, but to thrive and prosper and create value for its shareholders. Purpose of the paper: The aim of this study is to propose an appropriate tool for cluster analysis and determine the ability of a business to survive a potential financial distress. Methods: Details from financial statements of construction companies operating in the period 2015-2019 in the Czech Republic are analysed. Attention is mainly directed to items that represent the capital and asset structures of a company, liquid assets, and the ability to generate sales and profit. Artificial neural networks in the form of Kohonen networks are used for the purpose of cluster analysis. Financial analysis is used to examine the underlying dataset as well as for a detailed analysis of selected clusters, i.e. the contribution margin and ratio indicators. Findings & Value added: The basic analysis clearly shows that companies in liquidation attempt to reduce the value of inventories and engage additional foreign capital with a view to survival, while there is a certain solidarity between companies’ key persons. Cluster analysis using Kohonen networks is quite successful. The present methodology and approach can still be applied to the design of an enterprise decision support tool. Further research may study whether the representation of businesses in the different clusters will change over time, or whether the development of the construction industry can indeed be predicted based on an analysis of the leaders.