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Within the paper submitted, the author seeks the perspective for an investor, entrepreneur or anybody who does or plans to do business in the sector of transportation and warehousing and might need a method to determine the impact of sales and value added on earnings. The data are classified according to the CZ NACE coding for the transport and warehousing sectors. The complete data from the accounting records in the given field for the years 2016-2020 are selected. The mutual relationship of both parameters is determined using correlation. According to the results, in individual years, the correlation of both parameters and earnings differs, with each parameter showing a different impact on earnings in terms of individual years, type of company or region in which a given company operates. Overall, however, the research can be influenced by the given data, which need to be processed in more detail for possible further investigation of the given issue. The data were divided into parameters of individual years, regions, and the type of legal person.

The usage of artificial intelligence as business opportunities on the markets has a wide potential for many fields. Due to the rapid development of technology, artificial intelligence is quite expensive but useful. The research is done by making basic SWOT analysis (analysis Strenghts-Weaknesses-Opportunities-Threats) of the R&D (Research and Development) stage in the Czech Republic and by document analysis from articles on Web of Science and Scopus. The results shows, that the AI is beneficial for large number of fields and that the AI (Artificial Intelligence) has a great potential for implicating this in business, healthcare, economics and many more. The AI has potential to improve people’s life and work.

The paper deals with the the development of a specific company’s stock price time series. The aim of the paper is to use the time series method for a detailed analysis and evaluation of the development of Apple Inc. stock prices. Daily data from 2000 to 2020, daily data from the period of the economic crisis between 2007 and 2009 and daily data from the Covid-19 pandemic period from March 2020 to the end of the same year are used. The data, from the period of 2000 - 2020 show a gradual increase in Apple’s stock prices. The most common factor leading to the increase in stock prices is the launch of a new product or service on the global market. On the contrary, the reason for the decline in stock prices is customer dissatisfaction, the excess of demand over supply, or the political situation. The analysis of time series for the period of the economic crisis points to the fact that thanks to the development, innovation and constant introduction of new products into the market, the company was not significantly affected by the crisis and neither were stock prices. Naturally, there were some fluctuations in prices, but at the end of 2009, the company even reached the highest stock prices in its history to date. The analysis of time series during the global pandemic of Covid-19 shows a steady rise in stock prices. Currently, the company sells more and more products and introduces new services that help us work, study or entertain ourselves in these difficult times, in the safety of our homes.

Research background: Stock exchange trading is an activity carried out in order to achieve a profit. The oldest and largest market operator in the CR is the Prague Stock Exchange. The complex development of the market in a given period with regard to its development trends is monitored by means of the stock market index. The index of the Prague Stock Exchange is PX index. Purpose of the article: The objective of the contribution is the evaluation of the development of the PX index in the years 2018-2020 and the prediction of its further development. Methods: The data on the PX index were obtained from the official Prague Stock Exchange websites. The data are available for the period of 26 March 2018-31 March 2021. The processed data are analysed using neural networks, specifically the time series analysis. The opening price is used as a variable. Findings & Value added: The research results show that the Czech market index has been relatively stable in the past, its values being around its initial value, 1,000 points. No major fluctuations were recorded, as the PX index included very stable firms. However, with the onset of the COVID-19 pandemic, there was a sharp decline caused by the effect of anti-pandemic measures on the economy. Currently, the Czech market index is expected to grow gradually and stabilize at around 1,000 points.

The aim of business is the company value which depends on its ability to generate future profits. It means that all the future decisions must be1 adopted with regard to the influence on the company value. The subject of interest in this paper concerns forestry and logging companies, particularly financial statements of companies that were active on the Czech market in 2016 and thus showed a profit. Aim of this paper is to identify particular value generators of forestry and logging companies in the Czech Republic in 2016. For this reason, the best possible methodology was developed to determine them and the objective was eventually achieved. Using artificial neural networks, a total of 14 indicators, entering a company value creation process most frequently, were identified and measured by the EVA equity indicator. The following key variables were identified as company value generators: external sources, long-term liabilities, material and energy consumption, other operating expenses and sales of goods.

The objective of the contribution is to identify a possible relationship between the development of the price of Brent oil (Brent in USD/barrel) and the CNY / USD Exchange rate by means of artificial neural networks. Understanding future fluctuation characteristics and the trend in oil prices is the basis for a deep understanding of systemic mechanisms and trends in related research areas. However, given the complexities of oil prices, it is very difficult to obtain accurate forecasts. Within the experiment, a total of 50,000 artificial RBF neural networks were generated. Was found the CNY / USD price will play a significant role in creating China's real product. Given that it was already proven that the CNY / USD exchange depends on Brent in USD / barrel, it is important to focus the further research on finding out the time lag with which the price of Brent in USD / barrel is actually reflected in the price of CNY / USD.

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.

The present text focuses on perceiving real estate property as an investment asset that generates a certain amount of revenue to its owner, assuming expected risk and the expected level of liquidity. The first step is to determine the open market value of the selected property, which represent the expected expenses of the investment costs incurred (taking into account other acquisition costs), then we determine the open market rent value, which is the expected return on the selected property, then identify possible business risk associated with the commercial use of real estate and finally, the liquidity of the entire investment is estimated. In conclusion, methods for evaluating investments are applied to assess the realization of the investment – acquisition of the selected real estate for commercial purposes, the estimated return time and the percentage of the return on investment is calculated of the paper.

Artificial intelligence is largely used in many technical applications and allows you to provide various solutions in problem estimation, regression, or optimization. Artificial intelligence, specifically artificial neural networks, extend to the area of economics and finance. They are used primarily for operations that can´t be identified analytically. Neural networks are suitable for modelling very complex strategic decisions, for large sets of data, and so on. The main advantage is the ability to learn and then to capture hidden and strongly non-linear dependencies. In this paper they are used for the analysis of agricultural businesses. The aim is to analyse the state of the agricultural sector through the use of Kohonen networks and then to assess its future development. On the basis of the analysis, significant and large clusters of businesses are depicted, and the most significant clusters are analysed. It is possible to estimate the number of businesses that will be successful, those that will stagnate and those that will fail in the following period. Application of Kohonen networks is rather complex, but they have great potential and the results are very interesting.