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The objective of the paper is to determine the most suitable operational redundant assets and asset structure for a manufacturing enterprise. They are determined by the financial analysis of ratios, vertical and horizontal analysis, and the calculation of Altman Z-score. The recommended sizes of activity ratios are: the turnover of total assets should be equal to the value of 1, the time of inventory turnover should be as short as possible, the turnover of inventories should be as large as possible, the time of debt collection should be the shortest possible, the due date of fulfilling obligations should be equal to 2 or higher. The ratios are compared with the sector and the position of enterprise is assessed. The limit of the research is the incapability to determine the best asset structure, but only the best possible one, on the basis of comparing with the sector that sets the bar in a specific field. Every enterprise aspiring to be competitive should meet the sector standards, at least.

The presented paper explores the link between firm size and returns on sales (RoS) in Czech building companies. We examined how different business sizes relate to the return on sales, considering staff numbers and the RoS movement over a period. The CRIBIS database of CRIF Company - Czech Credit Bureau provided relevant data from 2016 to 2020, including profitable and active building enterprises. The industry analysis involves an empirical approach using industry structure analysis, return on sales analysis and observing its movement from 2016 to 2020. The industry predominantly encompasses small firms of fewer than 25 employees, exhibiting dramatic variations of the average return on sales over the period. Large companies exceeding 250 employees show complete conformity, yet their numbers are small in the construction sector. The correlation analysis revealed that the business size, i.e., staff numbers, is independent of the return on sales. The submitted work suggests a new size classification according to total assets and sales.

The article deals with the description of the methodology for determining the prices of construction work used most often in the reconstruction of historic buildings. The procedure is based on parametric estimating of buildings and on traditional tools and principles of pricing. The historic building is divided into structural parts and technological units. Structural elements are individually valued at the unit price, which was created on the basis of sub-budget. Very strict rules are set for the financing of cultural monuments and the method of valuation is based on the price systems’ methodology. The research also included an analysis of market prices and a subsequent comparison with indicative prices. Proper estimate of historic buildings’ reconstruction cost is also an important input for the decision-making process regarding the actual implementation of the project, its method of execution and its life cycle cost determination.

The aim of the paper is an application of the basic principles in determining rental housing prices and factors that may affect them. In the experimental part, an analysis of rental housing in the city of Brno is performed for the 2020 – 2021 period affected by the covid-19 pandemic. The analysis is processed for individual city districts and divided according to apartment layout. Finally, all outputs are compared and the real estate market development is determined with a focus on the biggest changes and their occurrence.

The Phillips curve was supposed to mean an expansion of the doctrine based on the original regulatory ideas of J. M. Keynes. At the time of its inception (1950s), it gave governments theoretical hope, coming from the possibility of choosing a negative correlation between the price level (P) and the product (Y). Her early denial (at least in the short term) by Milton Friedman, on the other hand, has not changed anything about other applications that are still relevant until present time. In the fact, advantage of Phillips curve is her ability based on broad-spectrum use for any type of national economy. The aim of this work is to determine the shape of the Phillips curve for the Czech Republic in the period from 2000 to the present and to compare its shape with the shape of the original Phillips curve. The method of regression analysis is used here, comparison and prediction are performed using time series. In this paper, we find out what the short-term Phillips curve looks like for the Czech Republic, that it does not coincide with the original Phillips curve, and that in the future we can count on a growing correlation between inflation and unemployment.

The article deals with the description of the methodology for calculating the reproduction value of buildings. It is a mathematical model that uses data obtained from a statistical survey and calculates the total reproduction value of buildings from a large data set. The model works with many variable inputs that describe the valued building. These include, for example, the use of the building, the enclosed space of the building, the floor area of the building, the building material of the supporting structures of the building, the age of the building, the technical condition of the building, the year the building was acquired and others. Based on the results, the model was calibrated and its parameters optimized to achieve the most accurate values.

Research background: In the past, some studies proved that the development of a currency exchange rate predicts the development of the whole national economy. The monetary market overtakes the development of the actual economy for a few months. Does this apply also in the case of the Czech koruna, in the era of the global Coronavirus pandemics and in the world affected by the pandemics? Purpose of the article: The main objective is to analyze a dependence of the Czech koruna (CZK) to Euro (EUR) exchange rate development on gross domestic product of the Czech Republic in the conditions of an expected crisis. Methods: The data used of the analysis are represented by the information about the CZK and EUR exchange rate from the beginning of 1999 to the 15th June 2020 and by the quarterly development of the Czech GDP. To measure the dependence and predict the development of the GDP based on the CZK exchange rate development, the method of AI is used, namely the regression analysis using the artificial neural networks. Findings & Value added: The effect of EUR/CZK on GDP can be quantified reaching around 31%. It is assumed that the GDP will fall significantly in 2020 with a certain growth only being possibly expected in 2021 (even more significantly in the second quarter of 2021). Due to the GDP development, the development of the EUR/CZK could then be forecasted as well.

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.

The People´s Republic of China is one of the largest, but also the most demanding markets in the world. The trade is limited by a number of barriers, strong competition and unusual environment for trades from other parts of the world. Despite those limitations, Czech exporters are able to establish themselves in the Chinese market, exporting mainly machines and vehicles. To predict future export trends is very difficult; however, these predictions can be crucial not only for individual exporters but also for the whole national economy. For predictions, economists use causal, intuitive or statistical methods. The objective of the contribution is to compare the accuracy of equalizing time series by means of regression analysis and artificial neural networks for a possible prediction of future export trends on the example of the Czech Republic export to the People´s Republic of China. For the purposes of analysis by means of statistical methods, the data obtained from monthly statements from the period starting from the year 2000 and ending in July 2018. First, a linear regression is carried out and subsequently, neural networks are used for regression. Finally, the results are compared. It appeared that in practice, mainly all retained neural networks are applicable. However, the first of them showed significant deviations within a very short period of time.

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.

The transport sector has a significant impact on the performance of the Czech economy. Transport companies, of course, have their own specificities, whether they deal with ecology or the financial and economic situation. It is precisely the economic position of a transport company that needs to be analysed in order to identify the need for change, to predict the further development of such company. For analysis, a variety of methods is used, of which artificial neural networks are a very interesting and effective tool. The aim of this paper is to make a cluster analysis of transport companies operating in the Czech Republic based on this tool. The data of the financial statements of transport companies in the Czech Republic in 2016 are taken into account. Only some items from the financial statements are selected for analysis. The file is then subjected to a cluster analysis, specifically using the Kohonen networks – Statistica software. In accordance with the methodology of the contribution, the data is divided into three sets - training, testing and validation. Companies were divided into clusters in the 10x10 Kohonen Map. Some clusters are significant in terms of number of companies. These clusters are further analysed. Specific conclusions are made: A larger company generates, on average, a higher operating profit, larger companies achieve higher ROE and, in the case of a larger company, the financial leverage acts more positively.

The article explores the effects of Industry 4.0 on business performance in Czech manufacturing companies, focusing on how this technological revolution influences company value and economic results. Topics discussed include the integration of automation and digital technologies, the impact on process efficiency and environmental sustainability, and the challenges associated with employee skill development and investment in new technologies.

The article examines the impact of the COVID-19 pandemic on the real estate market in the Czech Republic, focusing on price changes from 2016 to 2022. Topics discussed include the correlation between inflation and real estate prices, the relationship between Gross Domestic Product (GDP) and construction production, and the shift in demand from urban to suburban areas during the pandemic.

Accurate Brent Crude Oil price prediction is essential for informed decision-making and risk control in the global energy market. This paper examines the dynamics of Brent Crude Oil prices over the previous 35 years, utilizing predictive modelling techniques to project future prices and analyzing historical trends and relationships with the GDPs of major economies. The statistical methods used in this paper include the moving average and Pearson correlation coefficient. While correlation studies show significant positive links between Brent Crude Oil prices and the GDPs of major economies like China, Saudi Arabia, Europe, Russia, and the United States, historical data analysis reveals large price volatility. Highly accurate price forecasting using LSTM (Long Short-Term Memory) modelling approaches provides detailed risk management and decision-making information in the global energy market. The application of the Neural Network and the ARIMA model shows an increase in the price of Brent crude oil in the next year, 2024. Identifying the importance of the Brent crude oil price forecast and its effect on the international market is highly needed. This paper thus might help to increase economic growth. These results highlight the importance of advanced modelling techniques and economic indicators in managing oil price changes and help make informed decisions in the energy industry. [ABSTRACT FROM AUTHOR]

Making investment decisions in modern conditions is quite complicated. On the example of the construction industry, the article tests the approach based on WACC and WARA analyses. The risk and profitability of the specified markets were analyzed based on the Efficient Frontier method. The Polish market and the group of Euronext markets were selected for comparison. The necessity of calculation of WARA based on only significant assets was determined. The differentiation between WARA and WACC in the Polish construction market can be overcome by maximizing goodwill and intangible assets and minimizing tangible assets. It reveals problems not in the stock market, but in the industry and the economy as a whole, particularly the lack of functioning markets such as the stock market for individual assets. The role of intangibles is different for Euronext markets. On the other hand, the requirement of the international accounting standards to transfer goodwill from assets to "other income" in case of a negative value (bad will) makes adequate consideration of goodwill in the WARA model virtually impossible. Another problem is a calculation of relative weights of assets in conditions of occurrence of both positive and negative values, which creates prerequisites for different approaches and interpretations. Building material that is used in the construction industry tries to improve the environment. the source of building materials are natural resources such as sandstone. [ABSTRACT FROM AUTHOR]

The aim of the paper is to determine the actual cost of equity capital in the accommodation and food service activites sector in the Czech Republic between 2015 and 2019. The data was collected from the Albertina database and processed based on changes in retained earnings from previous years and the current financial year's profit. The results show the resulting cost of equity capital, which in the years under consideration averaged between 32.40 - 67.25% and the median values ranged between 11.78 - 22.72%. The contribution of this paper can be seen in bringing the resulting value of the actual cost of equity capital in the selected industry on which subsequent predictions for the future can be based. The limitation of the research can be seen in the use of older data. [ABSTRACT FROM AUTHOR]