Using Kohonen networks in the analysis of transport companies in the Czech Republic

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

Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company

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

Real estate as an investment asset

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 impact of Industry 4.0 on business results

The article aims to present the impact of Industry 4.0, which has developed worldwide as a new revolution in production and impacted manufacturing companies and changes in companies in the Czech Republic that apply it. Discussions in the engineering business emphasise the need to consider both the benefits and drawbacks of proposed modifications. In today's fast-paced environment, staying current with trends and continuing education is critical. Changes can be chaotic, affecting several industries. Businesses must change rapidly to stay caught up. The current trend is Industry 4.0, in which emphasis is placed on automation, digitisation, robotisation, artificial intelligence, etc., and this is an era of opportunities for digitalisation on a comprehensive scale. Employee change in response to new technology requires preparedness and skill development. Furthermore, business investment prioritises land and labour substitution for capital and technological equipment updates. The industry seeks to improve process quality and efficiency while promoting environmental sustainability and waste reduction in corporate production, emphasising the critical need for a comprehensive sustainable strategy. The study examines the influence of Industry 4.0 on corporate performance over five years, focusing on prospective transitions towards the new industrial age. The authors employed data collection, content analysis, and correlation approaches. The findings show varying added value patterns post-implementation, impacted mainly by firm size

The COVID-19 pandemic and the real estate market in the Czech Republic

This paper aims to identify the critical factors that influence price changes in the real estate market before, during, and after the outbreak of the COVID-19 pandemic for the period 2016-2022. The first part of the contribution deals with the literature search that examines the issue in different parts of the world. Furthermore, methods of solving the problem were chosen. Time series and correlation analysis methods were chosen for this work. The data used was selected from the areas of developments in real housing price indices, developments in real property prices, from the real index, the index of the growth rate of completed apartments in Prague, real estate price indices for territorial comparison, residential construction of family and apartment buildings, construction production, unemployment rate, rate inflation, GDP development, interest rate, and construction index. The results showed that the correlation coefficient between inflation and the price of real estate in the years 2019-2022 was around 0.8. Furthermore, the correlation coefficient between GDP and the price of sold apartments in the same period was 0.63. The relationship between GDP and construction production also plays a significant role, where the correlation coefficient was 0.69. The correlation coefficient between construction output and the interest rate was 0.4. If you can focus on the real estate market as it grew, so did the asking prices. In the first quarter before the outbreak of the COVID-19 pandemic, the average selling price was around CZK 57,900 per m2. At the end of the last quarter of 2022, prices reached an average of CZK 93,300 per m2

Perceiving of legal risk and the role of public sector in smes of v4 countries

The public sector plays an important role in the process of creating a quality of business environment. The objective of the paper was to present new scientific knowledge in the field of legal risk in the SMEs sector of the V4 countries. For this purpose, weights of selected factors of legal risk were quantified and there were compared the trends of the Czech Republic with other V4 countries. Empirical research was conducted via questionnaire survey on a sample of 1.585 respondents. On the basis of the research conducted, it can be concluded that the level of legal risk in the V4 countries was relatively high; this risk negatively affects business activities in the SMEs of the V4 countries. A major part of enterprises in the V4 countries evaluate the legal risk as unacceptable. Entrepreneurs in the V4 countries negatively evaluated the fact that the dynamics of legislative changes is too intensive and has a negative impact on their business activities. SMEs in the V4 countries considered the business environment to be too strictly regulated. On the other hand, entrepreneurs showed a high level of self-confidence in the knowledge of basic legal norms in business. Legal risk is perceived differently in individual countries. The lowest intensity of legal risk is perceived by SMEs in Hungary; the highest level of perceiving legal risk was shown in Poland. Empirical research showed that the size of enterprise and education of entrepreneurs do not have a significant impact on the formation of entrepreneurs´ attitudes in terms of legal risk. © 2021, Bucharest University of Economic Studies Publishing House. All rights reserved

Comparison of neural networks and regression time series in predicting export from Czech Republic into People´s Republic of China

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

What asset structure generates the highest possible profit for a manufacturing enterprise?

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

Investing in Real Estate in the Czech Republic and Analyzing the Dependence of Profitability and Technical and Socio-Economic Factors

This article deals with the study of the dependence between selected technical and socioeconomic factors in the real estate market that affects the return on investment. These factors include the average annual rental yield, sale/rental price for an apartment, the number of ads related to the sale/rental of apartments per 1000 inhabitants, the number of new apartment ads per 1000 inhabitants, and the share of persons facing distraint. Data from the EVAL software were used for calculation. EVAL software was developed by one of the authors of this article and allows the collecting of advertisements promoting real estate for sale and rental in the Czech Republic. This article uses data for individual districts in the Czech Republic. The article uses the methods of descriptive and mathematical statistics. The dependencies between technical and economic parameters are investigated using regression analysis. Significant dependencies were identified between the following parameters: Between selling price of an apartment and the average annual rental yield; Between the average annual rental yield and the average number of months needed to pay for the apartment; Between the average annual rental yield and the share of individuals facing distraint, and between the selling price of an apartment and the price of an apartment for rent