Financial Analysis of an Average Transport Company in the Czech Republic

The goal of this summary is to introduce shortly the financial analysis, the features typical for transport companies in general, and determine the financial characteristics of an average transport company in the Czech Republic. Financial analysis is an effective tool for the evaluation of enterprise financial efficiency. It is able to identify the strengths and weaknesses of a given enterprise. It is key tool for every transport company to measure and evaluate its efficiency. That will be helped by the balance sheet information, profit and loss statement information or cash flow statement. Ratios of profitability, activity ratios, Liquidity ratios, Debt ratios, absolute indicators, and the method of comprehensive evaluation of enterprise are used mainly. The data analysed in this contribution come from the Albertina database. It is the information of transport companies for the period of 2010 – 2014. Financial analysis of an average transport company is worked out based on the established data, and therefore the state of the future potential of transport in the Czech Republic. It can be claimed that the branch of transport is financially healthy and promising in the Czech Republic. Extension of growth, which has started already in 2011, is expected even further

Comparison of neural networks and regression time series in estimating the Czech Republic and China trade balance

Foreign trade has been and is considered to be very important. Trade balance measurement provides one of the best analyzes of a country's external economic relations. It serves as a monetary expression of economic transactions between a certain country and its foreign partners over a certain period. The aim of this paper is to compare the accuracy of time series alignment by means of regression analysis and neural networks on the example of the trade balance of the Czech Republic and the People's Republic of China. Trade balance data between the Czech Republic and the People's Republic of China is used. This is a monthly balance starting in 2000 and ending in July 2018. First, a linear regression is made followed by regression using artificial neural networks. A comparison of both methods at expert level and experience of the evaluator, the economist, is performed. Optically, the LOWESS curve appears to be best out of the linear regression and the fifth preserved RBF 1-24-1 network seems the mot appropriate out of neural networks

Comparison of neural networks and regression time series in estimating the development of the EU and the PRC trade balance

China, by GDP, is the second largest economic power, and hence also a key player in the field of international relations. As far as the EU is concerned, it is China's largest trading partner. From this point of view, it is clear that monitoring export and import development between these partners is essential. This paper therefore aims to compare two useful methods, namely the accuracy of time series alignment through regression analysis and artificial neural networks, to assess the evolution of the EU and the People's Republic of China trade balance. Data on the export and import trends of these two partners since 2000 have been used, and it is clear that the trade balance was completely different that year than it is now. The development over time is interesting. The most appropriate curve is selected from the linear regression, and from the neural networks three useful neural structures are selected. We also look at the prediction of future developments while taking into account seasonal fluctuations

Evaluation of the selected economic parameters of Czech companies and their potential for overcoming global crises during the Covid-19 pandemic

The aim of the study is to evaluate the potential of Czech companies in overcoming the pandemic crisis caused by Covid-19 on the basis of the selected economic parameters. To achieve this aim, development of Czech companies in the period of 2004-2018 has been analysed, which includes the periods before and during the financial crisis and the period of Czech economy growth. A thorough analysis of this period allows us to create an evaluation platform and formulate the assumptions as to what extent Czech companies are able to cope with another crisis caused by the Covid-19 pandemic. The research sample consists of 25,501 financially stable Czech companies of various sizes. 184,548 pieces of financial data from these companies are analysed using standard statistical methods, namely Kruskal–Wallis test. The results of the analyses show that in the case of Czech companies, the hypothesis of a significant impact of the financial crisis and the subsequent long recovery period is not confirmed. Czech companies show a relatively high resilience to the effects of the financial crisis of 2008 and the ability to overcome this crisis. Government measures to support the economy will play an important role in crisis management processes, enabling companies to survive in the worst stages of their operation and thus bridge the period of revenue losses and other negatives associated with the crisis. Based on the evaluation of the post-crisis development, we can state that Czech companies with appropriate government support will be able to overcome the negative consequences of the pandemic crisis caused by Covid-19. The results of the study have several implications for policy-making and are also beneficial for the development of a methodological platform and the creation of national and international benchmarking indicators. © Foundation of International Studies, 2021 and CSR, 2021.21/202

The impact of agreement on government procurement use on the competition in slovak healthcare sector

The efficiency of the public finance system is conditioned also by the efficiency of public procurement processes. The Slovak healthcare system has been under pressure to increase long-term efficiency. With respect to achieve the efficiency in healthcare system, the efficient public procurement is necessary condition. It is important to examine the factors influencing the public procurement system in the health sector as well as the causal relationships that would provide a valuable platform for the evaluation mechanisms aimed at the effectiveness of the planned purchases. The healthcare sector is specific because it is difficult to consider the effectiveness of the medical equipment in public procurement as well as its long-term effects, the total cost of the treatment and the individual requirements of the patient. The aim of the study is to clarify, whether the use of GPA impact the occurrence of savings within the public procurement process and if application of GPA induces the competition among tenders, thus whether the use of GPA increase number of offers. We use data on public procurement in healthcare sector in Slovak republic in 2019. The focus of analysis is on the Agreement on Government Procurement use by Slovak public procurement bodies and its impact on competition and creation of savings in public procurement process. Our findings suggest that the use of Agreement on Government Procurement induce emergence of savings in public procurement and increases the level of competition. Analysis also indicates that there exists relatively tight correspondence between competition and emergence of savings within public procurement process. It holds that higher the number of offers is, the higher savings are. © 2021, Bucharest University of Economic Studies Publishing House. All rights reserved.Agentúra na Podporu Výskumu a Vývoja, APVV: APVV-17-0360; Univerzita Tomáše Bati ve Zlín

Motivation program in small and medium-sized manufacturing enterprises based on the preference for needs

Motivation and meeting the employee needs as a part of human resource management fundamentally affect the improvement of job performance and corporate culture. The research fills in the research gap in the field of the definition of the level of motivation factors in small and medium-sized manufacturing enterprises following the preferences for their motivation needs in terms of the selected socio-demographic characteristics such as gender, age, completed education, and job position. A questionnaire was used to determine the level of motivation needs. The proposal of motivation factors as a part of the motivation programme is defined according to selected sociodemographic factors. Findings that a significant increase in motivation needs relating to finance, and work conditions occurred due to the COVID-19 pandemic can be considered a result of the study. The level of motivation does not vary in terms of gender and age. The differences are determined in terms of job position and education. The proposed motivation programme is tailored to the preferences of employees

Use of Neural Networks to Accommodate Seasonal Fluctuations When Equalizing Time Series for the CZK/RMB Exchange Rate

The global nature of the Czech economy means that quantitative knowledge of the influence of the exchange rate provides useful information for all participants in the international economy. Systematic and academic research show that the issue of estimating the Czech crown/Chinese yuan exchange rate, with consideration for seasonal fluctuations, has yet to be dealt with in detail. The aim of this contribution is to present a methodology based on neural networks that takes into consideration seasonal fluctuations when equalizing time series by using the Czech crown and Chinese yuan as examples. The analysis was conducted using daily information on the Czech crown/Chinese yuan exchange rate over a period of more than nine years. This is the equivalent of 3303 data inputs. Statistica software, version 12 by Dell Inc. was used to process the input data and, subsequently, to generate multi-layer perceptron networks and radial basis function neural networks. Two versions of neural structures were produced for regression purposes, the second of which used seasonal fluctuations as a categorical variable–year, month, day of the month and week—when the value was measured. All the generated and retained networks had the ability to equalize the analyzed time series, although the second variant demonstrated higher efficiency. The results indicate that additional variables help the equalized time series to retain order and precision. Of further interest is the finding that multi-layer perceptron networks are more efficient than radial basis function neural networks

Forecasting trade balance of Czech Republic and People´s Republic of China in equalizing time series and considering seasonal fluctuations

The objective of the contribution is to introduce a methodology for considering seasonal fluctuations in equalizing time series using artificial neural networks on the example of the Czech Republic and the People´s Republic of China trade balance. The data available is the data on monthly balance for the period between January 2000 and July 2018, that is, 223 input data. The unit is Euro. The data for the analysis are available on the World Bank web pages etc. Regression analysis is carried out using artificial neural networks. There are two types on neural networks generated, multilayer perceptron networks (MLP) and radial basis function networks (RBF). In order to achieve the optimal result, two sets of neural structures are generated. There are generated a total of 10,000 neural structures, out of which only 5 with the best characteristics are retained. Finally, the results of both groups of retained neural networks are compared. The contribution this paper brings is the involvement of variables that are able to forecast a possible seasonal fluctuation in the time series development when using artificial neural networks. Moreover, neural networks have been identified that achieve slightly better results than other networks, specifically these are the neural networks 1. MLP 13-6-1 and 3. MLP 13-8-1

Stock price development forecasting using neural networks

Stock price forecasting is highly important for the entire market economy as well as the investors themselves. However, stock prices develop in a non-linear way. It is therefore rather complicated to accurately forecast their development. A number of authors are now trying to find a suitable tool for forecasting the stock prices. One of such tools is undoubtedly artificial neural network, which have a potential of accurate forecast based even on non-linear data. The objective of this contribution is to use neural networks for forecasting the development of the ČEZ, a. s. stock prices on the Prague Stock Exchange for the next 62 trading days. The data for the forecast have been obtained from the Prague Stock Exchange database. These are final prices at the end of each trading day when the company shares were traded, starting from the beginning of the year 2012 till September 2017. The data are processed by the Statistica software, generating multiple layer perceptron (MLP) and radial basis function (RBF) networks. In total, there are 10,000 neural network structures, out of which 5 with the best characteristics are retained. Using statistical interpretation of the results obtained, it was found that all retained networks are applicable in practice

An Analysis of Correlation and Forecast of CZK to EUR in the Unstable Global World

Research background: In the Czech Republic, the CZK/EUR currency pair is the most observed one not only by Czech consumers but also by Czech and foreign companies. Not only the political situation in the world and the foreign investors but also other variables have a significant influence on the CZK. Purpose of the article: The aim of this paper is to analyse the development of the CZK/EUR exchange rate, to perform a correlation analysis of the CZK/EUR currency pair and to forecast its future development in today’s unstable global world affected by the Coronavirus pandemics. Methods: In order to fulfil the aim of the paper, the data of the CZK/EUR currency pair from the beginning of the year 1999 to mid-June 2020 was used. The correlation analysis and the forecast of the future development of the exchange rate are performed by means of the, in today’s globalized world very promising, technology of artificial neural networks. Findings & Value added: The analysis and the forecast of the exchange rate development is based on the time series model taking the previous value of the exchange rate and its previous volatility into consideration. The strongest propulsion power for the development of the CZK/EUR exchange rate will be the market atmosphere on the world’s markets and the associated capital transfer between risk assets and safe havens. Provided, that the current situation settled down definitively and the economy returned back to its normal state, it is probable that the Czech koruna would gain some part of its significant loss back within a medium-term period