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

Estimation of the development of the Euro to Chinese Yuan exchange rate using artificial neural networks

The exchange rate is one of the most monitored economic variables, from the position of individual citizens or economists, financial institutions or entrepreneurs. In the long run, it is a reflection of the condition of the economy, and in the short and medium term it has a significant impact on the economy. The time series of currency development maps past developments, current status, and is also able to predict future developments. This article analyzes the time series of the development of EUR to Yuan exchange rate using artificial intelligence. It aims to evaluate this development and to indicate the prediction of the future development of EUR to Yuan

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 effectiveness of designed and implemented land consolidation

The thesis deals with the state of the territory in the Českokrumlovsko-region before the land consolidation and the design and implementation state. As model land consolidation, the comprehensive land consolidation of Přídolí was chosen with a detailed examination of the plan of the common facilities. Individual measures in the plan of the common facilities were studied in the design, evaluated in the terrain and possibly supplemented by further proposals. The reconnaissance showed that the elements suggested in the selected land consolidation do not correspond with the real state. The acquired results were generalized for the projection of the comprehensive land consolidation. The problem turned out to be a fact, that the most suggested measures remain unimplemented

The use of neural networks to determine value based drivers for SMEs operating in the rural areas of the Czech Republic

Research background: In the past, the main objective of a company was to generate sufficient profit. Nowadays, a company must seek to achieve much broader objectives. To be successful in this pursuit, it must not only measure financial performance, but also monitor internal and external developments, increase shareholders' wealth and protect the interests of other stakeholders, i.e. to analyze and act on those factors that affect company value. Purpose of the article: The objective of the contribution is to determine through the use of artificial neural networks the relationship between business value drivers, or value based drivers (VBD), and EVA Equity, which is economic value added (EVA), of small and medium-sized enterprises operating in the rural areas of the Czech Republic. Methods: The data was obtained from the Bisnode´s Albertina database. The data set consists of the profit and loss accounts for 2013 to 2017 of small and medium-sized enterprises operating in rural areas of the Czech Republic. Two scenarios are analyzed. In the first, the independent variables are only the value drivers, whereas in the second, company location (region) is included. The objective is to find the dependence of EVA Equity on individual VBD and company location. A sensitivity analysis is conducted, on the basis of which the importance of individual value drivers and company location is determined. Findings & Value added: The output is a set of value drivers, which could be used by company managers to regulate the growth of EVA Equity, i.e. value for shareholders. The findings reveal that the difference between successful and unsuccessful companies is determined by the level of involvement of human capital; companies use a large number of substitutes for factors of production, whereby the involvement of borrowed capital is likely to cause a positive financial leverage effect

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

Considering seasonal fluctuations on balancing time series with the use of artificial neural networks when forecasting US imports from the PRC

The paper’s objective is to propose a particular methodology to be used to regard seasonal fluctuations on balancing time series while using artificial neural networks based on the example of imports from the People's Republic of China (PRC) to the USA (US). The difficulty of forecasting the volume of foreign trade is usually given by the limitations of many conventional forecasting models. For the improvement of forecasting it is necessary to propose an approach that would hybridize econometric models and artificial intelligence models. Data for an analysis to be conducted are available on the World Bank website, etc. Information on US imports from the PRC will be used. Each forecast is given by a certain degree of probability which it will be fulfilled with. Although it appeared before the experiment that there was no reason to include the categorical variable to reflect seasonal fluctuations of the USA imports from the PRC, the assumption was not correct. An additional variable, in the form of monthly value measurements, brought greater order and accuracy to the balanced time series

The applicability of FCFF method evaluating an enterprise of Real Estate segment

The article aims at evaluating a specific enterprise of the Real Estate segment using FCFF (Free Cash Flow to Firm) method. This technique determines the company’s value through free cash flows. Enterprise valuation presents a distinct discipline requiring appraiser’s deep understanding not only of the evaluated enterprise but also other external decisive influences. The theoretical part focuses on calculation procedures using The CAPM (Capital Asset Pricing Model) model quantifying separate variables that determine discount rates. The suggested technique deals with specific financial data of the company and is applicable in evaluating small and medium-sized enterprises

Estimation of the development of the Euro to Chinese Yuan exchange rate using artificial neural networks

The exchange rate is one of the most monitored economic variables, from the position of individual citizens or economists, financial institutions or entrepreneurs. In the long run, it is a reflection of the condition of the economy, and in the short and medium term it has a significant impact on the economy. The time series of currency development maps past developments, current status, and is also able to predict future developments. This article analyzes the time series of the development of EUR to Yuan exchange rate using artificial intelligence. It aims to evaluate this development and to indicate the prediction of the future development of EUR to Yuan

Prediction of stock price developments using the Box-Jenkins method

Stock prices develop in a non-linear way. Naturally, the stock price prediction is one of the most important issues at stock markets. Therefore, a variety of methods and technologies is devoted to the prediction of these prices. The present article predicts the future development of the stock price of ČEZ, a. s., on the Prague Stock Exchange using the ARIMA method - the Box-Jenkins method. The analysis employs the final price of the last trading day in a given month, from February 2012 to September 2017. The data come from the Prague Stock Exchange database. Statistica software is used for processing the data, namely advanced time series prediction methods, the ARIMA tool, and autocorrelation functions. First, the current stock development of ČEZ, a.s., was graphically evaluated, and this was followed by a stock price prediction for the next 60 days in which the shares would be traded. Lastly, the prediction residues were analysed. It was confirmed that the calculation was done correctly, but with little accuracy. The conclusion is an assertion that the Box-Jenkins method is not a suitable tool for prediction