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Jaromir Vrbka
Institute of Technology and Business, School of Expertness and Valuation, Okruzni 517/10, 37001 Ceske Budejovice, Czech Republic

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Conference paper
Published: 14 January 2021 in SHS Web of Conferences
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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.

ACS Style

Jaromír Vrbka; Pavla Vitková. The applicability of FCFF method evaluating an enterprise of Real Estate segment. SHS Web of Conferences 2021, 91, 01042 .

AMA Style

Jaromír Vrbka, Pavla Vitková. The applicability of FCFF method evaluating an enterprise of Real Estate segment. SHS Web of Conferences. 2021; 91 ():01042.

Chicago/Turabian Style

Jaromír Vrbka; Pavla Vitková. 2021. "The applicability of FCFF method evaluating an enterprise of Real Estate segment." SHS Web of Conferences 91, no. : 01042.

Conference paper
Published: 13 January 2021 in SHS Web of Conferences
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Research background: China’s share in the global economy has experienced a swift growth since opening up and reforming the country’s foreign policy in 1978. USA sanction on China has so far concentrated on a heap of issues including China’s enormous exchange shortfall with the U.S., currency control, constrained market access, licensed innovation robbery and security issues identified with Huawei. Also, USA sanction on China has so far lead to a decrease in exports and outflow of FDI, reduce in the inflow trade and investment, and apparently hinders the Chinese GPD growth and diminished its currency exchange rate. Purpose of the article: The aim is to predict the future development of the GDP of the China and the USA and to estimate their further development through the prism of mutual trade sanctions and COVID-19. Methods: The data collection demonstrates the course of a time series of a daily RMB exchange rate development from the beginning of 1992 to June 2020. Furthermore, it represents the time series of a quarterly development of the Chinese GDP for the same time period. Using neural networks, a regression for different variants of the time series delay in connection with the analysis of the USA sanctions is conducted. Findings & Value added: The GDP of both countries has developed over the last two years, as if sanctions had not been imposed. However, the situation is changing with COVID-19. In this case, it is clear that the impact will be more significant. US GDP will stagnate. PRC GDP will fall.

ACS Style

Petr Šuleř; Jaromír Vrbka. GDP Development of China and USA in terms of mutual sanctions and COVID-19. SHS Web of Conferences 2021, 92, 07061 .

AMA Style

Petr Šuleř, Jaromír Vrbka. GDP Development of China and USA in terms of mutual sanctions and COVID-19. SHS Web of Conferences. 2021; 92 ():07061.

Chicago/Turabian Style

Petr Šuleř; Jaromír Vrbka. 2021. "GDP Development of China and USA in terms of mutual sanctions and COVID-19." SHS Web of Conferences 92, no. : 07061.

Conference paper
Published: 10 December 2020 in IOP Conference Series: Materials Science and Engineering
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During its existence, each property requires specific construction operations to extend its useful life. Such construction operations and related investment are usually carried out gradually in dependence on the durability of the building materials used. To determine the overall amount of investment, a synthesis of selected valuation methods is used in this contribution. The aim of the synthesis is to achieve the most accurate result of the investment made. In selected methods used, the value of the investment made is correlated using coefficient to the price level of the investment year. The resulting investment value determined by means of the cost pricing synthesis methodology provides more accurate results than the application of a specific valuation method for the entire set of items priced.

ACS Style

Jaromír Vrbka; Tomáš Krulický; Tomáš Brabenec. Determining the amount of past investment property using the synthesis of cost evaluation methods. IOP Conference Series: Materials Science and Engineering 2020, 960, 042102 .

AMA Style

Jaromír Vrbka, Tomáš Krulický, Tomáš Brabenec. Determining the amount of past investment property using the synthesis of cost evaluation methods. IOP Conference Series: Materials Science and Engineering. 2020; 960 (4):042102.

Chicago/Turabian Style

Jaromír Vrbka; Tomáš Krulický; Tomáš Brabenec. 2020. "Determining the amount of past investment property using the synthesis of cost evaluation methods." IOP Conference Series: Materials Science and Engineering 960, no. 4: 042102.

Conference paper
Published: 11 October 2020 in Inventive Computation and Information Technologies
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The aim of this paper is to express the enterprise value for the purpose of a future comparison of the investment revenue in form of a business merger. To fulfil this aim, the model of discounted cash flow (DCF) is employed that is, according to reference literature, considered to be the key method of enterprise valuation, namely in the form of free cash flow to firm (FCFF). The predicted values of free cash flow to creditors and shareholders are discounted using the weighted average cost of capital to their current value for the respective years in the time period from 2018 to 2032 and for the period of perpetuity. Apart from the final enterprise value, it was pointed to not only the limits of the future cash flow prediction but also to vast opportunities for using the predicted values for financial planning of the company in the future.

ACS Style

H. Květová; J. Vrbka; P. Šuleř. Enterprise Value Assessment as an Evaluation Criterion of a Merger. Inventive Computation and Information Technologies 2020, 856 -863.

AMA Style

H. Květová, J. Vrbka, P. Šuleř. Enterprise Value Assessment as an Evaluation Criterion of a Merger. Inventive Computation and Information Technologies. 2020; ():856-863.

Chicago/Turabian Style

H. Květová; J. Vrbka; P. Šuleř. 2020. "Enterprise Value Assessment as an Evaluation Criterion of a Merger." Inventive Computation and Information Technologies , no. : 856-863.

Journal article
Published: 17 September 2020 in Sustainability
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The reconstruction of buildings generally prolongs their useful life, increases their utility value, and last but not least, leads to an increase in their value. These assumptions only apply if an independent third party reaches the same conclusion together with the owner. However, the undesirable effect of the reconstruction of a building may be a decrease in its value. The aim of this contribution is to determine the change in value of an older sample building assessed in the included case study as a result of its reconstruction. Valuation methods are applied, which, as it turns out, reveal the inaccuracy of the subjective view of the person who reconstructed the building. The resulting change in the value of the sample building is discussed from the point of view of the applied valuation methods and other value-creating aspects (subjective view of the owner on the value of the building, historical value of the building, etc.). The contribution concludes with recommendations for maximizing the increase in value of a property through its reconstruction so as to eliminate the risk of a decrease in its value.

ACS Style

Jaromir Vrbka; Tomas Krulicky; Tomas Brabenec; Jan Hejda. Determining the Increase in a Building’s Appreciation Rate Due to a Reconstruction. Sustainability 2020, 12, 7690 .

AMA Style

Jaromir Vrbka, Tomas Krulicky, Tomas Brabenec, Jan Hejda. Determining the Increase in a Building’s Appreciation Rate Due to a Reconstruction. Sustainability. 2020; 12 (18):7690.

Chicago/Turabian Style

Jaromir Vrbka; Tomas Krulicky; Tomas Brabenec; Jan Hejda. 2020. "Determining the Increase in a Building’s Appreciation Rate Due to a Reconstruction." Sustainability 12, no. 18: 7690.

Journal article
Published: 12 September 2020 in Sustainability
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There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.

ACS Style

Marek Vochozka; Jaromir Vrbka; Petr Suler. Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM. Sustainability 2020, 12, 7529 .

AMA Style

Marek Vochozka, Jaromir Vrbka, Petr Suler. Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM. Sustainability. 2020; 12 (18):7529.

Chicago/Turabian Style

Marek Vochozka; Jaromir Vrbka; Petr Suler. 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM." Sustainability 12, no. 18: 7529.

Journal article
Published: 22 June 2020 in Oeconomia Copernicana
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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.

ACS Style

Jaromír Vrbka. The use of neural networks to determine value based drivers for SMEs operating in the rural areas of the Czech Republic. Oeconomia Copernicana 2020, 11, 325 -346.

AMA Style

Jaromír Vrbka. The use of neural networks to determine value based drivers for SMEs operating in the rural areas of the Czech Republic. Oeconomia Copernicana. 2020; 11 (2):325-346.

Chicago/Turabian Style

Jaromír Vrbka. 2020. "The use of neural networks to determine value based drivers for SMEs operating in the rural areas of the Czech Republic." Oeconomia Copernicana 11, no. 2: 325-346.

Journal article
Published: 24 March 2020 in Journal of Risk and Financial Management
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Bankruptcy prediction is always a topical issue. The activities of all business entities are directly or indirectly affected by various external and internal factors that may influence a company in insolvency and lead to bankruptcy. It is important to find a suitable tool to assess the future development of any company in the market. The objective of this paper is to create a model for predicting potential bankruptcy of companies using suitable classification methods, namely Support Vector Machine and artificial neural networks, and to evaluate the results of the methods used. The data (balance sheets and profit and loss accounts) of industrial companies operating in the Czech Republic for the last 5 marketing years were used. For the application of classification methods, TIBCO’s Statistica software, version 13, is used. In total, 6 models were created and subsequently compared with each other, while the most successful one applicable in practice is the model determined by the neural structure 2.MLP 22-9-2. The model of Support Vector Machine shows a relatively high accuracy, but it is not applicable in the structure of correct classifications.

ACS Style

Jakub Horak; Jaromir Vrbka; Petr Suler. Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison. Journal of Risk and Financial Management 2020, 13, 60 .

AMA Style

Jakub Horak, Jaromir Vrbka, Petr Suler. Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison. Journal of Risk and Financial Management. 2020; 13 (3):60.

Chicago/Turabian Style

Jakub Horak; Jaromir Vrbka; Petr Suler. 2020. "Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison." Journal of Risk and Financial Management 13, no. 3: 60.

Conference paper
Published: 13 January 2020 in SHS Web of Conferences
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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.

ACS Style

Jaromír Vrbka; Marek Vochozka. Considering seasonal fluctuations on balancing time series with the use of artificial neural networks when forecasting US imports from the PRC. SHS Web of Conferences 2020, 73, 01033 .

AMA Style

Jaromír Vrbka, Marek Vochozka. Considering seasonal fluctuations on balancing time series with the use of artificial neural networks when forecasting US imports from the PRC. SHS Web of Conferences. 2020; 73 ():01033.

Chicago/Turabian Style

Jaromír Vrbka; Marek Vochozka. 2020. "Considering seasonal fluctuations on balancing time series with the use of artificial neural networks when forecasting US imports from the PRC." SHS Web of Conferences 73, no. : 01033.

Journal article
Published: 07 January 2020 in Littera Scripta
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ACS Style

Jaromír Vrbka; Petr Šuleř; Veronika Machová; Jakub Horák. Considering seasonal fluctuations in equalizing time series by means of artificial neural networks for predicting development of USA and People´s Republic of China trade balance. Littera Scripta 2020, 1 .

AMA Style

Jaromír Vrbka, Petr Šuleř, Veronika Machová, Jakub Horák. Considering seasonal fluctuations in equalizing time series by means of artificial neural networks for predicting development of USA and People´s Republic of China trade balance. Littera Scripta. 2020; ():1.

Chicago/Turabian Style

Jaromír Vrbka; Petr Šuleř; Veronika Machová; Jakub Horák. 2020. "Considering seasonal fluctuations in equalizing time series by means of artificial neural networks for predicting development of USA and People´s Republic of China trade balance." Littera Scripta , no. : 1.

Journal article
Published: 31 December 2019 in Equilibrium
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Research background: The trade sector is considered to be the power of economy, in developing countries in particular. With regard to the Czech Republic, this field of the national economy constitutes the second most significant employer and, at the same time, the second most significant contributor to GNP. Apart from traditional methods of business analyzing and identifying leaders, artificial neural networks are widely used. These networks have become more popular in the field of economy, although their potential has yet to be fully exploited. Purpose of the article: The aim of this article is to analyze the trade sector in the Czech Republic using Kohonen networks and to identify the leaders in this field. Methods: The data set consists of complete financial statements of 11,604 enterprises that engaged in trade activities in the Czech Republic in 2016. The data set is subjected to cluster analysis using Kohonen networks. Individual clusters are subjected to the analysis of absolute indicators and return on equity which, apart from other, shows a special attraction of individual clusters to potential investors. Average and absolute quantities of individual clusters are also analyzed, which means that the most successful clusters of enterprises in the trade sector are indicated. Findings & Value added: The results show that a relatively small group of enter-prises enormously influences the development of the trade sector, including the whole economy. The results of analyzing 319 enterprises showed that it is possible to predict the future development of the trade sector. Nevertheless, it is also evident that the trade sector did not go well in 2016, which means that investments of owners are minimal.

ACS Style

Jaromír Vrbka; Elvira Nica; Ivana Podhorská. The application of Kohonen networks for identification of leaders in the trade sector in Czechia. Equilibrium 2019, 14, 739 -761.

AMA Style

Jaromír Vrbka, Elvira Nica, Ivana Podhorská. The application of Kohonen networks for identification of leaders in the trade sector in Czechia. Equilibrium. 2019; 14 (4):739-761.

Chicago/Turabian Style

Jaromír Vrbka; Elvira Nica; Ivana Podhorská. 2019. "The application of Kohonen networks for identification of leaders in the trade sector in Czechia." Equilibrium 14, no. 4: 739-761.

Conference paper
Published: 09 May 2019 in Proceedings of 6th International Scientific Conference Contemporary Issues in Business, Management and Economics Engineering ‘2019
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Purpose – artificial neural networks are compared with mixed conclusions in terms of forecasting performance. The most researches indicate that deep-learning models are better than traditional statistical or mathematical models. The purpose of the article is to compare the accuracy of equalizing time series by means of regression analysis and neural networks on the example of the USA export to China. The aim is to show the possible uses and advantages of neural networks in practice. Research methodology – the period for which the data (USA export to the PRC) are available is the monthly balance starting from January 1985 to August 2018. First of all, linear regression as the relatively simple mathematical method is carried out. Subsequently, neural networks as the computational models used in artificial intelligence are used for regression. Findings – in terms of linear regression, the most suitable one appeared to be the curve obtained by means of the least squares methods by negative-exponential smoothing, and the curve obtained by means of the distance-weighted least squares method. In terms of neural networks, all retained structures appeared to be applicable in practice. Artificial neural networks have better representational power than traditional models. Research limitations – the simplification (quite a significant one) appears both in the cases of linear regression and regression by means of neural networks. We work only with two variables – input variable (time) and output variable (USA export to the PRC). Practical implications – in practice, the results – especially the method of artificial neural networks – can be used in the measurement and prediction of the development of exports, but especially in the short term. It can be stated that due to great simplification of the reality it isnʼt possible to predict extraordinary situations and their effect on the USA export to the PRC. Originality/Value – the article focuses on the comparison of two statistical methods, in particular, artificial intelligence is not used in such applications. However, in many economic industries, it has proven better results. It is found that artificial neural networks are able to effectively learn dependencies in and between the time series in the form of export development data.

ACS Style

Jakub Horák; Petr Šuleř; Jaromír Vrbka. Comparison of neural networks and regression time series when predicting the export development from the USA to PRC. Proceedings of 6th International Scientific Conference Contemporary Issues in Business, Management and Economics Engineering ‘2019 2019, 1 .

AMA Style

Jakub Horák, Petr Šuleř, Jaromír Vrbka. Comparison of neural networks and regression time series when predicting the export development from the USA to PRC. Proceedings of 6th International Scientific Conference Contemporary Issues in Business, Management and Economics Engineering ‘2019. 2019; ():1.

Chicago/Turabian Style

Jakub Horák; Petr Šuleř; Jaromír Vrbka. 2019. "Comparison of neural networks and regression time series when predicting the export development from the USA to PRC." Proceedings of 6th International Scientific Conference Contemporary Issues in Business, Management and Economics Engineering ‘2019 , no. : 1.

Chapter
Published: 16 March 2019 in Contributions to Economics
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To evaluate the financial health of a company, comprehensive enterprise evaluation methods are very important. These include, in particular, creditworthiness and bankruptcy models and economic value added (EVA). Creditworthiness models effectively evaluate a company’s financial health without using statistical methods, bankruptcy models indicate a threat to the financial health of the business, and are important for many decision-making processes. The aim of this contribution is to evaluate the financial health of an average mining and quarrying enterprise using comprehensive enterprise evaluation methods. Data of companies in this industry is used—specifically, the financial statements for 2012–2016. It is the average enterprise for which these bankruptcy and creditworthiness models are applied: Altman’s analysis in all modifications, indices IN (IN95, IN99, IN01, and IN05), Tafler index, Grünwald index, Kralicek’s quick test in original and modified version, and index of creditworthiness. EVA is further explored in two of its variants—EVA Equity and EVA Entity. Based on the results of these comprehensive enterprise evaluation methods, it can be concluded that the mining and quarrying industry is not financially sound in the Czech Republic. It is possible to correct negative phenomena that characterize the whole industry.

ACS Style

J. Vrbka; Z. Rowland. Assessing the Financial Health of Companies Engaged in Mining and Extraction Using Methods of Complex Evaluation of Enterprises. Contributions to Economics 2019, 321 -333.

AMA Style

J. Vrbka, Z. Rowland. Assessing the Financial Health of Companies Engaged in Mining and Extraction Using Methods of Complex Evaluation of Enterprises. Contributions to Economics. 2019; ():321-333.

Chicago/Turabian Style

J. Vrbka; Z. Rowland. 2019. "Assessing the Financial Health of Companies Engaged in Mining and Extraction Using Methods of Complex Evaluation of Enterprises." Contributions to Economics , no. : 321-333.

Conference paper
Published: 30 January 2019 in SHS Web of Conferences
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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.

ACS Style

Jaromír Vrbka; Zuzana Rowland; Petr Šuleř. Comparison of neural networks and regression time series in estimating the development of the EU and the PRC trade balance. SHS Web of Conferences 2019, 61, 01031 .

AMA Style

Jaromír Vrbka, Zuzana Rowland, Petr Šuleř. Comparison of neural networks and regression time series in estimating the development of the EU and the PRC trade balance. SHS Web of Conferences. 2019; 61 ():01031.

Chicago/Turabian Style

Jaromír Vrbka; Zuzana Rowland; Petr Šuleř. 2019. "Comparison of neural networks and regression time series in estimating the development of the EU and the PRC trade balance." SHS Web of Conferences 61, no. : 01031.

Journal article
Published: 01 December 2018 in Journal of International Studies
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ACS Style

Pavol Kral; Hussam Musa; George Lazaroiu; Maria Misankova; Jaromir Vrbka. Comprehensive assessment of the selected indicators of financial analysis in the context of failing companies. Journal of International Studies 2018, 11, 282 -294.

AMA Style

Pavol Kral, Hussam Musa, George Lazaroiu, Maria Misankova, Jaromir Vrbka. Comprehensive assessment of the selected indicators of financial analysis in the context of failing companies. Journal of International Studies. 2018; 11 (4):282-294.

Chicago/Turabian Style

Pavol Kral; Hussam Musa; George Lazaroiu; Maria Misankova; Jaromir Vrbka. 2018. "Comprehensive assessment of the selected indicators of financial analysis in the context of failing companies." Journal of International Studies 11, no. 4: 282-294.

Journal article
Published: 30 September 2018 in Equilibrium
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Research background: The problem of bankruptcy prediction models has been a current issue for decades, especially in the era of strong competition in markets and a constantly growing number of crises. If a company wants to prosper and compete successfully in a market environment, it should carry out a regular financial analysis of its activities, evaluate successes and failures, and use the results to make strategic decisions about the future development of the business. Purpose of the article: The main aim of the paper is to develop a model to reveal the un-healthy development of the enterprises in V4 countries, which is done by the multiple discriminant analysis. Methods: To conduct the research, we use the Amadeus database providing necessary financial and statistical data of almost 450,000 enterprises, covering the year 2015 and 2016, operating in the countries of the Visegrad group. Realizing the multiple discriminant analysis, the most significant predictor and the best discriminants of the corporate prosperity are identified, as well as the prediction models for both individual V4 countries and complex Visegrad model. Findings & Value added: The results of the research reveal that the prediction models use the combination of same financial ratios to predict the future financial development of a company. However, the most significant predictors are current assets to current liabilities ratio, net income to total assets ratio, ratio of non-current liabilities and current liabilities to total assets, cash and cash equivalents to total assets ratio and return of equity. All developed models have more than 80 % classification ability, which indicates that models are formed in accordance with the economic and financial situation of the V4 countries. The research results are important for companies themselves, but also for their business partners, suppliers and creditors to eliminate financial and other corporate risks related to the un-healthy or unfavorable financial situation of the company.

ACS Style

Tomas Kliestik; Jaromir Vrbka; Zuzana Rowland. Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis. Equilibrium 2018, 13, 569 -593.

AMA Style

Tomas Kliestik, Jaromir Vrbka, Zuzana Rowland. Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis. Equilibrium. 2018; 13 (3):569-593.

Chicago/Turabian Style

Tomas Kliestik; Jaromir Vrbka; Zuzana Rowland. 2018. "Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis." Equilibrium 13, no. 3: 569-593.

Conference paper
Published: 06 December 2017 in SHS Web of Conferences
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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.

ACS Style

Jaromír Vrbka; Zuzana Rowland. Stock price development forecasting using neural networks. SHS Web of Conferences 2017, 39, 1032 .

AMA Style

Jaromír Vrbka, Zuzana Rowland. Stock price development forecasting using neural networks. SHS Web of Conferences. 2017; 39 ():1032.

Chicago/Turabian Style

Jaromír Vrbka; Zuzana Rowland. 2017. "Stock price development forecasting using neural networks." SHS Web of Conferences 39, no. : 1032.

Conference paper
Published: 06 December 2017 in SHS Web of Conferences
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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.

ACS Style

Bořivoj Groda; Jaromír Vrbka. Prediction of stock price developments using the Box-Jenkins method. SHS Web of Conferences 2017, 39, 1007 .

AMA Style

Bořivoj Groda, Jaromír Vrbka. Prediction of stock price developments using the Box-Jenkins method. SHS Web of Conferences. 2017; 39 ():1007.

Chicago/Turabian Style

Bořivoj Groda; Jaromír Vrbka. 2017. "Prediction of stock price developments using the Box-Jenkins method." SHS Web of Conferences 39, no. : 1007.

Journal article
Published: 01 June 2016 in Naše more
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ACS Style

Marek Vochozka; Zuzana Rowland; Jaromír Vrbka. Financijska analiza prosječne prijevozne kompanije u pojedinoj zemlji. Naše more 2016, 63, 227 -236.

AMA Style

Marek Vochozka, Zuzana Rowland, Jaromír Vrbka. Financijska analiza prosječne prijevozne kompanije u pojedinoj zemlji. Naše more. 2016; 63 (3):227-236.

Chicago/Turabian Style

Marek Vochozka; Zuzana Rowland; Jaromír Vrbka. 2016. "Financijska analiza prosječne prijevozne kompanije u pojedinoj zemlji." Naše more 63, no. 3: 227-236.