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The objective of the contribution is to propose a new methodology for determining the optimal credit absorption capacity of an enterprise while maintaining the positive function of financial leverage, i.e., the maximum possible loan that would continuously bring benefit to the enterprise. The proposed methodology determines the credit absorption capacity of an enterprise according to EVA Equity and EVA Entity. Based on a theoretical analysis of both indicators, the possibility of applying the proposed methodology for this purpose was proved. To verify the theoretical assumptions, the optimal credit absorption capacity of enterprises operating in the agricultural sector of the CR was determined. The data used for the purposes of the contribution were obtained from the Albertina database for the years 2012–2018. The credit absorption capacity of the monitored enterprises ranged from CZK 6.88 million to CZK 9.6 million. The article also determines the optimal ratio of equity to debt capital.
Jiří Kučera; Marek Vochozka; Zuzana Rowland. The Ideal Debt Ratio of an Agricultural Enterprise. Sustainability 2021, 13, 4613 .
AMA StyleJiří Kučera, Marek Vochozka, Zuzana Rowland. The Ideal Debt Ratio of an Agricultural Enterprise. Sustainability. 2021; 13 (9):4613.
Chicago/Turabian StyleJiří Kučera; Marek Vochozka; Zuzana Rowland. 2021. "The Ideal Debt Ratio of an Agricultural Enterprise." Sustainability 13, no. 9: 4613.
Research background: Covid-19 has affected the global economy and has had an inevitable impact on capital markets. In the week of February 24?28, 2020, stock markets crashed. The index FTSE 100 decreased 13%, while the indices DJIA and S&P 500 fell 11?12%, the biggest drop since the 2007?2008 financial and economic crisis. It is therefore of interest to test the random walk hypothesis in developed capital markets, European and also non-European, in order to understand the different predictabilities between them. Purpose of the article: The aim is to analyze capital market efficiency, in its weak form, through the stock market indices of Belgium (index BEL 20), France (index CAC 40), Germany (index DAX 30), USA (index DOW JONES), Greece (index FTSE Athex 20), Spain (index IBEX 35), Ireland (index ISEQ), Portugal (index PSI 20) and China (index SSE) for the period from December 2019 to May 2020. Methods: Panel unit root tests of Breitung (2000), Levin et al. (2002) and Hadri (2002) were used to assess the time series stationarity. The test of Clemente et al. (1998) is used to detect structural breaks. The tests for the random walk hypothesis follows the variance ratio methodology proposed by Lo and MacKinlay (1988). Findings & Value added: In general, we found mixed confirmation about the EMH (efficient market hypothesis). Taking into account the conclusions of the rank variance test, the random walk hypothesis was rejected in the case of stock indices: Dow Jones, SSE and PSI 20, partially rejected in the case indices: BEL 20, CAC 40, FTSTE Athex 20 and DEX 30, but accepted for indices: IBEX 35 and ISEQ. The results also show that prices do not fully reflect the information available and that changes in prices are not independent and identically distributed. This situation has consequences for investors, since some returns can be expected, creating opportunities for arbitrage and for abnormal returns, contrary to the assumptions of random walk and information efficiency.
Rui Dias; Nuno Teixeira; Veronika Machova; Pedro Pardal; Jakub Horak; Marek Vochozka. Random walks and market efficiency tests: evidence on US, Chinese and European capital markets within the context of the global Covid-19 pandemic. Oeconomia Copernicana 2020, 11, 585 -608.
AMA StyleRui Dias, Nuno Teixeira, Veronika Machova, Pedro Pardal, Jakub Horak, Marek Vochozka. Random walks and market efficiency tests: evidence on US, Chinese and European capital markets within the context of the global Covid-19 pandemic. Oeconomia Copernicana. 2020; 11 (4):585-608.
Chicago/Turabian StyleRui Dias; Nuno Teixeira; Veronika Machova; Pedro Pardal; Jakub Horak; Marek Vochozka. 2020. "Random walks and market efficiency tests: evidence on US, Chinese and European capital markets within the context of the global Covid-19 pandemic." Oeconomia Copernicana 11, no. 4: 585-608.
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.
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 StyleMarek 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 StyleMarek 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.
The scientific periodical journal (indexed on Web of Science) published by the Tomas Bata University in Zlin offers results of basic and applied economic research in the English language.
Marek Vochozka; Institute of Technology and Business in České Budějovice; Zuzana Rowland; Petr Suler; Josef Marousek. The Influence of the International Price of Oil on the Value of the EUR/USD Exchange Rate. Journal of Competitiveness 2020, 12, 167 -190.
AMA StyleMarek Vochozka, Institute of Technology and Business in České Budějovice, Zuzana Rowland, Petr Suler, Josef Marousek. The Influence of the International Price of Oil on the Value of the EUR/USD Exchange Rate. Journal of Competitiveness. 2020; 12 (2):167-190.
Chicago/Turabian StyleMarek Vochozka; Institute of Technology and Business in České Budějovice; Zuzana Rowland; Petr Suler; Josef Marousek. 2020. "The Influence of the International Price of Oil on the Value of the EUR/USD Exchange Rate." Journal of Competitiveness 12, no. 2: 167-190.
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.
Marek Vochozka; Zuzana Rowland. Forecasting trade balance of Czech Republic and People´s Republic of China in equalizing time series and considering seasonal fluctuations. SHS Web of Conferences 2020, 73, 01032 .
AMA StyleMarek Vochozka, Zuzana Rowland. Forecasting trade balance of Czech Republic and People´s Republic of China in equalizing time series and considering seasonal fluctuations. SHS Web of Conferences. 2020; 73 ():01032.
Chicago/Turabian StyleMarek Vochozka; Zuzana Rowland. 2020. "Forecasting trade balance of Czech Republic and People´s Republic of China in equalizing time series and considering seasonal fluctuations." SHS Web of Conferences 73, no. : 01032.
The USA decided to regulate the trade more by imposing tariffs on specific types of traded goods. It is therefore more interesting to find out whether the current technologies based on artificial intelligence with time series influenced by extraordinary factors such as the trade war between two powers are able to work. The objective of the contribution is to examine and subsequently equalize two time series – the USA import from the PRC and the USA export to the PRC. The dataset shows the course of the time series at monthly intervals between January 2000 and July 2019. 10,000 multilayer perceptron networks (MLP) are generated, out of which 5 with the best characteristics are retained. It has been proved that multilayer perceptron networks are a suitable tool for forecasting the development of the time series if there are no sudden fluctuations. Mutual sanctions of both states did not affect the result of machine learning forecasting.
Zuzana Rowland; Jaromír Vrbka; Marek Vochozka. Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions. SHS Web of Conferences 2020, 73, 01025 .
AMA StyleZuzana Rowland, Jaromír Vrbka, Marek Vochozka. Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions. SHS Web of Conferences. 2020; 73 ():01025.
Chicago/Turabian StyleZuzana Rowland; Jaromír Vrbka; Marek Vochozka. 2020. "Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions." SHS Web of Conferences 73, no. : 01025.
Research background: The article deals with implementing VMI between the supplier and customer. To assess whether the system will be implemented, the evolution game theory is used. The contribution is based on the limitations of the study of the evolutionary game theory approach to modelling VMI policies (Torres et al., 2014) and its later extension, The evolutionary game theory approach to modelling VMI policies (Torres & García-Díaz, 2018). It aims is to complement the studies and provide a comprehensive picture of the issue. Purpose of the article: The main objective of the contribution is to respond to the question whether the VMI system will be introduced between the supplier and customer. Methods: In the first phase, the matrix is analysed from the point of view of the game meaning and its limit parameters. The limit parameters are set taking into account the economic reality. The only examined states of the matrix are those where the result is not obvious. For the purposes of the contribution, we work with a 5-year period. A new software capable of calculating evolutionary focus and their stability is created. Sensitivity analysis is carried out for the individual parameters that affect the system behaviour. Findings & Value added: Value added is a complex description of the system and complementation of previous studies in this field. VMI is confirmed. The results obtained can be used for practical management, so that the managers are able to identify what the actual costs are and what the probability of introducing the sys-tem is. At the same time, they can identify the parameters that can be influenced by them and observe their impact on the shift of the system introduction probability.
Vojtěch Stehel; Marek Vochozka; Tomas Kliestik; Vladimir Bakes. Economic analysis of implementing VMI model using game theory. Oeconomia Copernicana 2019, 10, 253 -272.
AMA StyleVojtěch Stehel, Marek Vochozka, Tomas Kliestik, Vladimir Bakes. Economic analysis of implementing VMI model using game theory. Oeconomia Copernicana. 2019; 10 (2):253-272.
Chicago/Turabian StyleVojtěch Stehel; Marek Vochozka; Tomas Kliestik; Vladimir Bakes. 2019. "Economic analysis of implementing VMI model using game theory." Oeconomia Copernicana 10, no. 2: 253-272.
The exchange rate is one of the most monitored economic variables reflecting the state of the economy in the long run, while affecting it significantly in the short run. However, prediction of the exchange rate is very complicated. In this contribution, for the purposes of predicting the exchange rate, artificial neural networks are used, which have brought quality and valuable results in a number of research programs. This contribution aims to propose a methodology for considering seasonal fluctuations in equalizing time series by means of artificial neural networks on the example of Euro and Chinese Yuan. For the analysis, data on the exchange rate of these currencies per period longer than 9 years are used (3303 input data in total). Regression by means of neural networks is carried out. There are two network sets generated, of which the second one focuses on the seasonal fluctuations. Before the experiment, it had seemed that there was no reason to include categorical variables in the calculation. The result, however, indicated that additional variables in the form of year, month, day in the month, and day in the week, in which the value was measured, have brought higher accuracy and order in equalizing of the time series.
Marek Vochozka; Jakub Horák; Petr Šuleř. Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate. Journal of Risk and Financial Management 2019, 12, 76 .
AMA StyleMarek Vochozka, Jakub Horák, Petr Šuleř. Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate. Journal of Risk and Financial Management. 2019; 12 (2):76.
Chicago/Turabian StyleMarek Vochozka; Jakub Horák; Petr Šuleř. 2019. "Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate." Journal of Risk and Financial Management 12, no. 2: 76.
Stock prices are developing very dynamically and nonlinearly. The stock price is affected by a number of factors. Stocks are therefore characterized by asymmetric volatility, non-stationarity, and sensitivity. Given these facts and the unpredictability of a global crisis, it is logical that the process of stock price prediction is a complex task. Traditional methods for price prediction are no longer enough; new applications and techniques, such as artificial neural networks, are coming to the forefront. The aim of this contribution is to analyze and predict the evolution of the stock price of Unipetrol, a.s. on the Prague Stock Exchange using artificial neural networks. Stock price data is available between January 2006 and April 2018. The data file is first analyzed. Subsequently, a total of 10,000 multilayer perceptron networks (MLPs) and a basic radial function network (RBF) are generated. A total of five neuron structures with the best characteristics are preserved. Using statistical interpretation, it is found that in practice, the MLP 1-17-1 network is applicable in one business day prediction.
V. Machová; M. Vochozka. Using Artificial Intelligence in Analyzing and Predicting the Development of Stock Prices of a Subject Company. Contributions to Economics 2019, 235 -245.
AMA StyleV. Machová, M. Vochozka. Using Artificial Intelligence in Analyzing and Predicting the Development of Stock Prices of a Subject Company. Contributions to Economics. 2019; ():235-245.
Chicago/Turabian StyleV. Machová; M. Vochozka. 2019. "Using Artificial Intelligence in Analyzing and Predicting the Development of Stock Prices of a Subject Company." Contributions to Economics , no. : 235-245.
In recent years, the primary copper ore stock has been cut sharply and the price of crude copper has been rising. On the other hand, thanks to a huge industrial interest, the production of copper products has increased significantly over recent years. It is therefore clear that the prediction of the copper price is very important. A variety of techniques, such as statistical methods—regression time series or artificial neural networks—are used for prediction. The aim of this contribution is to perform a regression analysis of the copper price development on the New York Stock Exchange using the mentioned linear regression and neural networks, expertly compare both methods, and identify the more suitable one for a possible prediction of future copper price developments. Input data includes copper price data from January 2006 to April 2018. First, linear regression is performed, and then, neural networks are used for regression analysis. A total of 1000 neuron structures are generated, five of which with the best characteristics are kept, and these are then further worked with. From the linear regression, the curve obtained by the spline function appears to be best, and the neural networks have all been proven to be usable in practice.
M. Vochozka; Jakub Horák. Comparison of Neural Networks and Regression Time Series When Estimating the Copper Price Development. Contributions to Economics 2019, 169 -181.
AMA StyleM. Vochozka, Jakub Horák. Comparison of Neural Networks and Regression Time Series When Estimating the Copper Price Development. Contributions to Economics. 2019; ():169-181.
Chicago/Turabian StyleM. Vochozka; Jakub Horák. 2019. "Comparison of Neural Networks and Regression Time Series When Estimating the Copper Price Development." Contributions to Economics , no. : 169-181.
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.
Marek Vochozka; Jaromír Vrbka. Estimation of the development of the Euro to Chinese Yuan exchange rate using artificial neural networks. SHS Web of Conferences 2019, 61, 01030 .
AMA StyleMarek Vochozka, Jaromír Vrbka. Estimation of the development of the Euro to Chinese Yuan exchange rate using artificial neural networks. SHS Web of Conferences. 2019; 61 ():01030.
Chicago/Turabian StyleMarek Vochozka; Jaromír Vrbka. 2019. "Estimation of the development of the Euro to Chinese Yuan exchange rate using artificial neural networks." SHS Web of Conferences 61, no. : 01030.
The cost of debt is referred to as the key factor determining profitability. It is a decisive factor in decision making of the management, especially in strategy development. The purpose of this paper is to establish the relationship between the volume of debt and the economic outturn of industrial enterprises. Using artificial neural networks, the relationship between interest costs and three profit categories is examined. Data of 5622 Czech processing enterprises in the years 2015-2017 are used. Multilayer perceptron neural networks and neural networks of basic radial functions are used for processing. A total of 10,000 neural structures are generated for each cost-interest relationship and the corresponding profit, of which 5 are retained, showing the best results. The results indicate that in all cases of profit there is no dependence between the interest and the amount of profit generated. Profiting companies do not get debt cheaper than other businesses.
Marek Vochozka. Effect of the economic outturn on the cost of debt of an industrial enterprise. SHS Web of Conferences 2017, 39, 1028 .
AMA StyleMarek Vochozka. Effect of the economic outturn on the cost of debt of an industrial enterprise. SHS Web of Conferences. 2017; 39 ():1028.
Chicago/Turabian StyleMarek Vochozka. 2017. "Effect of the economic outturn on the cost of debt of an industrial enterprise." SHS Web of Conferences 39, no. : 1028.
Conditions for applying modern approaches to the study of functioning effectiveness of corporate innovation systems are presented, the main ones of which are system, strategic and integrated approaches that will allow in practice to formulate clear guidelines for innovation development, to develop an effective algorithm of measures to achieve them and create conditions for overcome the lag in key indicators . The authors develop an organizational mechanism for implementation of strategic approach to the enterprise management system, which provides for a number of stages in the development and implementation of innovation strategy, where the corporate innovation system is the organizational basis for these processes. The monitoring of the effective functioning of this process allows to determine the internal potential of the enterprise for activating innovation activities. It is proposed to increase the level of information support for management decisions at the enterprise for innovation by introducing a methodology for analyzing the effectiveness of CIS functioning, within which stages of analysis are proposed, existing indicators are systematized, an integrated index of innovation activity is developed. Innovation activity level on the example of foreign enterprises of the automotive industry is developed that allows to present their results in a fragmented and generalized form.
Alla Kasych; Marek Vochоzka; Nataliia Buhas. Investigation of modern approaches to efficiency analysis of the functioning of corporate innovation systems. Technology audit and production reserves 2017, 2, 35 -40.
AMA StyleAlla Kasych, Marek Vochоzka, Nataliia Buhas. Investigation of modern approaches to efficiency analysis of the functioning of corporate innovation systems. Technology audit and production reserves. 2017; 2 (4):35-40.
Chicago/Turabian StyleAlla Kasych; Marek Vochоzka; Nataliia Buhas. 2017. "Investigation of modern approaches to efficiency analysis of the functioning of corporate innovation systems." Technology audit and production reserves 2, no. 4: 35-40.