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Wyświetlanie 1-3 z 3
Tytuł:
The use of neural networks to determine value based drivers for SMEs operating in the rural areas of the Czech Republic
Autorzy:
Vrbka, Jaromír
Powiązania:
https://bibliotekanauki.pl/articles/19233517.pdf
Data publikacji:
2020
Wydawca:
Instytut Badań Gospodarczych
Tematy:
business value drivers
value based drivers
small and medium-sized enterprises
rural area
ultimate goal
economic value added
neural networks
Opis:
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.
Źródło:
Oeconomia Copernicana; 2020, 11, 2; 325-346
2083-1277
Pojawia się w:
Oeconomia Copernicana
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis
Autorzy:
Kliestik, Tomas
Vrbka, Jaromir
Rowland, Zuzana
Powiązania:
https://bibliotekanauki.pl/articles/22446534.pdf
Data publikacji:
2018
Wydawca:
Instytut Badań Gospodarczych
Tematy:
bankruptcy
prediction model
discriminant analysis
Visegrad group
financial analysis
Opis:
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.
Źródło:
Equilibrium. Quarterly Journal of Economics and Economic Policy; 2018, 13, 3; 569-593
1689-765X
2353-3293
Pojawia się w:
Equilibrium. Quarterly Journal of Economics and Economic Policy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The application of Kohonen networks for identification of leaders in the trade sector in Czechia
Autorzy:
Vrbka, Jaromír
Nica, Elvira
Podhorská, Ivana
Powiązania:
https://bibliotekanauki.pl/articles/22446390.pdf
Data publikacji:
2019
Wydawca:
Instytut Badań Gospodarczych
Tematy:
trade sector
Kohonen networks
leaders in the field
cluster analysis
return on equity
Opis:
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.
Źródło:
Equilibrium. Quarterly Journal of Economics and Economic Policy; 2019, 14, 4; 739-761
1689-765X
2353-3293
Pojawia się w:
Equilibrium. Quarterly Journal of Economics and Economic Policy
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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