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    Wyświetlanie 1-5 z 5
    Tytuł:
    Management of financial risks in Slovak enterprises using regression analysis
    Autorzy:
    Valaskova, Katarina
    Kliestik, Tomas
    Kovacova, Maria
    Powiązania:
    https://bibliotekanauki.pl/articles/18799016.pdf
    Data publikacji:
    2018
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    financial risk
    default
    bankruptcy
    regression model
    Opis:
    Research background: Financial risk management is the task of monitoring financial risks and managing their impact. Financial risk is often perceived as the risk that a company may default on its debt payments. The issue of the debt, default or prosperity of the company are presented in the article as one of the ways of the risk management. A prediction of corporate default is an inseparable element of the risk management. Mainly the consequences of risk are the engine of research and development of methods and models, which enable to predict economic and financial situation in specific conditions of global economies. Purpose of the article: The main aim of the presented article is to assess financial risks of Slovak entities, realized by the identification of significant factors and determinants affecting the prosperity of Slovak companies. Methods: To conduct the research we have used the data of Slovak enterprises, obtained from annual financial reports covering the year 2015 and the calculated financial ratios of profitability, activity, liquidity and indebtedness that may affect the financial health of the company were applied in the regression analysis. Realizing the multiple regression analysis, the statistically significant determinants that affect the future financial development of the company are identified, as well as the regression model of the bankruptcy prediction. Findings & Value added: In the research aimed at the management of financial risks in Slovak enterprises, we focused on the revelation of significant economic risk factors using multiple regression. The results suggest that the most significant predictors are net return on capital, cash ratio, quick ratio, current ratio, net working capital, RE/TA ratio, current debt ratio, financial debt ratio and current assets turnover based on which the decision about the future company default can be made. These factors are significant enough to manage financial risks and to affect the profitability and prosperity of the company.
    Źródło:
    Oeconomia Copernicana; 2018, 9, 1; 105-121
    2083-1277
    Pojawia się w:
    Oeconomia Copernicana
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Earnings management model for Visegrad Group as an immanent part of creative accounting
    Autorzy:
    Kováčová, Mária
    Hrosova, Lenka
    Durana, Pavol
    Horak, Jakub
    Powiązania:
    https://bibliotekanauki.pl/articles/19322663.pdf
    Data publikacji:
    2022
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    creative accounting
    central Europe countries
    model of creative accounting
    regression analysis
    Opis:
    Research background: Creative accounting practices do not frequently violate the law and are not considered illegal; however, accounting managers may exploit legal ambiguities to portray the company's financial standing in accordance with management preferences. Therefore, the analysis is focused on the detection of earnings management in companies operating in the Visegrad Group, which represents one of the most commonly used techniques for revealing creative accounting. Purpose of the article: The aim of the presented study is to reveal the presence of creative accounting through the detection of earnings management in the countries of the Visegrad Group and, based on the detected results, to propose a regression model of earnings management in the Visegrad Group. Methods: To reveal the use of creative accounting in the Visegrad Group, ten selected models of earnings management were applied during the period 2016-2020 to a set of 8,134 companies. The Mann-Whitney test and multiple linear regression were used to verify the existence of earnings management. These findings served as the basis for the creation of the linear regression model of earnings management in the Visegrad Group. Findings & value-added: The presence of earnings management was best captured by the Hribar and Collins model in companies operating in the Visegrad Group in the period 2016-2020. The findings also confirmed that positive discretionary accruals acquired higher values over the entire period when compared to negative discretionary accruals, confirming that companies in the surveyed group manage their profits primarily by increasing them. Therefore, we created a regression model, that can serve as a unique basis and is capable of revealing the use of earnings management in the Visegrad Group, as until now nothing like this has been implemented in these countries. In conclusion, this study offers insights for academicians and researchers on creative accounting in the selected period. Lastly, the study contributes to the existing theory by conducting new research on the earnings management determinants of the countries studied.
    Źródło:
    Oeconomia Copernicana; 2022, 13, 4; 1143-1176
    2083-1277
    Pojawia się w:
    Oeconomia Copernicana
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Advanced methods of earnings management: monotonic trends and change-points under spotlight in the Visegrad countries
    Autorzy:
    Kliestik, Tomas
    Valaskova, Katarina
    Nica, Elvira
    Kovacova, Maria
    Lazaroiu, George
    Powiązania:
    https://bibliotekanauki.pl/articles/19233520.pdf
    Data publikacji:
    2020
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    business finance
    change-point
    earnings management
    monotonic trend
    European countries
    Opis:
    Research background: Enterprises manage earnings in an effort to balance their profit fluctuations to provide increasingly consistent earnings in every reporting period. Earnings management is legal and very effective method of accounting techniques and may be used to obtain specific objectives of the enterprises involving the manipulation of accruals. Therefore, there is a need to analyze it in the context of group of countries, while the issue of their detection in the new ways appears.  Purpose of the article: The analysis of annual earnings before interest and taxes (EBIT) of 5,640 enterprises from the Visegrad Four during the period 2009-2018 confirms that the development of earnings management in these countries is not a randomness. Thus, the aim of this article is to determine the existence of positive trend in earnings management and to detect the change-point in its development for each Visegrad country. Methods: Grubbs test, Mann-Kendall trend test and Buishand test were used as appropriate statistical methods. Mann-Kendall test identifies significant monotonic trend occurrence in earnings manipulation in every country. Buishand test indicates significant years, which divides the development of EBIT into two homogenous groups with individual central lines. Findings & Value added: Based on the statistical analysis applied, we rejected randomness in the managing of earning, but we determined the trend of its increasing. The positive earnings manipulation was not homogenous in the analyzed period, however, a change-point was defined. Year 2014 was identified as a break-point for Slovak, Polish and Hungarian enterprises considering the earnings manipulation. Year 2013 was detected as a change-point in Czech enterprises. The methodical approach used may be very helpful for researchers from other countries to determine, detect and understand earnings management as well as for the investors to make decisions based on a specificities of an individual country.
    Źródło:
    Oeconomia Copernicana; 2020, 11, 2; 371-400
    2083-1277
    Pojawia się w:
    Oeconomia Copernicana
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries
    Autorzy:
    Kovacova, Maria
    Kliestik, Tomas
    Valaskova, Katarina
    Durana, Pavol
    Juhaszova, Zuzana
    Powiązania:
    https://bibliotekanauki.pl/articles/19106225.pdf
    Data publikacji:
    2019
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    bankruptcy
    bankruptcy prediction
    variables
    countries of Visegrad four
    Opis:
    Research background: Since the first bankruptcy prediction models were developed in the 60's of the 20th century, numerous different models have been constructed all over the world. These individual models of bankruptcy prediction have been developed in different time and space using different methods and variables. Therefore, there is a need to analyse them in the context of various countries, while the question about their suitability arises. Purpose of the article: The analysis of more than 100 bankruptcy prediction models developed in V4 countries confirms that enterprises in each country prefer different explanatory variables. Thus, we aim to review systematically the bankruptcy prediction models developed in the countries of Visegrad four and analyse them, with the emphasis on explanatory variables used in these models, and evaluate them using appropriate statistical methods. Methods: Cluster analysis and correspondence analysis were used to explore the mutual relationships among the selected categories, e.g. clusters of explanatory variables and countries of the Visegrad group. The use of the cluster analysis focuses on the identification of homogenous subgroups of the explanatory variables to sort the variables into clusters, so that the variables within a common cluster are as much similar as possible. The correspondence analysis is used to examine if there is any statistically significant dependence between the monitored factors ? bankruptcy prediction models of Visegrad countries and explanatory variables. Findings & Value added: Based on the statistical analysis applied, we confirmed that each country prefers different explanatory variables for developing the bankruptcy prediction model. The choice of an appropriate and specific variable in a specific country may be very helpful for enterprises, researchers and investors in the process of construction and development of bankruptcy prediction models in conditions of an individual country.
    Źródło:
    Oeconomia Copernicana; 2019, 10, 4; 743-772
    2083-1277
    Pojawia się w:
    Oeconomia Copernicana
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals
    Autorzy:
    Lăzăroiu, George
    Gedeon, Tom
    Rogalska, Elżbieta
    Andronie, Mihai
    Frajtova Michalikova, Katarina
    Musova, Zdenka
    Iatagan, Mariana
    Uță, Cristian
    Michalkova, Lucia
    Kovacova, Maria
    Ștefănescu, Roxana
    Hurloiu, Iulian
    Zabojnik, Stanislav
    Stefko, Robert
    Dijmărescu, Adrian
    Dijmărescu, Irina
    Geamănu, Marinela
    Powiązania:
    https://bibliotekanauki.pl/articles/39832736.pdf
    Data publikacji:
    2024
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    deep and machine learning
    COVID 19
    prediction
    detection
    diagnosis
    organizational management
    hospital
    Opis:
    Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.
    Źródło:
    Oeconomia Copernicana; 2024, 15, 1; 27-58
    2083-1277
    Pojawia się w:
    Oeconomia Copernicana
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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