Influence of age on selected parameters of insolvent companies
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Author(s)Dagmar CamskaLink to ORCID Index: https://orcid.org/0000-0003-4597-379X
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Jiri KleckaLink to ORCID Index: https://orcid.org/0000-0001-5203-9466
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Hana ScholleovaLink to ORCID Index: https://orcid.org/0000-0002-3729-9689
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DOIhttp://dx.doi.org/10.21511/ppm.19(2).2021.07
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Article InfoVolume 19 2021, Issue #2, pp. 77-90
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Creative Commons Attribution 4.0 International License
It is natural for the market economy that companies are forced to leave the market when they are not able to survive anymore. This paper is focused on the age structure of the companies in default. The age is considered a period between corporate establishment and insolvency declaration. The paper analyzes whether companies, which report financial accounting statements, have different age structure than non-reporting entities. Data sample consists of 212 companies (147 reporting and 65 non-reporting entities). Moreover, the analysis points out if corporate financial standing differ according to the age structure observed. Using descriptive statistics tools, the observed relationship between the company age and the frequency of insolvency cases is expressed. The evaluation of the financial standing is based on a ratio analysis. Indicators such as return on assets, return on sales, debt ratio, cash and non-cash liquidity, and asset turnover are applied. The results show there are not significant differences in the age structure between the reporting and non-reporting enterprises. Values of financial indicators seem to be independent on the age structure. The paper provides explanations and brings a classification of specific differences observed such as a distinction between reasons due to sector specificities and partly due to the specifics of the current business environment in the Czech Republic (monitored period 2014 – first quarter 2019).
Acknowledgments
The authors are thankful to the Grant Agency of Academic Alliance (renamed the Grant Agency Academia Aurea) No. GAAA 10/2018 “Financial characteristics of enterprise in bankruptcy” for financial support to carry out this research.
- Keywords
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JEL Classification (Paper profile tab)G33, M21
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References55
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Tables2
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Figures4
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- Figure 1. Age structure observed
- Figure 2. Trends modelled by 5-year moving average
- Figure 3. Value of total enterprise assets (measured in thousand CZK)
- Figure 4. Value of enterprise EBITDA (measured in thousand CZK)
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- Table 1. Data sample structure
- Table 2. Median of selected financial indicators
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Project risk management of the construction industry enterprises based on fuzzy set theory
Oleg Gavrysh ,
Valeriia Melnykova
doi: http://dx.doi.org/10.21511/ppm.17(4).2019.17
Problems and Perspectives in Management Volume 17, 2019 Issue #4 pp. 203-213 Views: 936 Downloads: 173 TO CITE АНОТАЦІЯThe construction industry is a crucially important element of the Ukrainian economy, since its development and performance affect other industries. The economic recession consequences and the unforeseen recent events, caused by different types of risks, have adversely affected the construction industry development and necessitated the search for modern methods of risk management. The study is based on a sample of five projects from five construction industry enterprises and covered the period of 2010–2018. A set of project risks, investigated by the group of experts, was analyzed based on fuzzy set theory, and included seven phases of the fuzzy set model construction to assess project risks of construction industry enterprises. Based on the identified elements of a fuzzy set model and a set of significant project risks, a value classifier of significant project risks for construction industry enterprises was developed. This allowed to estimate the current values of project risk indicators and to identify them by levels of their fuzzy subset membership. Besides, a classifier for the quantitative assessment of the total project risks level for investment projects was developed, which allowed estimating the value of the aggregate indicator. In order to improve the existed methodology, the study suggested introducing probabilistic values for the risk of project failure depending on the significance of the overall project risks. Accordingly, the paper identifies the probability of significant project risks simultaneous occurring during the project implementation. However, the higher the likelihood of risk, the higher the probability of investment project failure.
