Influence of age on selected parameters of insolvent companies
-
Author(s)Dagmar CamskaLink to ORCID Index: https://orcid.org/0000-0003-4597-379X
,
Jiri KleckaLink to ORCID Index: https://orcid.org/0000-0001-5203-9466
,
Hana ScholleovaLink to ORCID Index: https://orcid.org/0000-0002-3729-9689
-
DOIhttp://dx.doi.org/10.21511/ppm.19(2).2021.07
-
Article InfoVolume 19 2021, Issue #2, pp. 77-90
- Cited by
- 674 Views
-
282 Downloads
This work is licensed under a
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
-
JEL Classification (Paper profile tab)G33, M21
-
References55
-
Tables2
-
Figures4
-
- 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)
-
- Table 1. Data sample structure
- Table 2. Median of selected financial indicators
-
- Achim, M. V., Mare, C., & Borlea, S. N. (2012). A statistical model of financial risk bankruptcy applied for Romanian manufacturing industry. Procedia Economics and Finance, 3, 132-137.
- Agarwal, V., & Taffler, R. (2007). Twenty-five years of the Taffler z-score model: does it really have predictive ability? Accounting and Business Research, 37(4), 285-300.
- Akhter, N., Sieger, P., & Chirico, F. (2016). If We Can’t have It, then No One Should: Shutting Down versus Selling in Family Business Portfolios. Strategic Entrepreneurship Journal, 10(4).
- Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23(4), 589-609.
- Altman, E. I., & Hotchkiss, E. (2010). Corporate financial distress and bankruptcy: Predict and avoid bankruptcy, analyze and invest in distressed debt. New Jersey: John Wiley & Sons.
- Arroyave, J. (2018). A comparative analysis of the effectiveness of corporate bankruptcy prediction models based on financial ratios: Evidence from Colombia. Journal of International Studies, 11(1), 273-287.
- Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63-93.
- Bilan Y., Mishchuk, H., Roshchyk, I., & Joshi, O. (2020a). Hiring and retaining skilled employees in SMEs: problems in human resource practices and links with organizational success. Business: Theory and Practice, 21(2), 780-791.
- Bilan, Y., Mishchuk, H., Samoliuk, N., & Mishchuk, V. (2020b). Gender discrimination and its links with compensations and benefits practices in enterprises. Entrepreneurial Business and Economics Review, 8(3), 189-204.
- Bokšová, J., & Randáková, M. (2013). Zveřejňují podniky, které procházejí insolvenčním řízením, své účetní závěrky? Český finanční a účetní časopis, 8(4), 164-171.
- Bradley, D., & Rubach, M. (2002). Trade Credit and Small Business – A Cause of Business Failures? (pp. 1-7). Conway, University of Central Arkansas.
- Campillo, J. P., Serer, G. L., & Ferrer, E. V. (2013). Validez de la información financiera en los procesos de insolvencia. Un estudio de la pequeña empresa española. Cuadernos de Economía y Dirección de la Empresa, 16(1), 29-40.
- Camska, D. (2013). Basic Characteristics of Enterprises in Insolvency. Recenzovaný sborník mezinárodní odborné konference Hradecké ekonomické dny 2013 Roč. 3(1), 83-88.
- Chava, S., & Jarrow, R. (2004). Bankruptcy Prediction with Industry Effects. Review of Finance, 8(4), 537-569.
- Cressy, R. (2006). Why the most firm die young? Small Business Economics, 26(2), 103-116.
- Crone, S., & Finlay, S. (2012). Instance Sampling in Credit Scoring: an Empirical Study of Sample Size and Balancing. International Journal of Forecasting, 28(1), 224-238.
- De Laurentis, G., Maino, R., & Molteni, L. (2010). Developing, validating and using internal ratings. Hoboken: John Wiley & Sons.
- Doyle, J., Weili, G., & McVay, S. (2007). Determinants of weaknesses in internal control over financial reporting. Journal of Accounting and Economics, 44(1-2), 193-223.
- Du Jardin, P. (2017). Dynamics of Firm Financial Evolution and Bankruptcy Prediction. Expert Systems with Application, 75, 25-43.
- European Commission. (2012). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. Entrepreneurship 2020 Action Plan. Reigniting the entrepreneurial spirit in Europe.
- Fairfield, P. M., Rammath, S., & Yohn, T. L. (2009). Do Industry-Level Analyses Improve Forecasts of Financial Performance? Journal of Accounting Research, 47(1), 147-178.
- Fayolle, A., Linan, F., & Moriano, J. A. (2014). Beyond entrepreneurial intentions: values and motivations in entrepreneurship. International Entrepreneurship and Management Journal, 10(4), 679-689.
- Frank, M. Z., & Goyal, V. K. (2009). Capital Structure Decisions: Which Factors Are Reliably Important? Financial Management, 38(1), 1-37.
- Ganco, M., & Agerwal, R. (2009). Performance Differentials between Diversifying Entrants and Entrepreneurial Start-ups: a Complexity Approach. Academy of Management Review, 34(2).
- Ganguin, B., & Bilardello, J. (2005). Fundamentals of Corporate Credit Analysis, Standard & Poor’s. New York: McGraw-Hill.
- García Lara, J. M., Osma, B. G., & Neophytou, E. (2009). Earnings quality in ex-post failed firms. Accounting and business research, 39(2), 119-138.
- Habib, A., & Hasan, M. M. (2017). Firm life cycle, corporate risk-taking and investor sentiment. Accounting and Finance, 57(2), 465-497.
- Hájek, P., Zhunissova, G., Oralbaeva, Z., Zhidebekkyzy, A., & Baidildina, A. (2019). Competitiveness and economic profit analysis of Kazakhstan’s poultry companies. Journal of International Studies, 12(2), 147-164.
- Jackson, A. B., Plumlee, M. A., & Rountree, B. R. (2018). Decomposing the market, industry, and firm components of profitability: implications for forecasts of profitability. Review of Accounting Studies, 23, 1071-1095.
- Jordan, B., Westerfield, R., & Ross, S. (2011). Corporate Finance Essentials. New York, McGraw-Hill.
- Jovanovic, B. (1982). Selection and the Evolution of Industry. Econometrica, 50(3), 649-670.
- Karniouchina, E. V., Carson, S. J., Short, J. C., & Ketchen, D. J. (2013). Extending the firm vs. industry debate: Does industry life cycle stage matter? Strategic Management Journal, 34(8), 1010-1018.
- Kliestik, T., Valaskova, K. Lazaroiu, G., Kovacova, M., & Vrbka, J. (2020). Remaining Financially Healthy and Competitive: the Role of Financial Predictors. Journal of Competitiveness, 12(1), 74-92.
- Korol, T. (2019). Dynamic Bankruptcy Prediction Models for European Enterprises. Journal of Risk and Financial Management, 12(4), 15.
- Kücher, A., Mayr, S., Mitter, C., Duller, C., & Feldbauer-Durstmüller, B. (2018). Firm age dynamics and causes of corporate bankruptcy: age dependent explanations for business failure. Review of Managerial Science, 1-29.
- Lee, G. K., & Alnahedh, M. (2016). Industries’ Potential for Interdependency and Profitability: A Panel of 135 Industries, 1988–1996. Strategy Science, 1(4), 285-308.
- Lee, S.-H., Yamakawa, Y., Peng, M. W., & Barney, J. B. (2011). How do bankruptcy laws affect entrepreneurship development around the world? Journal of Business Venturing, 26(5), 505-520.
- Lukason (2018). Age and size dependencies of firm failure processes: an analysis of bankrupted Estonian firms. International Journal of Law and Management, 60(6), 1272-1285.
- Maubossin, M., Callahan D., & Majd, D. (2017). Corporate Longevity. Credit Suisse.
- Moyer, S. G. (2005). Distressed debt analysis: strategies for speculative investors. Boca Raton: J. Ross Publishing.
- Nehrebecka, N. (2011). Wykorzystanie łańcuchów Markowa do prognozowania zmian w strukturze polskich przedsiębiorstw. Gospodarka Narodowa, 251(10), 59-98.
- Öztekin, Ö. (2015). Capital structure decisions around the world: which factors are reliably important? Journal of Financial and Quantitative Analysis, 50(3), 301-323.
- Pakes, A., & Ericsson, R. (1998). Empirical implications of alternative models of firm dynamics. Journal of Economic Theory, 79(1), 1-45.
- Paudel, U. R., & Devkota, N. (2018). Socio-economic influences on small business performance in Nepal–India open border: Evidence from cross-sectional analysis. Economics and Sociology, 11(4), 11-30.
- Peng, M. W., Yamakawa, Y., & Lee, S.-H. (2010). Bankruptcy Laws and Entrepreneur–Friendliness. Entrepreneurship Theory and Practice, 34(3), 517-530.
- Pirohanic, O. (2020). Insolvence 2019 v ČR. Prague, CRIF – Czech Credit Bureau.
- Schonfeld, J. (n.d). Financial situation of pre-packed insolvencies. Journal of Business Economics and Management (in press).
- Schonfeld, J., Kudej, M., & Smrcka, L. (2019). Financial Characteristics of Pre-Moratorium Companies. Politicka Ekonomie, 67(5), 490-510.
- Sikora, T., & Baranowska-Prokop, E. (2018). Explaining success perception of Polish international new ventures: Four perspectives. Economics and Sociology, 11(4), 106-127.
- Smrčka, L. (2013, March). The problems of the insolvency register in the Czech Republic from the perspective of information technology. In Rocha Á., Correia A., Wilson T., Stroetmann K. (Eds), Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing (vol 206). Springer, Berlin, Heidelberg.
- Smrčka, L., Arltová, M., & Schönfeld, J. (2013). Reasons for the failure to implement financial rehabilitation procedures in insolvent reality. Politická Ekonomie, 61(2), 188-208.
- Strouhal, J., Gurtviš, N., Nikitina-Kalamäe, M., Li, t.W., Lochman, A.-L-, & Born, K. (2014). Are Companies Willing To Publicly Present their Financial Statements on Time? Case of Czech and Estonian TOP100 Companies. In the 7th International Scientific Conference on Managing and Modelling of Financial Risks Modelling of Financial Risks of VSB Tech Univ Ostrava.
- Stryckova, L. (2017). The Relationship Between Company Returns and Leverage Depending on the Business Sector: Empirical Evidence from the Czech Republic. Journal of Competitiveness, 9(3), 98-110.
- Svobodová, L. (2013). Trends in the number of bankruptcies in the Czech Republic. International Scientific Conference on Hradec Economic Days 2013 – Economic Development and Management of Regions, 3(1), 393-399.
- Vlachý, J. (2018). Corporate Finance. Prague, Leges.
-
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: 919 Downloads: 169 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.
