Financial threat profiles of industrial enterprises in Poland

Abstract

Research background: The nature of bankruptcy has been the subject of interest for economic theories, both positive?identifying relationships between bankruptcy and other economic categories ? and normative, shaping the rules for the proper regulation of bankruptcy. In turn, the functioning of an enterprise in conditions of risk, financial threat, and finally a crisis that could lead to bankruptcy, are of interest to management. The interpenetration of these two dimensions provided the motivation for this study, which assumes a bottom-up approach: from individual results to summarised multi-sectional comparisons.

Purpose of the article: The purpose of the research was to evaluate the level, directions of change, and structure of the degree of financial threat in industrial enterprises. The period under analysis was 2007?2018 and the whole population of industrial enterprises in Poland (15,999 entities) was examined. The enterprises were small and medium-sized enterprises (SMEs) as well as large enterprises (LEs). The financial analysis covered macro-, meso-, and microeconomic levels.

Methods: The analysis was conducted using a comparative approach and financial threat predictions obtained from the original multivariable logit model. Heat maps were used to evaluate the intensity of changes in financial threat. The displacement of objects in structures was studied, ordered, and classified. Four normative standards of threat scenarios were defined and then used to evaluate similarities in the profiles of the structures examined, using the similarity measure. The ranking and its variability were analysed in the assessment of profiles.

Findings & value added: As the result of the research, properties were described and profiles were determined for the structures in terms of the degree of threat and its correlation with rate of bankruptcy and creating added value. The originality of the research comes from the use of novel dynamic logit models. The added value is a unique study on the entire population of industrial enterprises in the national economy and a methodology for identifying financial threat profiles and their similarity at subsequent aggregation levels (the micro-, meso-, and macro-levels). This made it possible to derive patterns and regularities for economic policy and guidelines for business management.

Keywords

financial threat, bankruptcy, early warning systems, risk management, enterprise crisis

References

1. Altman, E. I. (1993). Corporate financial distress and bankruptcy. A complete guide to predicting & avoiding distress and profiting from bankruptcy. New York: John Wiley & Sons Press.
   View in Google Scholar
2. Altman, E. I., & Narayanan, P. (1997). An international survey of business failure classification models. Financial Markets, Institutions and Instruments, 6(2), 1?57. doi: 10.1111/1468-0416.00010. DOI: https://doi.org/10.1111/1468-0416.00010
   View in Google Scholar
3. Altman, E. I., Sabato, G., & Wilson, N. (2010). The value of non-financial information in small and medium-sized enterprise risk management. Journal of Credit Risk, 2(6), 95?127. doi: 10.21314/JCR.2010.110. DOI: https://doi.org/10.21314/JCR.2010.110
   View in Google Scholar
4. Ansoff, H. I. (1985). Strategic management. Warszawa: PWE Press.
   View in Google Scholar
5. Antonowicz, P. (2007). Methods of evaluation and economic and financial condition of enterprises. Gdańsk: ODiDK.
   View in Google Scholar
6. Argenti, J. (1976). Corporate collapse. The causes and symptoms. London: McGraw?Hill Book.
   View in Google Scholar
7. Back, B., Laitinen, T., Hekanaho, J., & Sere, K. (1997). The effect of sample size on different failure prediction methods. Turku Centre for Computer Science, TUCS Technical Report, 155, 1?23.
   View in Google Scholar
8. Bărbu?ă-Mi?u, N., & Codreanu, E.-S. (2014). Analysis and prediction of the bankruptcy risk in romanian building sector companies. Ekonomika, 93(2), 131?146. doi: 10.15388/EKON.2014.2.3542. DOI: https://doi.org/10.15388/Ekon.2014.2.3542
   View in Google Scholar
9. Bărbu?ă-Mi?u, N., & Madaleno, M. (2020). Assessment of bankruptcy risk of large companies, European countries evolution analysis. Journal of Risk and Financial Management, 13(3), 58. doi: 10.3390/jrfm13030058. DOI: https://doi.org/10.3390/jrfm13030058
   View in Google Scholar
10. Beaver, W. H. (1968). Alternative accounting measures as predictors of failure. Accounting Review, 43(1), 113?122. doi: 10.2307/244122.
    View in Google Scholar
11. Cabała, P. (2008). Enterprise early warning systems. Kraków: Uniwersytet Ekonomiczny w Krakowie.
    View in Google Scholar
12. Crocford, G. N. (1982). The bibliography and history of risk management. Geneva Papers on Risk and Insurance, 7(2), 169?179. doi: 10.1057/gpp.1982.10. DOI: https://doi.org/10.1057/gpp.1982.10
    View in Google Scholar
13. Cultrera L., & Brédart X. (2016). Bankruptcy prediction: the case of Belgian SMEs. Review of Accounting and Finance, 15(1), 101?119. doi: 10.1108/RAF-06-2014-0059. DOI: https://doi.org/10.1108/RAF-06-2014-0059
    View in Google Scholar
14. Drucker, P. F. (2010). The practice of management. New York: Harper Business.
    View in Google Scholar
15. Fijorek, K., Kaczmarek, J., Kolegowicz, K., & Krzemiński, P. (2011). The concept of research on the state and performance of enterprises for the purpose of the Rapid Reaction Instrument. Assumptions of the "Early Warning System" solution - Methods and tools for monitoring the economy in the microeconomic component. Kraków: Małopolska Szkoła Administracji Publicznej Uniwersytetu Ekonomicznego w Krakowie.
    View in Google Scholar
16. Fijorek, K., & Sokołowski, A. (2012). Separation?resistant and bias?reduced logistic regression: STATISTICA macro. Journal of Statistical Software, 47, 1?12. doi: 10.18637/jss.v047.c02. DOI: https://doi.org/10.18637/jss.v047.c02
    View in Google Scholar
17. Firth, D. (1993). Bias reduction of maximum likelihood estimates. Biometrika, 80(1), 27?38. doi: 10.2307/2336755. DOI: https://doi.org/10.1093/biomet/80.1.27
    View in Google Scholar
18. Greiner, L. E. (1972). Evolution and revolution as organizations grow. Harvard Business Review, 7/8, 37?46. doi: 10.1111/j.1741-6248.1997.00397.x. DOI: https://doi.org/10.1111/j.1741-6248.1997.00397.x
    View in Google Scholar
19. Gupta, J., Barzotto, M., & Khorasgani, A. (2018). Does size matter in predicting SMEs failure? International Journal of Finance and Economics. Advance online publication. doi: 10.2139/ssrn.2638485. DOI: https://doi.org/10.1002/ijfe.1638
    View in Google Scholar
20. Hadasik, D. (1998). Bankruptcy of companies in Poland and methods of forecasting it. Zeszyty Naukowe, Akademia Ekonomiczna w Poznaniu, 153, 1?198.
    View in Google Scholar
21. Hafiz, A. A., Lukumon, O. O., Hakeem, A. O., Vikas K., Saheed, O. A., Olugbenga, O. A., & Muhammad, B. (2018). Systematic review of bankruptcy prediction models: towards a framework for tool selection. Expert Systems with Applications, 94, 164?184. doi: 10.1016/j.eswa.2017.10.040. DOI: https://doi.org/10.1016/j.eswa.2017.10.040
    View in Google Scholar
22. Hosmer, D. W., & Lemeshow, S. (1989). Applied logistic regression. New York: John Wiley & Sons Publishing.
    View in Google Scholar
23. Hudáková, M., & Dvorský, J. (2018). Assessing the risks and their sources in dependence on the rate of implementing the risk management process in the SMEs. Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(3), 543?567. doi: 10.24136/eq.2018.027. DOI: https://doi.org/10.24136/eq.2018.027
    View in Google Scholar
24. Jajuga, K. (2006). Statistical models of early warning - formal methods. Barometr Regionalny, 6, 52?55. Retrieved from http://br.wszia.edu.pl/zeszyty/pdfs/br 06_07jajuga.pdf. DOI: https://doi.org/10.56583/br.1655
    View in Google Scholar
25. Juszczyk, S. (2010). Forecasting of bankruptcy of enterprises. Ekonomista, 5, 701?728.
    View in Google Scholar
26. Kaczmarek, J. (2010). Counteracting the crisis in the economies of new EU member states in the context of the development of integration processes. In M. Lanfranchi (Ed.). The community integration process between Eastern and Southern Europe. Messina: Edizioni Dr. Antonino Sfaneni, 19?39.
    View in Google Scholar
27. Kaczmarek, J. (2014). A crisis and a treat vs. the financial security aspects of going concern. Economic Horizons, 16, 195?209. doi: 10.5937/ekonhor1403 195K. DOI: https://doi.org/10.5937/ekonhor1403195k
    View in Google Scholar
28. Kaczmarek, J. (2019). The mechanisms of creating value vs. financial security of going concern - sustainable management. Sustainability, 11(8), 2278, 1?24. doi: 10.3390/su11082278. DOI: https://doi.org/10.3390/su11082278
    View in Google Scholar
29. Kanapickiene R., & Marcinkevicius R. (2014). Possibilities to apply classical bankruptcy prediction models in the construction sector in Lithuania. Economics and Management, 19(4). doi: 10.5755/j01.em.19.4.8095. DOI: https://doi.org/10.5755/j01.em.19.4.8095
    View in Google Scholar
30. Kaplan, R., & Norton, D. (2006). The balanced scorecard: translating strategy into action. Boston: Harvard Business Press.
    View in Google Scholar
31. Kharbanda, O. P., & Stallworthy, E. A. (1985). Corporate failure. Prediction, panacea and prevention. London?New York: McGraw?Hill.
    View in Google Scholar
32. Kliestik, T., Vrbka, J., & Rowland, Z. (2018). Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis. Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(3), 569?593. doi: 10.24136/eq. 2018.028. DOI: https://doi.org/10.24136/eq.2018.028
    View in Google Scholar
33. Kovacova, M., Kliestik, T., Valaskova, K., Durana, P., & Juhaszova, Z. (2019). Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries. Oeconomia Copernicana, 10(4), 743?772. doi: 10.2413 6/oc.2019.034. DOI: https://doi.org/10.24136/oc.2019.034
    View in Google Scholar
34. Lam, J. (1985). Ten predictions for risk management. RMA Journal, 5, 84?85.
    View in Google Scholar
35. Long, S. J. (1997). Regression models for categorical and limited dependent variables. London: Thousand Oaks, SAGE Publications.
    View in Google Scholar
36. Obłój, K. (1987). Organisational crises. Przegląd Organizacji, 1, 7?14.
    View in Google Scholar
37. Odunaiya, T. (2013). A study of the accuracy of bankruptcy prediction models: can impending bankruptcies on the UK high street be predicted? University of Westminster.
    View in Google Scholar
38. Platt, H. D., & Platt, M. B. (2002). Predicting corporate financial distress: reflection on choice?based sample bias. Journal of Economics and Finance, 26(2), 184?199. doi: 10.1007/BF02755985. DOI: https://doi.org/10.1007/BF02755985
    View in Google Scholar
39. Pozzoli M., & Paolone F. (2017). Corporate financial distress. A study of the Italian manufacturing industry. Cham: Springer. doi: 10.1007/978-3-319-67355-4. DOI: https://doi.org/10.1007/978-3-319-67355-4
    View in Google Scholar
40. Prusak, B. (2005). Modern methods of forecasting financial risks for companies. Warszawa: Difin.
    View in Google Scholar
41. Quinn, B., & Cameron, K. (1983). Organizational life cycles and shifting criteria of effectiveness: some preliminary evidence. Management Science, 29(1), 33?51. doi: 10.1287/mnsc.29.1.33. DOI: https://doi.org/10.1287/mnsc.29.1.33
    View in Google Scholar
42. Rappaport, A. (1998). Creating shareholder value: a guide for managers and investors. New York, USA: The Free Press.
    View in Google Scholar
43. Senbet, L. W., & Wang, T. Y. (2012). Corporate financial distress and bankruptcy: a survey (foundations and trends in finance). Boston: Now Publishers Inc. DOI: https://doi.org/10.2139/ssrn.2034646
    View in Google Scholar
44. Slatter, S., & Lovett, D. (1999). Corporate recovery: managing companies in distress. Frederick (MD): Beard Books.
    View in Google Scholar
45. Smart, C. F., Thompson, W. A., & Vertinsky, I. (1978). Diagnosing corporate effectiveness and susceptibility to crisis. Journal of Business Administration, 9, 57?96.
    View in Google Scholar
46. Smith, M., & Liou, D. (2007). Industrial sector and financial distress. Managerial Auditing Journal, 22(4), 376?391. doi: 10.1108/02686900710741937. DOI: https://doi.org/10.1108/02686900710741937
    View in Google Scholar
47. Sobczyk, E. J., Kaczmarek, J., Fijorek, K., & Kopacz, M. (2020). Efficiency and financial standing of coal mining enterprises in Poland in terms of restructuring course and effects. Gospodarka Surowcami Mineralnymi, 36(2), 127?152. doi: 10.24425/gsm.2020.132565. DOI: https://doi.org/10.24425/gsm.2020.132565
    View in Google Scholar
48. Špička, J. (2013). The financial condition of the construction companies before bankruptcy. European Journal of Business and Management, 5(23), 160?169.
    View in Google Scholar
49. Svabova, L., & Durica, M. (2019). Being an outlier: a company non-prosperity sign? Equilibrium. Quarterly Journal of Economics and Economic Policy, 14(2), 359?375. doi: 10.24136/eq.2019.017. DOI: https://doi.org/10.24136/eq.2019.017
    View in Google Scholar
50. Syamni, G., Majid, M. S. A., & Siregar, W. V. (2018). Bankruptcy prediction models and stock prices of the coal mining industry in Indonesia. Etikonomi, 17(1). doi: 10.15408/etk.v17i1.6559. DOI: https://doi.org/10.15408/etk.v17i1.6559
    View in Google Scholar
51. Tobback, E., Bellotti, T., Moeyersoms, J., Stankova, M., & Martens, D. (2017). Bankruptcy prediction for SMEs using relational data. Decision Support Systems, 102, 69?81. doi: 10.1016/j.dss.2017.07.004. DOI: https://doi.org/10.1016/j.dss.2017.07.004
    View in Google Scholar
52. Valaskova, K., Kliestik, T., & Kovacova, M. (2018). Management of financial risks in Slovak enterprises using regression analysis. Oeconomia Copernicana, 9(1), 105?121. doi: 10.24136/oc.2018.006. DOI: https://doi.org/10.24136/oc.2018.006
    View in Google Scholar
53. Weibel, P. F. (1973). The value of criteria to judge credit worthiness in the lending of banks. Stuttgart: Bern.
    View in Google Scholar
54. Winston, W., Angrisani, D. P., & Goldman, R. L. (1997). Predicting successful hospital mergers and acquisitions: a financial and marketing analytical tool, New York: Routledge.
    View in Google Scholar
55. Zelek, A. (2003). Corporate crisis management - a strategic perspective. Warszawa: Instytut Organizacji i Zarządzania w Przemyśle ?ORGMASZ?.
    View in Google Scholar