Diagnostics of systemic risk impact on the enterprise capacity for financial risk neutralization: the case of Ukrainian metallurgical enterprises

• Authors: Irina Kolupaieva , Svitlana Pustovhar , Oleg Suprun , Olena Shevchenko

Abstracts

EN

Research background: A significant share of Ukrainian enterprises in modern conditions is accompanied by unprofitability of their activity. On the back of Ukrainian enterprises unprofitability, there is a problem of methodical provision of financial risk management, which lies in the fact that a major part of scientistific works in this area focus on the study of internal factors and indicators of financial risk. At the same time, the system risk is levelled out. Purpose of the article: The aim of the study is the improvement of enterprises' financial risk management tools based on the assessment of the company's ability to neutralize financial risk taking into account system risk effects. Methods: The methodological apparatus includes: The "weight center" method; expert appraisal method; multidimensional factor analysis method; neural network apparatus. Findings & Value added: As a result of the study, an approach to assessing the impact of system risk on the ability of an enterprise to neutralize financial risk is developed. The expert evaluation method is based on an integrated model that allows for estimation of the ability of metallurgical enterprises to neutralize financial risks. The system risk factors, namely the factor of commodity markets state, the political and demographic, fiscal, monetary factors as well as the factor of the external balance financial estimates, were determined. By constructing a neural network, elasticity of enterprises' ability to neutralize financial risk in relation to systemic risk factors was calculated. The proposed approach allows for conducting preventive financial risk diagnostics on the basis of assessing the current financial status and the ability to neutralize financial risk in an open economic system - taking into account the system risk impact.

Keywords

EN

enterprise financial risk; financial risk neutralization; enterprise insolvency; systemic risk

• Publisher: Instytut Badań Gospodarczych
• Journal: Oeconomia Copernicana
• Year: 2019
• Volume: 10
• Issue: 3
• Pages: 471-491

Dates

published

2019

Contributors

author

Irina Kolupaieva

• Kharkiv Institute of Trade and Economics of Kyiv National University of Trade and Economics

• https://orcid.org/0000000312560817

author

Svitlana Pustovhar

• Kharkiv Institute of Trade and Economics of Kyiv National University of Trade and Economics

• https://orcid.org/0000000241998044

author

Oleg Suprun

• Kharkiv Institute of Trade and Economics of Kyiv National University of Trade and Economics

• https://orcid.org/0000000244626115

author

Olena Shevchenko

• Donbass State Engineering Academy

• https://orcid.org/000000031825233X

References

• Altman, E., & Hotchkiss, E. (2006). Corporate financial distress and bankruptcy: predict and avoid bankruptcy, analyze and invest in distressed debt. Hoboken: John Wiley and Sons, Ltd.
• Antunes, F., Ribeiro, B., & Pereira, F. (2017). Probabilistic modeling and visualization for bankruptcy prediction. Applied Soft Computing, 60. doi: 10.1016/ j.asoc.2017.06.043.
• Azayite, F., & Achchab, S. (2016). Hybrid discriminant neural networks for bankruptcy prediction and risk scoring. Procedia Computer Science, 83. doi: 10.1016/j.procs.2016.04.149.
• Berent, T., Bławat, B., Dietl, M., Krzyk, P., & Rejman, R. (2017). Firm’s default - new methodological approach and preliminary evidence from Poland. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4). doi: 10.24136/eq.v12i4.39.
• Chan-Lau, J. (2006). Fundamentals-based estimation of default probabilities: a survey. Retrieved from http://ideas.repec.org/p/imf/imfwpa/06-149.html (05.02.2019).
• Florio, C., & Leoni, G. (2017). Enterprise risk management and firm performance: the Italian case. British Accounting Review, 49. doi: 10.1016/j.bar.2016.08.003.
• Fraser, J., & Simkins, B. (2016). The challenges of and solutions for implementing enterprise risk management. Business Horizons, 59. doi: 10.1016/j.bushor. 2016.06.007.
• Fulmer, J. G. Jr. (1984). Bankruptcy classification model for small firms. Journal of Commercial Bank Lending, 66(11).
• Hamilton, D. T., Sun, Zh., & Ding, M. (2011). Through-the-Cycle EDF credit measures. Moody's Analytics Methodology. Retrieved from http://www. moodysanalytics.com/~/media/Microsites/ERS/2011/through-cycle-EDF/Moo dysAnalytics_Through-the-Cycle%20EDF%20Measure%20Methodology%2 0Overview.pdf (05.02.2019).
• Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems with Applications, 117. doi: 10.1016/j.eswa.2018.09.039.
• Ismihan, M., & Ozkan, F. G. (2012). Public debt and financial development: a theoretical exploration. Economics Letters, 115. doi: 10.1016/j.econlet.2011. 12.040.
• Klebanova, T., Guryanova, L., & Bogonikolos, N. (2006). Modeling of financial flows of an enterprise under conditions of uncertainty. Kharkov: PH "INZHEK".
• Kliestik, T., Kocisova, K., & Misankova, M. (2015). Logit and Probit Model used For Prediction of Financial Health of Company. Procedia Economics and Finance, 23. doi: 10.1016/S2212-5671(15)00485-2.
• Kliestik, T., Kovacova, M., Podhorska, I., & Kliestikova, J. (2018а). Searching for key sources of goodwill creation as new global managerial challenge. Polish Journal of Management Studies, 17(1). doi: 10.17512/pjms.2018.17.1.12.
• Kliestik, T., Misankova, M., Valaskova, K., & Svabova, L. (2018b). Bankruptcy prevention: new effort to reflect on legal and social changes. Science and Engineering Ethics, 24(2). doi: 10.1007/s11948-017-9912-4.
• 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). doi: 10.24136/eq.2018.028
• Kočišová, K., & Mišanková, M. (2014). Discriminant analysis as a tool for forecasting company's financial health. Procedia - Social and Behavioral Sciences, 110. doi: 10.1016/j.sbspro.2013.12.961.
• Kovacova, M., & Kliestik, T. (2017). Logit and Probit application for the prediction of bankruptcy in Slovak companies. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4). doi: 10.24136/eq.v12i4.40.
• Malichová, E., & Ďurišová, M. (2015). Evaluation of financial performance of enterprises in IT sector. Procedia Economics and Finance, 34. doi: 10.1016/S2212-5671(15)01625-1.
• Menke, W. (2018). Factor analysis. Geophysical data analysis. New-York: Academic Press. doi: 10.1016/B978-0-12-813555-6.00010-1.
• Pustovhar, S. (2014a). Determination of complementary and destructive factors of the environment and assessment of their impact on the risk of financial insolvency of metallurgical enterprises of Ukraine. Business inform, 11.
• Pustovhar, S. (2014 b). Prediction of financial insolvency of metallurgical enterprises. Scientific Herald of Kherson State University, 9.
• Rousseau, R., Egghe, L., & Guns, R. (2018). Statistics. Becoming metric-wise. Chandos Publishing. doi: 10.1016/B978-0-08-102474-4.00004-2.
• Sitnik, Y. (2017). Method of rating assessment of the effectiveness of intellectualization of management systems. Ukrainian Journal of Applied Economics, 2(1).
• Springate, G. L. V. (1978). Predicting the possibility of failure in a canadian firm. Canada: Simon Fraser University.
• State Statistics Service of Ukraine. (2019). Retrieved from http://www.ukrstat.gov.ua/ (05.02.2019).
• Tereschenko, O., & Stetsko, M. (2017). Diagnosis of insolvency of enterprises as a technology to support financial decision-making. Effective economy, 3. Retrieved from http://www.economy. nayka.com.ua/?op=1&z=5521 (05.02.2019)
• The World Bank Group. (2019). Retrieved from http://www.worldbank.org (05.02.2019).
• Toffler, R., & Tishaw, H. (1977). Going, going, gone – four factors which predict. Accountancy, March.
• Tyrowicz, J., & van der Velde, L. (2018). Labor reallocation and demographics. Journal of Comparative Economics, 46. doi: 10.1016/j.jce.2017.12.003.
• Wilson, T. (1997). Portfolio Credit Risk: part I. Risk Magazine, September.

Identifiers

DOI

10.24136/oc.2019.023

Biblioteka Nauki

19090958