Financial crisis of real sector enterprises: an integral assessment
-
DOIhttp://dx.doi.org/10.21511/imfi.16(4).2019.31
-
Article InfoVolume 16 2019, Issue #4, pp. 366-381
- Cited by
- 830 Views
-
237 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Successful crisis resolution of the enterprise depends heavily on its timely detection, which is facilitated by the use of forecasting models. This allows understanding the scale of the problems in a timely manner and developing the appropriate measures, applying various financial mechanisms to prevent it, and in case of occurrence, reducing the amount of losses. In this context, it is important to choose the most optimal informational model that would provide the most objective forecasts, considering the financial activity peculiarities of the analyzed enterprise. Given a wide list of models that predict the financial crisis, there is a need to analyze and select the most accurate model for enterprises in the real economy. Ten Ukrainian machine builders are used to assess the bankruptcy probability using the most popular models; a taxonomic analysis was carried out, which allows systematizing a large amount of data and analyzing their impact on enterprise development. An integral index was determined, which allowed predicting the financial performance dynamics. For each enterprise, ten indicators were used characterizing their financial state for the period 2014–2018. It is substantiated that the selected models differ from each other by the set of initial data and the number of coefficients from four to seven. It is also determined that the efficient use of studied models is quite different; so when choosing a model to predict the bankruptcy probability, it is necessary to consider the peculiarities of the enterprise’s production activity, the accuracy in creating the financial statements and many other factors, including the presence of company’s shares in circulation at the stock market. It is worthwhile to use a taxonomic analysis to make a comprehensive comparison of the enterprise financial state and to substantiate the final choice of the bankruptcy forecasting model.
- Keywords
-
JEL Classification (Paper profile tab)G32, G33
-
References37
-
Tables4
-
Figures3
-
- Figure 1. Growth rates of the production value of the manufacture of machinery and equipment
- Figure 2 . Dynamics of the Ukrainian machine builders’ integral values according to models being studied (2014–2018)
- Figure 3. Dynamics of the taxonomy index change of machine-building enterprises for 2014–2018
-
- Table 1. Summary of business assessment results*
- Table A1. Normalized matrix of indicators of enterprises’ financial capacity for 2014–2018
- Table B1. The distance between indicators and reference vector of machine-building enterprises of Ukraine for 2014–2018
- Table C1. An integral indicator of taxonomy of Ukrainian machine-building enterprises for 2014–2018
-
- 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. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking & Finance, 32(8), 1541-1551.
- Aleksanyan, L., & Huiban, J.-P. (2016). Economic and Financial Determinants of Firm Bankruptcy: Evidence from the French Food Industry. Review of Agricultural, Food and Environmental Studies, 97, 89-108.
- Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman’s Z-Score Model. Journal of International Financial Management and Accounting, 28(2), 131-171.
- Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
- Altman, E. I. (1983). Corporate Financial Distress. A complete Guide to Predicting, Avoiding, and Dealing with Bankruptcy. John Wiley.
- Altman, E. I. (2018). A fifty-year retrospective on credit risk models, the Altman z-score family of models and their applications to financial markets and managerial strategies. Journal of Credit Risk, 14(4), 1-34.
- 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.
- Beaver, W. H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4, 71-111.
- Beaver, W. H. (1968). Alternative Accounting Measures as Predictors of Failure, Financial Ratios as Predictors of Failure. Journal of Accounting Research, 43, 113-122.
- Brindescu-Olariu, D. (2016). Assessment of the bankruptcy risk based on the solvency ratio. Theoretical and Applied Economics, XXIII 3(608), 257-266.
- Chesser, D. L. (1974). Predicting loan noncompliance. The Journal of Commercial Bank Lending, 56, 28-38.
- Ciesielski, P., Domeracki, M., & Gruszczynski, M. (2005). New bankruptcy prediction models for Polish companies (Working Paper 4-05). 6th Conference “Financial Management – Business, Banking and Finance in Emerging Markets.” Miedzyzdroje, Poland.
- Gilbert, L. R., Menon, K., & Schwartz, K. B. (1990). Predicting bankruptcy for firms in financial distress. Journal of Business Finance & Accounting, 17(1), 161-171.
- Global Bankruptcy Report. (2019). Dun & Bradstreet Worldwide Network.
- Haber, J. F. (2005). Assessing How Bankruptcy Prediction Models Are Evaluated. Journal of Business & Economics Research, 3(1), 87-92.
- Holder, C. (1979). Variables explicatives de performances et controle de gestion dans les P.M.I. Universite Paris Dauphine.
- Horváthová, J., & Mokrišová, M. (2018). Risk of Bankruptcy, Its Determinants and Models. Risks, 6(117).
- Hsiao, H.-F., Lin, S.-H., & Hsu, A.-C. (2010). Earnings management, corporate governance, and auditor’s opinions: a financial distress prediction model. Investment Management and Financial Innovations, 7(3), 29-40.
- Jabeur, S. B., & Fahmi, Y. (2018). Forecasting financial distress for French firms: a comparative study. Empirical Economics, 54(3), 1173-1186.
- Káčer, M., Ochotnický, P., & Alexy, M. (2019). The Altman’s revised Z’-score model, non-financial information and macroeconomic variables: Case of Slovak SMEs. Ekonomicky casopis, 4, 335-366.
- Kluza, K. (2017). Risk assessment of the local government sector based on the ratio analysis and the DEA method. Evidence from Poland. Eurasian Economic Review, 7(3), 329-351.
- Kozlovskyi, S., Butyrskyi, A., Poliakov, B., Bobkova, A., Lavrov, R., Ivanyuta, N. (2019). Management and comprehensive assessment of the probability of bankruptcy of Ukrainian enterprises based on the methods of fuzzy sets theory. Problems and Perspectives in Management, 17(3), 370-381.
- Gawron, K., Yakymchuk, A., & Tyvonchuk, O. (2019). The bankrupt entity’s assets valuation methods: Polish approach. Investment Management and Financial Innovations, 16(3), 319-331.
- Matviichuk, A. V. (2010). Bankruptcy prediction in transformational economy: discriminant and fuzzy logic approaches. Fuzzy Economic Review, 15(1), 21-38.
- Matviichuk, A. V. (2013). Nechitki, neiromerezhevi ta dyskryminantni modeli diahnostuvannia mozhlyvosti bankrutstva pidpryiemstv [Fuzzy, Neural Network and Discriminant Models for Enterprise Bankruptcy Possibility Diagnosis]. Neiro-nechitki tekhnolohii modeliuvannia v ekonomitsi – Neuro-Fuzzy Modeling Technologies in Economy, 2, 71-118. (In Ukrainian).
- Pakdaman, H. (2018). Investigating the ability of Altman and Springate and Zmijewski and grover bankruptcy prediction models in Tehran Stock Exchange. Espacios, 39(14), 33.
- Paseková, M., Fišerová, Z., & Bařinová, D. (2016). Bankruptcy in Czech Republic – from the perspectives of debtors, creditors, and the judiciary, 2008–2013. Journal of International Studies, 9(1), 180-191.
- Pisula, T., Brożyna, J., & Mentel, G. (2016). Statistical Methods of the Bankruptcy Prediction in the Logistics Sector in Poland and Slovakia. Transformations in Business & Economics, 15(1)(37), 93-114.
- Prusak, B. (2018). Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries International. Journal of Financial Studies, 6(60).
- Spicka, J. (2013). The financial condition of the construction companies before bankruptcy. European Journal of Business and Management, 5(23), 160-169
- Springate, G. L. V. (1978). Predicting the possibility of failure in a Canadian firm (Unpublished master’s thesis). Simon Fraser University, Canada.
- Szetela, B., Mentel, G., & Brożyna, J. (2019). Modelling European sovereign default probabilities with copulas. Economic Research-Ekonomska Istraživanja, 32(1), 1716-1726.
- Taffler, R. J. (1984). Empirical Methods for the Monitoring of U.K. Corporations. Journal of Banking and Finance, 8(2), 199-227.
- Tereshchenko, O. O. (2004). Antykryzove finansove upravlinnia na pidpryiemstvi [Anti-crisis financial management at the enterprise]. Kiyv: KNEU. (In Ukrainian).
- Toffler, R., & Tishaw, H. (1977). Going, going, gone – four factors which predict. Accountancy, 50-54.
- Tymoshchuk, O., Kirik, O., & Dorundiak, K. (2019). Comparative Analysis of the Methods for Assessing the Probability of Bankruptcy for Ukrainian Enterprises. 15th International Scientific Conference on Intellectual Systems of Decision Making and Problems of Computational Intelligence (pp. 281-293). ISDMCI 2019, 21-25 May 2019.