Abstract

Determining and forecasting the financial situation of insurance companies of Ukraine has become an important issue of financial supervision in view of the need to ensure the sustainability of the financial sector and reduce the negative impact of the insolvency of insurance companies. Given the significant number of bankruptcies of insurance companies in Ukraine in recent years, a discriminatory method may be applied to improve off-site financial supervision. The paper analyzes the advantages and disadvantages of discriminatory models developed for companies producing goods and providing non-financial services to banking institutions and insurance companies. It is substantiated that in order to improve the quality of estimation and forecasting of the financial state of insurance companies in Ukraine, the development of a discriminant model should take into account the domestic specificity of the insurance business. The scientific work defines the stages of development and testing of the quality of a discriminant model for determining the financial status of insurance companies in Ukraine. For the development of a discriminant model, taking into account the existing statistical base, 31 indicators of activity of 12 insurance companies for 2015 were selected. The selection of statistical indicators for use as factors in a discriminant model was performed on the basis of a two-choice F-test, a Farrar-Globard algorithm, a matrix of pair coefficients of correlation. A discriminatory model for determining the financial status of insurance companies in Ukraine included net returns on equity, asset turnover ratios, insurance risk rates, and changes in equity. The application of the model allows you to determine whether the insurance company has a satisfactory or unsatisfactory financial condition. Approval of the developed discriminant model has proven its high quality.

Keywords: insurance company, financial status, discriminatory analysis, discriminatory function.

JEL Classification: C15, C35, G22, G33, M21.

Cite as: Kremen V., Riashchenko V., Bochkarova T. (2017). A Discriminant Analysis of Insurance Companies in Ukraine. Financial Markets, Institutions and Risks, 1(4), 65-73. DOI: 10.21272/fmir.1(4).65-73.2017

References

1. Anghelache, С., Armeanu, D. (2008). Application of Discriminant Analysis on Romanian Insurance Market. Theoretical and Applied Economics, 11 (528), 51-62.
2. Barr Richard, S., Thomas, S. (1996). Bank Failure Prediction Using DEA to Measure Management Quality, Southern Methodist University.
3. Beaver, W. H. (1966). Financial Rations and Predictions of Failure. Empirical Research in Accounting Selected Studies. Supplement to Journal of Accounting Research, 45-49.
4. Cherep, A. V., Komisarenko, О. А. (2013). Rozrobka modeli prohnozuvannia bankrutstva komertsiinykh bankiv Ukrainy na osnovi zarubizhnoho dosvidu [Development of a model for forecasting bankruptcy of commercial banks of Ukraine on the basis of foreign experience]. Financial and credit activity: problems of theory and practice, 1, 18-23.
5. Chesser, D. L. (1974). Predicting Loan Noncompliance. The Journal of Commercial Bank Lending, 56 (12), 28-38.
6. Conan, J., Holder, M. (1979). Variables explicatives de performance et controle de gestion dans les P.M.I., These d’Etat, CERG, Universite Paris Dauphine.
7. Erdogan, B. E. (2008). Bankruptcy of Turkish Commercial Banks Using Financial Ratios. Applied Mathematical Sciences, 60, 2973-2982.
8. Financial statements of insurance companies. Retrieved from: http://www.smida.gov.ua. Accessed 1 June 2017.
9. Fulmer, J. G. (1984). A Bankruptcy Classification Model For Small Firms. Journal of Commercial Bank Lending, 25-37.
10. Gilbert, L. R., Krishnagopal, M., Schwartz, К. B. (1990). Predicting Bankruptcy for Firms in Financial Distress. Journal of Business Finance & Accounting, 161-171.
11. Halling, M., Hayden, Е. (2008). Bank Failure Prediction: A Two-Step Survival Time Approach. IFC Bulletin, 28, 48-73.
12. Hanweck, G. A. (1977). Predicting Bank Failure. Research Papers in Banking and Financial Economics. Financial Studies Section, Division of Research and Statistics, Board of Governors of the Federal Reserve System.
13. Iankovets, T. M., Cherniuk, Yu. V. (2016). Porivniannia suchasnykh modelei diahnostyky ymovirnosti bankrutstva pidpryiemstva: zakordonnyi ta vitchyznianyi dosvid. Comparison of modern models for diagnosing the probability of bankruptcy of an enterprise: foreign and domestic experience. Economics. Investments: practice and experience, 20, 58-62.
14. Kaciak, E. (2000). Predicting bank failures in a newly emerging free-market economy. Perspectives-Electronic Journal of the American Association of Behavioral and Social Sciences, 105-117.
15. Martin, D. (1977). Early-warning of Bank Failure: A Logit Regression Approach. Journal of Banking and Finance, 1, 249-276.
16. Martіnez, Z. D., Menеndez, J. F., Vargas, J. S. (2004) See5 Algorithm versus Discriminant Analysis. An Application to the Prediction of Insolvency in Spanish Non-life Insurance Companies. Documentos de trabajo de la Facultad de Ciencias Económicas y Empresariales. Retrieved from: http://eprints.ucm.es/ 6836/1/0412.pdf.
17. Report of the National Commission that performs state regulation in the field of financial services markets. Retrieved from: https://nfp.gov.ua. Accessed 1 June 2017.
18. Springate, L.V. Gordon (1978). Predicting the Possibility of Failure in a Canadian Firm. Unpublished M.B.A. Research Project, Simon Fraser University.
19. Taffler, R. J., Tisshaw, H. (1977). Going, going, gone – four factors which predict. Accountancy, 50-54.
20. Tereshchenko, O. O. (2003). Dyskryminantna model intehralnoi otsinky finansovoho stanu pidpryiemstva Discriminant model of the integrated assessment of the financial condition of the enterprise. Ukraine economy, 8, 38-44.