Finance and Credit

Prediction of bankruptcy of small and medium-sized business entities in Russia

Vol. 24, Iss. 11, NOVEMBER 2018

Received: 10 November 2017

Received in revised form: 11 January 2018

Accepted: 29 August 2018

Available online: 29 November 2018

Subject Heading: THEORY OF FINANCE

JEL Classification: C35, D24

Pages: 2537–2552

Fedorova E.A. Financial University under Government of Russian Federation, Moscow, Russian Federation

Musienko S.O. Financial University under Government of Russian Federation, Moscow, Russian Federation

Fedorov F.Yu. OOO RedSys, Moscow, Russian Federation

ORCID id: not available

Subject This article analyzes the regulatory and legal framework, defining criteria of recognition of the enterprise bankrupt and compares these criteria with forecasting of bankruptcy of small and medium-sized enterprises.
Objectives The article aims to determine the regulatory values of the criteria used in the legislative acts of the Russian Federation, as well as the most frequently used ones in foreign studies when predicting the bankruptcy of small and medium-sized enterprises.
Methods For the study, we used the following methods of classification: the Random Decision Forests method, Classification And Regression Tree (CART) analysis technique, Gradient Tree Boosting technique, and the Bagging ensemble meta-algorithm.
Results The article shows that the current criteria specified in the legislative acts as criteria for determining bankruptcy are not universal and require clarification for small and medium-sized enterprises.
Conclusions and Relevance Legislative criteria to define bankruptcy require refinement taking into account the size of an enterprise. The results of the study can be used by small and medium-sized enterprises to predict the likelihood of bankruptcy and make timely management decisions, and the legislative powers to introduce amendments to legislation acts.

Keywords: bankruptcy, SME, business smashup criteria


  1. Sokolinskaya N.E., Kupriyanova L.M. (Eds). Kreditovanie kak vazhneishii faktor razvitiya malogo biznesa v Rossii: monografiya [Lending as the most important factor in the development of small business in Russia: a monograph]. Moscow, KnoRus Publ., 2011, 232 p.
  2. Kravchenko E.N. (Ed.). Maloe predprinimatel'stvo v sovremennoi Rossii: konkurentsiya, planirovanie, finansy, riski: monografiya [Small business in modern Russia: competition, planning, finance, risk: a monograph]. Wrocław, Fond Russko-pol'skii institut Publ., 2015, 280 p.
  3. Gordina V.V. [Problems and peculiarities of small business crediting at the present stage]. Finansovaya analitika: problemy i resheniya = Financial Analytics: Science and Experience, 2014, vol. 7, iss. 7, pp. 32–38. URL: Link (In Russ.)
  4. Tobback E., Bellotti T., Moeyersoms J. et al. Bankruptcy Prediction for SMEs Using Relational Data. Decision Support Systems, 2017, vol. 102, pp. 69–81. URL: Link
  5. Bălan M. Stochastic Methods for Prediction of the Bankruptcy Risk of SMEs. Procedia Economics and Finance, 2012, vol. 3, pp. 125–131. URL: Link00130-X
  6. Campa D., Camacho-Miñano M. The Impact of SME's Pre-Bankruptcy Financial Distress on Earnings Management Tools. International Review of Financial Analysis, 2015, vol. 42, pp. 222–234. URL: Link
  7. Bol'shakova O.E., Maksimov A.G., Maksimova N.V. [On the issue of forecasting the solvency of small and medium-sized businesses and probability of their bankruptcy]. Finansovaya analitika: problemy i resheniya = Financial Analytics: Science and Experience, 2016, vol. 9, iss. 8, pp. 47–62. URL: Link (In Russ.)
  8. Demeshev B.B., Tikhonova A.S. [Dynamics of predictive power of insolvency models for Russian small-medium enterprises: wholesale and retail trade]. Korporativnye finansy = Journal of Corporate Finance Research, 2014, vol. 8, no. 3, pp. 4–22. URL: Link (In Russ.)
  9. Kayasheva E.V. [Default probability modeling for micro and small enterprises]. Finansovaya analitika: problemy i resheniya = Financial Analytics: Science and Experience, 2014, vol. 7, iss. 17, pp. 44–56. URL: Link (In Russ.)
  10. Fedorova E.A., Dovzhenko S.E., Fedorov F.Yu. [Bankruptcy-prediction models for Russian enterprises: specific sector-related characteristics]. Problemy prognozirovaniya = Problems of Forecasting, 2016, no. 3, pp. 32–40. URL: Link (In Russ.)
  11. Fedorova E.A., Fedorov F.Yu. [Forecasting of bankruptcy of the enterprise in the transport industry]. Finansovyi menedzhment = Financial Management, 2015, no. 5, pp. 3–11. (In Russ.)
  12. Altman E.I. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 1968, vol. 23, no. 4, pp. 589–609. URL: Link
  13. Springate G.L.V. Predicting the Possibility of Failure in a Canadian Firm. Simon Fraser University, January, 1978, 164 p.
  14. Taffler R.J. The Assessment of Company Solvency and Performance using a Statistical Model. Accounting and Business Research, 1983, vol. 13, iss. 52, pp. 295–308.
  15. Fulmer J.G. Jr., Moon J.E., Gavin T.A. et al. A Bankruptcy Classification Model for Small Firms. Journal of Commercial Bank Lending, 1984, no. 7, pp. 25–37.
  16. Filipe S.F., Grammatikos T., Michala D. Forecasting Distress in European SME Portfolios. Journal of Banking & Finance, 2016, vol. 64, no. 1, pp. 112–135. URL: Link
  17. Smaranda C. Scoring Functions and Bankruptcy Prediction Models – Case Study for Romanian Companies. Procedia Economics and Finance, 2014, vol. 10, pp. 217–226. URL: Link00296-2
  18. Fedorova E.A., Lazarev M.P., Fedin A.V. [Forecasting the entity's bankruptcy in line with the operating environment factors]. Finansovaya analitika: problemy i resheniya = Financial Analytics: Science and Experience, 2016, vol. 9, iss. 42, pp. 2–12. URL: Link (In Russ.)
  19. Filobokova L.Yu. [Methodological approaches to rapid assessment of the financial and economic situation of small enterprises]. Finansovaya analitika: problemy i resheniya = Financial Analytics: Science and Experience, 2013, vol. 6, iss. 39, pp. 2–6. URL: Link (In Russ.)
  20. Schapire R.E. The Boosting Approach to Machine Learning: An Overview. In: Denison D.D., Hansen M.H., Holmes C.C., Mallick B., Yu B. (eds) Nonlinear Estimation and Classification. Lecture Notes in Statistics, vol 171. Springer, New York, NY, pp. 149–171. URL: Link
  21. Booth A., Gerding E.H., McGroarty F. Automated Trading with Performance Weighted Random Forests and Seasonality. Expert Systems with Applications, 2014, vol. 41, iss. 8, pp. 3651–3661. URL: Link
  22. Chung-Ying Yeh, Shih-Kuo Yeh, Ren-Raw Chen. Liquidity Discount in the Opaque Market: The Evidence from Taiwan's Emerging Stock Market. Pacific-Basin Finance Journal, 2014, vol. 29, pp. 297–309. URL: Link
  23. Bol'shakova O.E., Maksimov A.G., Maksimova N.V. [About models of diagnostics of solvency of small and medium-sized business enterprises]. Vestnik Voronezhskogo gosudarstvennogo universiteta. Seriya: ekonomika i upravlenie = Proceedings of Voronezh State University. Series: Economics and Management, 2014, no. 3, pp. 131–142. URL: Link (In Russ.)
  24. Corazza M., Funari S., Gusso R. Creditworthiness Evaluation of Italian SMEs at the Beginning of the 2007–2008 crisis: An MCDA Approach. The North American Journal of Economics and Finance, 2016, vol. 38, pp. 1–26. URL: Link

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Vol. 24, Iss. 11
November 2018