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Financial Analytics: Science and Experience
 

On the issue of forecasting the solvency of small and medium-sized businesses and probability of their bankruptcy

Vol. 9, Iss. 8, FEBRUARY 2016

PDF  Article PDF Version

Received: 16 November 2015

Accepted: 3 February 2016

Available online: 27 February 2016

Subject Heading: RISK, ANALYSIS AND EVALUATION

JEL Classification: C25, C51, G17, G33

Pages: 47-62

Bol'shakova O.E. Sberbank of Russia, Volgo-Vyatka Office, Nizhny Novgorod, Russian Federation
bolshakova.olia.337@gmail.com

Maksimov A.G. National Research University – Higher School of Economics, Nizhny Novgorod, Russian Federation
amaksimov@hse.ru

Maksimova N.V. National Research University – Higher School of Economics, Nizhny Novgorod, Russian Federation
nvmaksimova@hse.ru

Importance The article discusses the issues of evaluating the financial standing of small and medium-sized businesses.
     Objectives The research aims at setting up and improving models to assess the solvency of small and medium-sized businesses and forecast their bankruptcy.
     Methods We reviewed various types of models. Using econometric tool, we built and evaluated a number of logit models. Coefficients of models were reviewed with the maximum likelihood models. The research also involves various economic and statistical methods, i.e. factor analysis algorithms, step-by-step selection methods, variance inflation factor, etc. Classification characteristics of models were tested with the training sample, considering the area under the ROC curve and analysis of errors of Type 1 and 2. Empirical data proceed from financial statements of companies.
     Results The quality of models improved if we take into account the industry of small and medium-sized businesses, design and include parameters describing the corporate resource management. The models we built for diagnostics demonstrated good classification and forecasting trends.
     Conclusions and Relevance The proposed models, if used, will help identify distressed companies and assess bankruptcy risks. It will be valuable for business proprietors, lenders, counter-parties, and judicial authorities when they contemplate initiating one of bankruptcy procedures.

Keywords: small business, medium-sized business, diagnostics, model, solvency, econometric modeling

References:

  1. Smelova T.A., Merzlikina G.S. Otsenka ekonomicheskoi sostoyatel’nosti v antikrizisnom upravlenii predpriyatiem [Evaluating the economic solvency as part of corporate crisis management]. Volgograd, Politekhnik Publ., 2003, 191 p.
  2. Aziz M.A., Dar H.A. Predicting Corporate Bankruptcy: Whither Do We Stand? Corporate Governance International Journal of Business in Society, 2005, vol. 6, no. 1, pp. 18–33.
  3. Demeshev B.B., Tikhonova A.S. Prognozipovanie bankrotstva rossiiskikh kompanii: mezhotraslevoe sravnenie [Forecasting bankruptcy of the Russian businesses: a cross-sectoral comparison]. Ekonomicheskii zhurnal Vysshei shkoly ekonomiki= HSE Economic Journal, 2014, vol. 18, no. 3, pp. 359–386.
  4. Altman E.I. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 1968, vol. 23, iss. 4, рp. 589–609.
  5. Springate G.L.V. Predicting the Possibility of Failure in a Canadian Firm. Simon Fraser University, 1978.
  6. Fulmer J.G.Jr., Moon J.E., Gavin T.A., Erwin M.J. A Bankruptcy Classification Model for Small Firms. Journal of Commercial Bank Lending, 1984, no. 7, pp. 25–37.
  7. Fedotova M.A. Sravnitel'nyi analiz metodik otsenki kreditosposobnosti zaemshchika [The contrastive analysis of techniques for evaluating the borrower's creditworthiness]. Vestnik Samarskogo gosudarstvennogo ekonomicheskogo universiteta = Vestnik of Samara State University of Economics, 2010, no. 1, pp. 101–106.
  8. Zaitseva O.P. Antikrizisnyi menedzhment v rossiiskoi firme [Crisis management in the Russian company]. Sibirskaya finansovaya shkola = Siberian Financial School, 1998, no. 11-12, pp. 66–73.
  9. Davydova G., Belikov A. Metodika kolichestvennoi otsenki riska bankrotstva predpriyatii [Methods for assessing the bankruptcy risk of companies]. Upravlenie riskom = Risk Management, 1999, no. 3, pp. 13–20.
  10. Yazdanfar D. The Bankruptcy Determinants of Swedish SME. Belfast, Institute for Small Business & Entrepreneurship, 2008, pp. 1–14.
  11. Lugovskaya L. Predicting Default of Russian SMEs on the Basis of Financial and Non-Financial Variables. Journal of Financial Services Marketing, 2010, vol. 14, iss. 4, pp. 301–313.
  12. Altman E.I., Sabato G. Modeling Credit Risk for SMEs: Evidence from the US Market. Abacus, 2007, vol. 43, iss. 3, pp. 332–357.
  13. Altman E.I., Sabato G., Wilson N. The Value of Non-Financial Information in SME Risk Management. Journal of Credit Risk, 2010, vol. 6, iss. 2, pp. 95–127.
  14. Ohlson J.A. Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 1980, vol. 18, no. 1, pp. 109–131.
  15. Khaidarshina G.A. Kompleksnaya model' otsenki riska bankrotstva [A comprehensive model for the bankruptcy risk assessment]. Finansy = Finance, 2009, no. 2, pp. 67–69.
  16. Shumway T. Forecasting Bankruptcy More Accurately: A Simple Hazard Model. Journal of Business, 2001, vol. 74, no. 1, pp. 101–124.
  17. Bol'shakova O.E., Maksimov A.G., Maksimova N.V. O modelyakh diagnostiki sostoyatel'nosti predpriyatii malogo i srednego biznesa [On models for assessing the solvency of small and medium-sized businesses]. Vestnik VGU. Seriya Ekonomika i Upravlenie = Proceedings of Voronezh State University. Series: Economics and Management, 2014, no. 3, pp. 131–142.
  18. Tam K.Y., Kiang M.Y. Managerial Applications of Neural Networks: The Case of Bank Failure Predictions. Management Science, 1992, vol. 38, no. 7, pp. 926–947.
  19. Wilson R.L., Sharda R. Bankruptcy Prediction Using Neural Networks. Decision Support Systems, 1994, vol. 11, no. 5, pp. 545–557.
  20. Altman E.I., Marco G., Varetto F. Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks: the Italian Experience. Journal of Banking & Finance, 1994, vol. 18, no. 3, pp. 505–529.
  21. Wei L., Li J., Chen Z. Credit Risk Evaluation Using Support Vector Machine with Mixture of Kernel. Proceedings of the 7th International Conference on Computational Science. Lecture Notes in Computational Science and Engineering, 2007, vol. 4488, pp. 431–438.
  22. Bazhenov O.V. Opredelenie normativnykh znachenii klyuchevykh pokazatelei finansovo-khozyaistvennoi deyatel'nosti predpriyatii mednoi promyshlennosti [Determining standards of key indicators of financial and business performance of entities operating in the copper industry]. Diskussiya = Discussion, 2003, no. 4, pp. 28–34.
  23. Pulic A. VAICTM – an Accounting Tool for IC Management. International Journal of Technology Management, 2000, vol. 20, iss. 5-8, pp. 702–714.
  24. Farrell M.J. The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, 1957, vol. 120, no. 3, pp. 253–290.
  25. Meeusen W., Van den Broeck J. Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error. International Economic Review, 1977, vol. 18, iss. 2, pp. 435–444.
  26. Battese G.E., Coelli T.J., Colby T.C. Estimation of Frontier Production Functions and the Efficiencies of Indian Farms Using Panel Data from ICRISAT Village Level Studies. Journal of Quantitative Economics, 1989, no. 5, pp. 327–348.
  27. Afanas'ev M.Yu. Model' proizvodstvennogo potentsiala s upravlyaemymi faktorami neeffektivnosti [The production potential model with controllable inefficiency factors]. Prikladnaya ekonometrika = Applied Econometrics, 2006, no. 4, pp. 74–89.

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