Bankruptcy prediction for Russian companies: Application of combined classifiers

Expert Systems with Applications

Volumn 40, Issue 18, 2013, Pages 7285-7293

  Fedorova, Elena ^a   Gilenko, Evgenii ^b   Dovzhenko, Sergey ^b  

^a FINANCIAL UNIVERSITY UNDER THE GOVERNMENT OF THE RUSSIAN FEDERATION   (Russian Federation)

^b ST PETERSBURG STATE UNIVERSITY   (Russian Federation)

Author keywords

AdaBoost; Artificial neural networks; Bankruptcy prediction; Classification and regression trees; Logit regression

Indexed keywords

BANKRUPTCY PREDICTION; CLASSIFICATION AND REGRESSION TREE; COMBINED CLASSIFIERS; EFFECTIVE APPROACHES; FINANCIAL INDICATOR; LOGIT-REGRESSION; MANUFACTURING COMPANIES;

ADAPTIVE BOOSTING; INDUSTRY; NEURAL NETWORKS;

FORECASTING;

EID: 84881415129     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.07.032     Document Type: Article

References (30)

1. E.I. Altman Financial ratios, discriminant analysis and the prediction of corporate bankruptcy Journal of Finance 23 1968 589 609
2. M.A. Boyacioglu, Y. Kara, and O.K. Baykan Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey Expert Systems with Applications 36 2009 3355 3366
3. A. Brezigar-Masten, and I. Masten CART-based selection of bankruptcy predictors for the logit model Expert Systems with Applications 39 2012 10153 10159
4. Chawla, N. V.; Japkowicz, N.; & Kolcz, A. (2004). Editorial: Special issue on learning from imbalanced data sets. In Sigkdd explorations (Vol. 6, no. 1, p. 1).
5. M.-Y. Chen Bankruptcy prediction in firms with statistical and intelligent techniques and a comparison of evolutionary computation approaches Computers & Mathematics with Applications 62 2011 4514 4524
6. S. Cho, H. Hong, and B.-C. Ha A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance: For bankruptcy prediction Expert Systems with Applications 37 2010 3482 3488
7. G.V. Davydova, and A.Y. Belikov The methodology of a firm's bankruptcy risk estimation Risk Management 3 1999 13 20 [in Russian: FOR VERIFICATION.FOR VERIFICATION. FOR VERIFICATION,aByery Cyrillic signDeh CyrillicoBa, FOR VERIFICATION.®. FOR VERIFICATION.e FOR VERIFICATION Ee Cyrillic signkoB. MetoDeh CyrillicEe Cyrillic signka ko FOR VERIFICATION Ee Cyrillic signLatin small letter turned HectBehhoEe Cyrillic sigň oTse Cyrillic signehkEe Cyrillic sign pEe Cyrillic signcka Cyrillic small letter BEahkpotctBa πpeDeh CyrillicπpEe Cyrillic signCyrillic small letter YA signtEe Cyrillic signEe Cyrillic sigň // YπpaB FOR VERIFICATION.ehEe Cyrillic signe pEe Cyrillic signckom. - 1999. - N 3. c.13-20]
8. E.A. Fedorova, E.V. Gilenko, and S.E. Dovzhenko Models for bankruptcy forecasting: Case study of Russian enterprises Studies on Russian Economic Development 24 2 2013 159 164 Available online http://link.springer.com/article/ 10.1134/S1075700713020044
9. Y. Freund, and R.E. Schapire A decision-theoretic generalization of on-line learning and an application to boosting Journal of Computer and System Sciences 55 1997 119 139
10. J.G. Fulmer Jr.; J.E. Moon, T.A. Gavin, and M.J. Erwin A bankruptcy classification model for small firms Journal of Commercial Bank Lending 7 1984 25 37
11. H. Ghodrati, and A.M. Moghaddam A study of the accuracy of bankruptcy prediction models: Altman, Shirata, Ohlson, Zmijewsky, CA score, Fulmer, Springate, Farajzadeh genetic, and Mckee genetic models for the companies of the stock exchange of tehran American Journal of Scientific Research 59 2012 55 67
12. Gu, J. (2007). Random forest based imbalanced data cleaning and classification. In PAKDD (pp. 1-7).
13. Ho, T. K.; Hull, J. J.; & Srihari, S. N. (1992). A regression approach to combination of decisions by multiple character recognition algorithms. In SPIE proc.: Vol. 1661. Machine vision applications in character recognition and industrial inspection, Feb.; 10-12. San Jose, CA.
14. M.Y. Kiang A comparative assessment of classification methods Decision Support Systems 35 2003 441 454
15. Lee, Y. -S.; & Yen, S. -J. (2006). Cluster-based sampling approaches to imbalanced data distributions. Proc. of eighth international conference on data warehousing and knowledge discovery (DaWaK'2006): Vol. 4081. Lecture notes in computer science (LNCS), September (pp. 427-436).
16. K. McCarthy, B. Zabar, and G. Weiss Does cost-sensitive learning beat sampling for classifying rare classes? Proc. of the first international workshop on utility-based data mining 2005 ACM Press NY, USA 69 77
17. J.H. Min, and C. Jeong A binary classification method for bankruptcy prediction Expert Systems with Applications 36 2009 5256 5263
18. Minavev, E. S.; & Panagushin, V. P. (1998). Crisis management. textbook. Prior, Moscow. [in Russian: MEe Cyrillic signhaBeB E.C.; ΠahaCyrillic small letter GHEyshah Cyrillic signEe Cyrillic signh Gayauyiby B.G. Aynbrpbpbcyoe ygpadkeybe. Exe,yoe goco,be lkz ne[ybxecrb[ dypod- M.: ΠpEe Cyrillic signop, 1998. - 432 c].
19. R. Polikar Ensemble based systems in decision making IEEE Circuits and Systems Magazine Third Quarter 2006 21 45
20. Springate, G. L. V. (1978). Predicting the possibility of failure in a canadian firm. MBA research project, Simon Fraser University, [Unpublished].
21. R.J. Taffler The assessment of company solvency and performance using a statistical model Accounting and Business Research 15 52 1983 295 308
22. F.-M. Tseng, and Y.-C. Hu Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks Expert Systems with Applications 37 2010 1846 1853
23. Visa, S.; & Ralescu, A. (2005). Issues in mining imbalanced data sets. In Proc. of the sixteen midwest artificial intelligence and cognitive science conference.
24. Zaytseva, O. P. (1998). Crisis management in Russian firms. Aval', Siberian financial school, No. 11-12 [in Russian: Cyrillic capital letter EaEe Cyrillic sigňTse Cyrillic signeBa O. Π. Paqweda O. G. Aynbrpbpbcysq veyel;veyn d poccbqcroq abpve // Adakm, Cb,bpcraz abyaycodaz iroka , No 11-12, 1998].
25. M.E. Zmijewski Methodological issues related to the estimation of financial distress prediction models Journal of Accounting Research 24 Supplement 1984 59 82
26. Interfax SPARK - Professional market and company analysis system (http://www.spark-interfax.ru/Front/Index.aspx, last available online on the 1st of June, 2013).
27. 118-MinEcon - the Order of the Ministry of Economy of Russian Federation of the 1st of October, 1997, No. 118 "Methodological Recommendations Concerning the Reform of Enterprises (Organizations)". The methodological recommendations were developed to guide the implementation of various steps to improve an enterprise's management, to increase the effectiveness of production, the competitiveness of the goods produced and the productivity of labor, to minimize the costs of production and to improve financial state of the enterprise. The Order is not available online in English.
28. 127-FZ - Russian Federal Law No. 127-FZ of October 26, 2002 "On Insolvency (Bankruptcy)". The new Federal law contains several substantial amendments in the field of legislative regulation of bankruptcy procedures. Available online from the GARANT system (http://english.garant.ru/mon/archive/ 2002/10/30/, last available online on the 1st of June 2013).
29. 209-FZ - Russian Federal Law No. 209-FZ of July 24, 2007 "On the Development of Small and Medium-Size Businesses in the Russian Federation". The law creates legal fundamentals for development of small and medium-size businesses in the Russian Federation. Available online from the GARANT system (http://english.garant.ru/mon/archive/2007/07/27/, last available online on the 1st of June 2013).
30. 367-GovRF - the Decision of the Government of Russian Federation No. 367 of June 25, 2003 "On the Endorsement of the Rules of Financial Analysis Carried out by the Bankruptcy Commissioner". The Decision defines the principles and terms of financial analysis carried out by the bankruptcy commissioner, as well as the composition of information used in it by the bankruptcy commissioner. Available online from GARANT system (http://english.garant.ru/mon/archive/2003/06/27/, last available online on the 1st of June 2013).