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DRIVERS OF BANKS LICENSE WITHDRAWAL: THE AFTER CRISIS (2010–2011) STUDY
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DRIVERS OF BANKS LICENSE WITHDRAWAL: THE AFTER CRISIS (2010–2011) STUDY
Annotation
PII
S042473880000616-6-1
Publication type
Article
Status
Published
Authors
Andrey Emelyanov  Olga Bryukhova
Edition
Volume 51 Issue 3
Pages
41-53
Abstract

The article presents the reasons for withdrawal of licenses from the Russian commercial banks in the post-crisis period from 01 January 2010 to 31 December 2011. Logistic regression is used to predict the fi nancial stability of the banks. The model is built on the basis of monthly balance sheet statements, taken fi ve months before observing the status of the bank. The impact of unbalanced sample on the forecasting accuracy of the model is also discussed.

Keywords
bank, failure prediction, logit-model, fi nancial ratios
Date of publication
01.07.2015
Number of purchasers
1
Views
794
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0.0 (0 votes)
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Golovan' S.A., Evdokimov A.M., Karminskij A.M., Peresetskij A.A. (2004). Modeli veroyatnosti defolta rossijskikh bankov. Vliyanie makroehkonomicheskikh faktorov na ustojchivost' bankov. M.: REhSh.


Golovan' S.A., Karminskij A.M., Kopylov A.V., Peresetskij A.A. (2003). Modeli veroyatnosti defolta rossijskikh bankov. Predvaritel'noe razbienie bankov na klastery. M.: REhSh.


Drobyshevskij S.M., Zubarev A.V. (2011). Faktory ustojchivosti rossijskikh bankov 2007–2009. M.: Institut im. E.T. Gajdara.


Emel'yanov A.M., Bryukhova O.O. (2013). Otsenka veroyatnosti bankrotstva bankov //Finansy i kredit. № 27 (555). S. 47–58.


Karminskij A.M., Kostrov A.V. (2013). Modeli veroyatnosti defolta rossijskikh bankov: rasshirennye vozmozhnosti // Zhurnal Novoj ehkonomicheskoj assotsiatsii. № 1. S. 64–86.


Karminskij A.M., Kostrov A.V., Murzenkov T.N. (2012). Modelirovanie veroyatnosti defolta rossijskikh bankov s ispol'zovaniem ehkonometricheskikh metodov. M.: Izd. dom GU VShEh.


Peresetskij A.A. (2007). Metody otsenki veroyatnosti defolta banka // Ehkonomika i matematicheskie metody. T. 43. № 3. S. 37–62.


Peresetskij A.A. (2013). Modeli prichin otzyva litsenzij rossijskikh bankov. Vliyanie neuchtennykh faktorov // Prikladnaya ehkonometrika. T. 30. № 2. S.49–64.


Federal'nyj zakon ot 25 fevralya 1999 g. № 40-FZ (red. ot 2 iyulya 2013 g.) «O nesostoyatel'nosti (bankrotstve) kreditnykh organizatsij».


Altman E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy // The Journal of Finance. Vol. 4. P. 589–610.


Bell T.B. (1997). Neural Nets or the Logit Model? A Comparison of Each Model’s Ability to Predict Commercial Bank Failures // International Journal of Intelligent Systems in Accounting, Finance and Management. Vol. 6. P. 249–264.


Berg S.A., Hexeberg B. (1994). Early Warning Indicators for Norwegian Banks: A Logit Analysis of the Experiences from the Banking Crisis. Norges Bank Working Paper.


Bongini P., Claessens S., Ferri G. (2001). The Political Economy of Distress in East Asian Financial Institutions // Journal of Financial Services Research. Vol. 19. P. 5 –25.


Charnes A., Cooper W.W., Rhodes E. (1978). Measuring the Effi ciency of Decision-Making Units // European Journal of Operation Research. Vol. 6. P. 429–444.


Erdogan B.E. (2008). Bankruptcy Prediction of Turkish Commercial Banks Using Financial Ratios // Applied Mathematical Sciences. Vol. 60. P. 2973–2982.


Estrella A., Park S., Peristiani S. (2000). Capital Ratios as Predictors of Bank Failure // FRBNY Economic Policy Review. P. 33–52.


He H., Edwardo A. (2009). Learning from Imbalanced Data // IEEE Transactions on Knowledge and Data Engineering. Vol. 9. P. 1263–1284.


Kahn C., Papanikolaou N. (2013). What Problem Banks Reveal about Future Financial Distress: Evidence from the Late 2000s Financial Crisis. (April 30, 2013). Available at SSRN: http://ssrn.com/abstract=1913710


Kolari J., Caputo M., Wagner D. (1996) Trait Recognition: An Alternative Approach to Early Warning Systems in Commercial Banking // Journal of Business Finance and Accounting. Vol. 23. P. 1415–1434.


Kolari J., Glennon D., Shin H., Caputo M. (2002). Predicting Large US Commercial Bank Failures // Journal of Economics and Business. Vol. 54. P. 361–387.


Lanine G., Vennet R. (2006) Failure Prediction in the Russian Bank Sector with Logit and Trait Recognition Models // Expert Systems with Applications. Vol. 30. P. 463–478.


Logan A. (2000). The Early 1990s Small Banking Crisis: Leading Indicators // Bank of England Financial Stability Review. No. 9. P. 130 – 145.


Louzada F., Ferreira-Silva P.H., Diniz C.A.R. (2012). On the Impact of Disproportional Samples in Credit Scoring Models: An Application to a Brazilian Bank Data // Expert Systems with Application. Vol. 39. P. 8071–8078.


Martin D. (1977). Early Warning of Bank Failure: A Logit Regression Approach // Journal of Banking and Finance. Vol. 1. P. 249–276.


Paola B., Laeven L., Majnoni G. (2002). How Good is the Market at Assessing Bank Fragility? A Horse Race between Different Indicators // Journal of Banking and Finance. Vol. 26. P. 1011–1028.


Zhao H., Sinha A., Ge W. (2009). Effects of Feature Construction on Classifi cation Performance: An Empirical Study in Bank Failure Prediction // Expert Systems with Applications. Vol. 36. P. 2633–2644.

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