Failure prediction in the Russian bank sector with logit and trait recognition models

G. Lanine () and Rudi Vander Vennet

Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration

Abstract: The Russian banking sector experienced considerable turmoil in the late 1990s, especially around the Russian banking crisis in 1998. The question is what types of banks are vulnerable to shocks and whether or not bank-specific characteristics can be used to predict vulnerability to failures. In this study we employ a parametric logit model and a nonparametric trait recognition approach to predict failures among Russian commercial banks. We test the predictive power of both models based on their prediction accuracy using holdout samples. Both models performed better than the benchmark; the trait recognition approach outperformed logit in both the original and the holdout samples. As expected liquidity plays an important role in bank failure prediction, but also asset quality and capital adequacy turn out to be important determinants of failure.

Keywords: Russian banks; bank failure prediction; logit model; trait recognition; forecasting accuracy (search for similar items in EconPapers)
JEL-codes: C25 G21 G33 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2005-08
New Economics Papers: this item is included in nep-cwa, nep-dcm, nep-fin, nep-for and nep-tra
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:rug:rugwps:05/329

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