Authors: Alexander Karminsky; Alexander Kostrov
Addresses: Department of Finance, Higher School of Economics, 26 Shabolovka str., 101000, Moscow, Russia ' Faculty of Mathematics and Statistics, University of St. Gallen, 6 Bodanstrasse, 9000, St. Gallen, Switzerland; Higher School of Economics, 26 Shabolovka str., 101000, Moscow, Russia
Abstract: Since 2013, we have observed an increasing number of failed Russian banks with negative capital and falsified financial reporting. We use previously unavailable data for the period 2010-1H2015 to develop a logit model predicting the probability of bank failure with negative capital. In order to do so, we suggest solutions for the class imbalance and variable selection problems. The models chosen are confirmed to be robust and have longer forecasting horizons compared to previous research. Also, we implement a novel probability-based approach to the out-of-sample forecasting evaluation which confirms a good fit of the selected models to data. The model predicts bank failures in three quarters and finds 33% of actual failures among 5% of banks with the highest predicted probability to fail (out-of-sample). In addition, we make available previously unpublished banking data for Russia.
Keywords: default probability; fraudulent financial reporting; logit model; Central Bank of Russia; financial mismanagement; bank creditors; fraud; banking industry; bank failure forecasting; bank failures; negative capital; failed banks; class imbalance; variable selection.
International Journal of Computational Economics and Econometrics, 2017 Vol.7 No.1/2, pp.170 - 209
Accepted: 11 Jul 2016
Published online: 12 Oct 2016 *