The back side of banking in Russia: forecasting bank failures with negative capital

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 In addition, we make available previously unpublished banking data for Russia.

Increase of banks’ credit risks forecasting power by the usage of the set of alternative models

“…All recent studies advise to pay great attention to the presence of the class imbalance problem in data on defaults and its impact on the estimation procedure and on some standard forecasting power indicators (Esarey and Pierce, 2012;Karminsky and Kostrov, 2017;Lanine and Vennet, 2006). Few events of default are usually available to estimate the model properly in the training set.…”

“…As for the modeling methods of this research, binary logit/probit regressions were chosen for PD estimation and multinomial ordered logit/probit for credit ratings modeling. It was shown (Jiao et al, 2007;Karminsky and Kostrov, 2017;Zan et al, 2004) that the predictions of more complex modeling methods like artificial intelligence models do not outperform the standard binary and ordered multinomial models. These methods were described and applied in the paper of , and in Karminsky and Kostrov (2017).…”

“…The occurrence of the default event is rare, so a PD model becomes overfitted towards non-default events. Even the classical balancing data methods provided by He and Garcia (2009) and Garcia et al (2012) do not fully solve this problem and a PD model gives underestimated results (Karminsky and Kostrov, 2017). On the other hand, rating models are not fully reliable either.…”

Negative Net Worth of Manufacturing Companies: Corporate Governance and Industry Expectations

“…При таком подходе появляется возможность заранее оценивать начальную точку возникновения проблем компаний - дыру в капитале, а не конечную - решение арбитражного суда о признании должника банкротом. В частности, события в банковском секторе - активный отзыв лицензий предоставляют хороший плацдарм для исследований вероятности и размера дыр в капитале отечественных банков (Мамонов, 2017;Karminsky, Kostrov, 2017).…”

The Formation of Hidden Negative Capital in Banking: A Product Mismatch Hypothesis

“…Indeed, one can find numerous studies on the probability of bank failures dealing with U.S. banks(Cole and White, 2012;DeYoung and Torna, 2013;Cleary and Hebb, 2016; Audrino et al, forthcoming), to name the most recent ones, banks in developing and emerging economies(Karminsky and Kostrov, 2017;Brown and Dinç, 2011;Arena, 2008;Mannasoo and Mayes, 2010; Fungacova and Weill, 2013, among others), and even EU banks(Poghosyan and Cihak, 2011;Betz et al, 2014). Conversely, the literature on HNC is biased towards the United States and appears during and just after the crisis periods in the late 1980s to the early 1990s(Bovenzi and Murton, 1988;James, 1991;Osterberg and Thomson, 1995) and after the Great Recession(Shaeck, 2008;Bennett and Unal, 2014;Kang et al, 2015;Balla et al, 2015;Cole and White, 2017;Granja et al, 2017).…”