Predictors of Banks’ Default

The goal of my research is to identify and to interpret the most significant factors which predict banks' default from 2013 till 2018. Therefore, my research paper investigates financial parameters of Russian commercial banks from 2013 till 2018. Noted, that the sample of data contains banks which became a default due to crash in financial parameters of banks. Banks' default also means reorganization. The data also contains banks which were and are reorganized. Revoking banks' licenses due to illegal activities is not the case of my research work. Assumptions, the model and empirical research are based on the studied literature. In my research paper I use the panel data from 2013 till 2018. The model has financial microeconomic parameters of each bank and macroeconomic parameters such as GDP growth rate and key interest rate. The new in my model is including the parameter of financial crises' times (times of sanctions imposition) as a dummy variable. Experts of Russian Economy claim that Russia has a financial crises in the forth quarter of 2014 and the first and second quarters of 2015. The model is binary choice model which estimates the probability of banks' default. I fitted three different logit models: the first one contains only financial and microeconomic variables, in the second model macroeconomic factors, such as GDP growth rate and key interest rate, was added, and the third one contains all micro- and macroeconomic variables and added dummy variable which represents times of financial crises in Russia. After fitting all three models I compared them through measures of fitting and select the best fitted model for further testing hypothesis and interpretation of results.