Prediction of Banks Distress – Regional Differences and Macroeconomic Conditions

Małgorzata Iwanicz-Drozdowska; Aneta Ptak-Chmielewska

doi:10.18778/0208-6018.345.03

Authors

Małgorzata Iwanicz-Drozdowska Warsaw School of Economics, Collegium of Management and Finance, Institute of Finance Financial System Department https://orcid.org/0000-0002-8490-5178
Aneta Ptak-Chmielewska Warsaw School of Economics, Collegium of Economic Analysis, Institute of Statistics and Demography Applied Statistics Unit https://orcid.org/0000-0002-9896-4240

DOI:

https://doi.org/10.18778/0208-6018.345.03

Keywords:

banks distress, CAMEL, logistic prediction, macroeconomic variables

Abstract

In this study we focus on distress events of European banks over the period of 1990–2015, using unbalanced panel of 3,691 banks. We identify 132 distress events, which include actual bankruptcies as well as bailout cases. We apply CAMEL‑like bank‑level variables and control macroeconomic variables (GDP, inflation, unemployment rate). The analysis is based on traditional logistic regression and k‑means clustering. We find, that the probability of distress is connected with macroeconomic conditions via regional grouping (clustering). Bank‑level variables that were stable predictors of distress from 1 to 4 years prior to event are equity to total assets ratio (leverage) and loans to funding (liquidity).

From macroeconomic factors, the GDP growth is a reasonable variable, however with differentiated impact: for 1 year distance high distress probability is connected with low GDP growth, but for 2, 3 and 4 year distance: high distress probability is conversely connected with high GDP growth.

This shows the changing role of macroeconomic environment and indicates the potential impact of favorable macroeconomic conditions on building‑up systemic problems in the banking sector.

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