Prediction of Banks Distress – Regional Differences and Macroeconomic Conditions

• Authors: Małgorzata Iwanicz-Drozdowska , Aneta Ptak-Chmielewska

Title variants

Predykcja bankructwa banków – różnice regionalne i uwarunkowania makroekonomiczne

• Languages of publication: EN

Abstracts

EN

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.

PL

Artykuł poświęcony został bankructwom banków europejskich z lat 1990–2015. Wykorzystane zostały w nim niezbilansowane dane panelowe dla 3691 banków. Zidentyfikowano 132 bankructwa – zarówno faktyczne, jak i wynikające z konieczności subwencji. Wykorzystano zmienne na poziomie banków typu CAMEL i kontrolne zmienne makroekonomiczne (PKB, inflacja, stopa bezrobocia). Analiza oparta została na tradycyjnym modelu regresji logistycznej do predykcji bankructwa i metodzie k‑średnich do grupowania. Otrzymane wyniki wskazują, że prawdopodobieństwo bankructwa jest zależne od warunków makroekonomicznych poprzez wyniki klasteryzacji. Zmienne na poziomie banków, które są stabilnym predyktorem bankructwa od roku do czterech lat przed zdarzeniem, to: kapitał do aktywów ogółem (dźwignia) oraz kredyty do funduszy (płynność). Z czynników makroekonomicznych istotne znaczenie ma PKB, ale ze zróżnicowanym wpływem: dla roku przed bankructwem wysokie prawdopodobieństwo bankructwa jest związane z niską dynamika PKB, ale dla 2, 3 i 4 lat przed bankructwem wysokie ryzyko bankructwa jest związane z wysoką dynamika PKB, czyli jest to zależność odwrotna. Pokazuje to zmienną rolę otoczenia makroekonomicznego i wskazuje na potencjalny wpływ sprzyjających warunków makroekonomicznych na powstawanie problemu systemowego w sektorze bankowym.

Keywords

EN

banks distress; CAMEL; logistic prediction; macroeconomic variables

PL

bankructwo; CAMEL; predykcja logistyczna; zmienne makroekonomiczne; bankructwo; CAMEL; predykcja logistyczna; zmienne makroekonomiczne

• Publisher: Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
• Journal: Acta Universitatis Lodziensis. Folia Oeconomica
• Year: 2019
• Volume: 6
• Issue: 345
• Pages: 73-57

Dates

published

2019-12-30

Contributors

author

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

author

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

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Identifiers

DOI

10.18778/0208-6018.345.03