Prediction of Banks Distress – Regional Differences and Macroeconomic Conditions

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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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References

Altman E. I., Cizel J., Rijken H. A. (2014), Anatomy of bank distress: the information content of accounting fundamentals within and across countries, http://dx.doi.org/10.2139/ssrn.2504926
Google Scholar

Arena M. (2008), Bank failures and bank fundamentals: A comparative analysis of Latin America and East Asia during the nineties using bank‑level data, “Journal of Banking and Finance”, vol. 32(2), pp. 299–310.
Google Scholar

Betz F., Oprica S., Peltonen T., Sarlin P. (2014), Predicting distress in European banks, “Journal of Banking and Finance”, no. 45, pp. 225–241.
Google Scholar

Cole R. A., White L. J. (2012), Déjà Vu All Over Again: The Causes of U.S . Commercial Bank Failures This Time Around, “Journal of Financial Services Research”, vol. 42(1), pp. 5–29.
Google Scholar

Cox R. A.K., Wang G. W. (2014), Predicting the US bank failure : A discriminant analysis, “Economic Analysis and Policy”, vol. 44(2), pp. 202–211.
Google Scholar

Drehmann M., Juselius M. (2014), Evaluating early warning indicators of banking crises: Satisfying policy requirements, “International Journal of Forecasting”, vol. 30, issue 3, pp. 759–780.
Google Scholar

Hájek P., Olej V., Myšková R. (2015), Predicting Financial Distress of Banks Using Random Subspace Ensembles of Support Vector Machines, [in:] R. Silhavy, R. Senkerik, Z. Oplatkova, Z. Prokopova, P. Silhavy (eds.), Artificial Intelligence Perspectives and Applications. Advances in Intelligent Systems and Computing, vol. 347, Springer, Cham.
Google Scholar

Hambusch G., Shaffer S. (2016), Forecasting bank leverage: an alternative to regulatory early warning models, “Journal of Regulatory Economics”, vol. 50(1), pp. 38–69.
Google Scholar

Iwanicz‑Drozdowska (ed.) (2016), European Bank Restructuring During the Global Financial Crisis, Palgrave Macmillan, London.
Google Scholar

Iwanicz‑Drozdowska M., Laitinen E., Suvas A. (2018), Paths of glory or paths of shame? An analysis of distress events in European banking, “Bank i Kredyt”, vol. 49(2), pp. 115–144.
Google Scholar

Kapinos P., Mitnik O. A. (2016), A Top‑down Approach to Stress‑testing Banks, “Journal of Financial Services Research”, vol. 49(2), pp. 229–264.
Google Scholar

Kolari J., Glennon D., Shin H., Caputo M. (2002), Predicting large US commercial bank failures, “Journal of Economics and Business”, vol. 54(4), pp. 361–387.
Google Scholar

Lopez J. A. (1999), Using CAMELS ratings to monitor bank conditions, Federal Reserve Bank of San Francisco Economic Letter, no. 19.
Google Scholar

López Iturriaga F. J., Sanz I. P. (2015), Bankruptcy visualization and prediction using neural networks: a study of U. S. commercial banks, “Expert Systems with Applications”, no. 42(6), pp. 2857−2868.
Google Scholar

Maghyereh A. I., Awartani B (2014), Bank distress prediction: Empirical evidence from the Gulf Cooperation Council countries, “Research in International Business and Finance”, no. 30, pp. 126–147.
Google Scholar

Peek J., Rosengren E. (1996), The use of capital ratios to trigger intervention in problem banks: too little, too late, “New England Economic Review”, September/October, pp. 49–58.
Google Scholar

Peltonen T. A., Piloiu A., Sarlin P. (2015), Network linkages to predict bank distress, European Central Bank, Working Paper Series, no. 1828.
Google Scholar

Poghosyan T., Čihak M. (2011), Determinants of Bank Distress in Europe: Evidence from a New Data Set, “Journal of Financial Services Research”, vol. 40(3), pp. 163–184.
Google Scholar

Ravisankar P., Ravi V. (2010), Financial distress prediction in banks using Group Method of Data Handling neural network, counter propagation neural network and fuzzy ARTMAP, “Knowledge‑Based Systems”, vol. 23(8), pp. 823–831.
Google Scholar

Shaffer S. (2012), Bank failure risk: Different now?, “Economics Letters”, vol. 116(3), pp. 613–616.
Google Scholar

Sinkey J. F. Jr (1975), A multivariate statistical analysis of the characteristics of problem banks, “Journal of Finance”, vol. XXX(1), pp. 21–36.
Google Scholar

SirElkhatim M. A., Salim N. (2015), Prediction of Banks Financial Distress, “SUST Journal of Engineering and Computer Sciences”, vol. 16(1), pp. 40–55.
Google Scholar

Wheelock D. C., Wilson P. W. (2000), Why do banks disappear? The determinants of U. S. bank failures and acquisitions, “The Review of Economics and Statistics”, no. 82(1), pp. 127−138.
Google Scholar

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Published

2019-12-30

How to Cite

Iwanicz-Drozdowska, M., & Ptak-Chmielewska, A. (2019). Prediction of Banks Distress – Regional Differences and Macroeconomic Conditions. Acta Universitatis Lodziensis. Folia Oeconomica, 6(345), 73–57. https://doi.org/10.18778/0208-6018.345.03

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