Paths of Glory or Paths of Shame? : an Analysis of Distress Events in European Banking

• Autorzy: Małgorzata Iwanicz-Drozdowska , Erkki K. Laitinen , Arto Suvas
• Języki publikacji: EN

Abstrakty

EN

This paper sheds some new light on banks' distress in Europe, with special attention paid to the period of the global financial crisis (GFC). Unlike in previous research we investigate non-distress ("glory") and distress ("shame") paths of banks from 1 to 4 years prior to a distress event to test how different they are. This approach allows us to outline guidelines for supervisors on how to detect banks generating higher risk of distress several years before its occurrence. We use a balanced panel of data, applying factor and cluster analysis for extraction of distress processes and a logistic regression for distress prediction. We conclude that the differences between distressed and non-distressed banks become more visible 1 and 2 years prior to the distress event. However, liquid assets and loans to assets ratios are significant and stable predictors of banks' distress even 3-4 years in advance. (original abstract)

Słowa kluczowe

EN

Banking risk; Literature review; Research results

PL

Ryzyko bankowe; Przegląd literatury; Wyniki badań

• Czasopismo: Bank i Kredyt
• Rocznik: 2018
• Numer: nr 2
• Strony: 115--143

Twórcy

autor

Małgorzata Iwanicz-Drozdowska

• Warsaw School of Economics, Poland

autor

Erkki K. Laitinen

• University of Vaasa

autor

Arto Suvas

• University of Vaasa

Bibliografia

• Alam P., Booth D., Lee K., Thordarson T. (2000), The use of fuzzy clustering algorithm and self- organizing neural networks for identifying potentially failing banks: an experimental study, Expert Systems with Applications, 18(3), 185-199.
• Altman E.I. (1968), Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance, 23, 589-609.
• 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.
• 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, 32(2), 299-310.
• Argenti J. (1976), Corporate Collapse: the Causes and Symptoms, McGraw-Hill.
• Beaver W.H. (1966), Financial ratios as predictors of failure. Empirical research in accounting. Selected studies, Journal of Accounting Research, 4, 71-111.
• Berg S., Hexenberg B. (1994), Early warning indicators for Norwegian banks: a logit analysis of the experiences from the banking crisis, Norges Bank Working Papers, 1.
• Betz F., Oprica S., Peltonen T., Sarlin P. (2014), Predicting distress in European banks, Journal of Banking and Finance, 45, 225-241.
• Boyacioglu M.A., Kara Y., Baykan O. (2009), Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: a comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey, Expert Systems with Applications, 36(2), 3355-3366.
• Canbas S., Cabuk A., Kilic S.B. (2005), Prediction of commercial bank failure via multivariate statistical analysis of financial structures: the Turkish case, European Journal of Operational Research, 166(2), 528-546.
• Chauhan N., Ravi V., Karthik Chandra D. (2009), Differential evolution trained wavelet neural networks: application to bankruptcy prediction in banks, Expert Systems with Applications, 36(4), 7659 -7665.
• Clare A., Priestley R. (2002), Calculating the probability of failure of the Norwegian banking sector, Journal of Multinational Financial Management, 12(1), 21-40.
• Cole R.A., Gunther J.W. (1995), Separating the likelihood and timing of bank failure, Journal of Banking and Finance, 19(6), 1073-1089.
• 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, 42(1), 5-29.
• Cooper M.C., Milligan G.W. (1988), The effect of error on determining the number of clusters. Proceedings of the international workshop on data analysis, Decision Support and Expert Knowledge Representation in Marketing and Related Areas of Research, 319 -328.
• Covas F.B., Rump B., Zakrajšek E. (2014), Stress-testing US bank holding companies: a dynamic panel quantile regression approach, International Journal of Forecasting, 30(3), 691-713.
• Cox R.A.K., Wang G.W. (2014), Predicting the US bank failure: a discriminant analysis, Economic Analysis and Policy, 44(2), 202-211.
• D'Aveni R. (1989), The aftermath of organizational decline: a longitudinal study of the strategic and managerial characteristics of declining firms, Academy of Management Journal, 32(3), 577-605.
• Du Jardin P. (2015), Bankruptcy prediction using terminal failure processes, European Journal of Operational Research, 242(1), 286- 303.
• Everitt B.S., Landau S., Leese M., Stahl D. (2009), Cluster Analysis, John Wiley & Sons.
• Fabrigar L.R., Wegener D.T., MacCallum R.C., Strahan E.J. (1999), Evaluating the use of exploratory factor analysis in psychological research, Psychological Methods, 4(3), 272-299.
• Hosmer D.W., Lemeshow S. (1989), Applied logistic regression, Willey.
• Hosmer D.W. jr., Lemeshow S., Sturdivant R.X. (2013), Applied Logistic Regression, John Wiley & Sons.
• Iwanicz-Drozdowska M., Smaga P., Witkowski B. (2016), Bank restructuring in the EU. Which way to go?, Journal of Policy Modeling , 38(3), 572-586.
• Kaiser H.F. (1960), The application of electronic computers to factor analysis, Educational and Psychological Measurement , 20, 141-151.
• Kapinos P., Mitnik O. (2016), A top-down approach to stress-testing banks, Journal of Financial Services Research , 49(2), 229-264.
• Kolari J., Glennon D., Shin H., Caputo M. (2002), Predicting large US commercial bank failures, Journal of Economics and Business , 54(4), 361-387.
• Laitinen E. (1991), Financial ratios and different failure processes, Journal of Business Finance and Accounting , 18(5), 649-673.
• Lanine G., Vander Vennet R. (2006), Failure predictions in the Russian bank sector with logit and trait recognition models, Expert Systems with Applications , 30, 463-478.
• Lopez J.A. (1999), Using CAMELS ratings to monitor bank conditions , Federal Reserve Bank of San Francisco Economic Letter, 19.
• 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 , 42(6), 2857-2868.
• Maghyereh A.I., Awartani B. (2014), Research in international business and finance bank distress prediction: empirical evidence from the Gulf Cooperation Council countries, Research in International Business and Finance , 30, 126-147.
• Mannasoo K., Mayes D. (2005), Investigating the early signals of banking sector vulnerabilities in Central and East European emerging markets , Working Papers of Eesti Pank, 2005-08.
• Mannasoo K., Mayes D. (2009), Explaining bank distress in Eastern European transition economies, Journal of Banking and Finance , 33(2), 244-253.
• Martin D. (1977), Early warning of bank failure, Journal of Banking and Finance, 1(3), 249-276.
• Mayes D., Stremmel H. (2014), The effectiveness of capital adequacy measures in predicting bank distress, SUERF Study, 2014/1.
• Mellahi K., Wilkinson A. (2004), Organizational failure: a critique of recent research and a proposed integrative framework, International Journal of Management Reviews, 5(1), 21- 41.
• Meyer P.A., Pifer H.W. (1970), Prediction of bank failures, The Journal of Finance , 25, 853-868.
• Milligan G.W., Cooper M.C. (1985), An examination of procedures for determining the number of clusters in a data set, Psychometrika, 50, 159 -179.
• Molina C.A. (2002), Predicting bank failures using a hazard model: the Venezuelan banking crisis, Emerging Markets Review , 3(1), 31-50.
• Mulaik S.A. (2009), Foundations of Factor Analysis , Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences.
• Ooghe H., de Prijcker S. (2008), Failure processes and causes of company bankruptcy: a typology, Management Decision , 46(2), 223-242.
• 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, 49-58.
• Pettway R., Sinkey J.F. jr (1980), Establishing on-site bank examination priorities: an early warning system using accounting and market information, Journal of Finance , 35(1), 137-150.
• Poghosyan T., Čihak M. (2011), Determinants of bank distress in Europe: evidence from a new data set, Journal of Financial Services Research , 40(3), 163-184.
• Richardson B., Nwankwo S., Richardson S. (1994), Understanding the causes of business failure crises. Generic failure types: boiled frogs, drowned frogs, bullfrogs and tadpoles, Management Decision , 32(4), 9-22.
• Santomero A., Vinso J. (1977), Estimating the probability of failure for commercial banks and the banking system, Journal of Banking and Finance , 1(2), 185-205.
• Shaffer S. (2012), Bank failure risk: different now? Economics Letters , 116(3), 613-616.
• Sinkey J.F. jr (1975), A multivariate statistical analysis of the characteristics of problem banks, Journal of Finance , 30(1), 21-36.
• Sinkey J.F. jr (1978), Identifying "problem" banks , Journal of Money, Credit and Banking , 10(2), 184 -193.
• Trahms C.A., Ndofor H.A., Sirmon D.G. (2013), Organizational decline and turnaround: a review and agenda for future research, Journal of Management , 39(5), 1277-1307.
• 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 , 82(1), 127-138.
• Wong J., Wong T., Leung P. (2010), Predicting banking distress in the EMEAP economies, Journal of Financial Stability , 6(3), 169-17.
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