IDEAS home Printed from https://ideas.repec.org/a/fgv/eaerae/v55y2015i5a55836.html
   My bibliography  Save this article

Estudo cross-country sobre os fatores determinantes da crise financeira bancária

Author

Listed:
  • Liu, Zhen Jia

Abstract

Bank failures affect owners, employees, and customers, possibly causing large-scale economic distress. Thus, banks must evaluate operational risks and develop early warning systems. This study investigates bank failures in the Organization for Economic Co-operation and Development, the North America Free Trade Area (NAFTA), the Association of Southeast Asian Nations, the European Union, newly industrialized countries, the G20, and the G8. We use financial ratios to analyze and explore the appropriateness of prediction models. Results show that capital ratios, interest income compared to interest expenses, non-interest income compared to non-interest expenses, return on equity, and provisions for loan losses have significantly negative correlations with bank failure. However, loan ratios, non-performing loans, and fixed assets all have significantly positive correlations with bank failure. In addition, the accuracy of the logistic model for banks from NAFTA countries provides the best prediction accuracy regarding bank failure.

Suggested Citation

  • Liu, Zhen Jia, 2015. "Estudo cross-country sobre os fatores determinantes da crise financeira bancária," RAE - Revista de Administração de Empresas, FGV-EAESP Escola de Administração de Empresas de São Paulo (Brazil), vol. 55(5), September.
  • Handle: RePEc:fgv:eaerae:v:55:y:2015:i:5:a:55836
    as

    Download full text from publisher

    File URL: http://bibliotecadigital.fgv.br/ojs/index.php/rae/article/view/55836
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ehab Zaki & Rahim Bah & Ananth Rao, 2011. "Assessing probabilities of financial distress of banks in UAE," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 7(3), pages 304-320, June.
    2. Sinha, Pankaj & Taneja, Varundeep Singh & Gothi, Vineet, 2009. "Evaluation of riskiness of Indian Banks and probability of book value insolvency," MPRA Paper 15251, University Library of Munich, Germany.
    3. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto, 2011. "Partial Least Square Discriminant Analysis (PLS-DA) for bankruptcy prediction," Working Papers CEB 11-024, ULB -- Universite Libre de Bruxelles.
    4. Taha Zaghdoudi, 2013. "Bank Failure Prediction with Logistic Regression," International Journal of Economics and Financial Issues, Econjournals, vol. 3(2), pages 537-543.
    5. Karatzoglou, Alexandros & Meyer, David & Hornik, Kurt, 2006. "Support Vector Machines in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i09).
    6. Meyer, Paul A & Pifer, Howard W, 1970. "Prediction of Bank Failures," Journal of Finance, American Finance Association, vol. 25(4), pages 853-868, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
    2. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    3. Yi-Shu Wang & Xue Jiang & Zhen-Jia-Liu, 2016. "Bank Failure Prediction Models for the Developing and Developed Countries: Identifying the Economic Value Added for Predicting Failure," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(9), pages 522-533, September.
    4. Zied Ftiti & Aviral Tiwari & Amél Belanès & Khaled Guesmi, 2015. "Tests of Financial Market Contagion: Evolutionary Cospectral Analysis Versus Wavelet Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 46(4), pages 575-611, December.
    5. Caporale, Guglielmo Maria & Matousek, Roman & Stewart, Chris, 2012. "Ratings assignments: Lessons from international banks," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1593-1606.
    6. Saiful Anwar & A.M Hasan Ali, 2018. "ANNs-BASED EARLY WARNING SYSTEM FOR INDONESIAN ISLAMIC BANKS," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 20(3), pages 325-342, January.
    7. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    8. Mitroussi, K. & Abouarghoub, W. & Haider, J.J. & Pettit, S.J. & Tigka, N., 2016. "Performance drivers of shipping loans: An empirical investigation," International Journal of Production Economics, Elsevier, vol. 171(P3), pages 438-452.
    9. Paola Bongini & Stijn Claessens & Giovanni Ferri, 2001. "The Political Economy of Distress in East Asian Financial Institutions," Journal of Financial Services Research, Springer;Western Finance Association, vol. 19(1), pages 5-25, February.
    10. Allen N. Berger & Björn Imbierowicz & Christian Rauch, 2016. "The Roles of Corporate Governance in Bank Failures during the Recent Financial Crisis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 729-770, June.
    11. M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 83-102, June.
    12. Paolo Sorino & Maria Gabriella Caruso & Giovanni Misciagna & Caterina Bonfiglio & Angelo Campanella & Antonella Mirizzi & Isabella Franco & Antonella Bianco & Claudia Buongiorno & Rosalba Liuzzi & Ann, 2020. "Selecting the best machine learning algorithm to support the diagnosis of Non-Alcoholic Fatty Liver Disease: A meta learner study," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
    13. Benítez-Peña, Sandra & Blanquero, Rafael & Carrizosa, Emilio & Ramírez-Cobo, Pepa, 2024. "Cost-sensitive probabilistic predictions for support vector machines," European Journal of Operational Research, Elsevier, vol. 314(1), pages 268-279.
    14. Na Tang & Maoxiang Yuan & Zhijun Chen & Jian Ma & Rui Sun & Yide Yang & Quanyuan He & Xiaowei Guo & Shixiong Hu & Junhua Zhou, 2023. "Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants," IJERPH, MDPI, vol. 20(5), pages 1-17, February.
    15. Hale, Galina & Lopez, Jose A., 2019. "Monitoring banking system connectedness with big data," Journal of Econometrics, Elsevier, vol. 212(1), pages 203-220.
    16. Thomas B. King & Daniel A. Nuxoll & Timothy J. Yeager, 2006. "Are the causes of bank distress changing? can researchers keep up?," Review, Federal Reserve Bank of St. Louis, vol. 88(Jan), pages 57-80.
    17. Halil Erdal & Aykut Ekinci, 2013. "A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 199-215, August.
    18. Gerhard Hambusch & Sherrill Shaffer, 2016. "Forecasting bank leverage: an alternative to regulatory early warning models," Journal of Regulatory Economics, Springer, vol. 50(1), pages 38-69, August.
    19. Yuan, Kunpeng & Chi, Guotai & Zhou, Ying & Yin, Hailei, 2022. "A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description," Research in International Business and Finance, Elsevier, vol. 59(C).
    20. Fayçal Mraihi, 2016. "Distressed Company Prediction Using Logistic Regression: Tunisian’s Case," Quarterly Journal of Business Studies, Research Academy of Social Sciences, vol. 2(1), pages 34-54.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fgv:eaerae:v:55:y:2015:i:5:a:55836. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Núcleo de Computação da FGV EPGE (email available below). General contact details of provider: https://edirc.repec.org/data/eagvfbr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.