IDEAS home Printed from https://ideas.repec.org/p/gtr/gatrjs/jfbr152.html
   My bibliography  Save this paper

Bankruptcy Prediction Model of Banks in Indonesia Based on Capital Adequacy Ratio

Author

Listed:
  • Lis Sintha

    (Universitas Kristen Indonesia, Jl. Mayjen Soetoyo no.2, Cawang, Jakarta (13630), Indonesia Author-2-Name: Author-2-Workplace-Name: Author-3-Name: Author-3-Workplace-Name: Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:)

Abstract

Objective � The purpose of this study is to examine the influence of capital on bankruptcy banks. The hypothesis of this research is that capital has an effect on the bankruptcy of a bank. Methodology/Technique � This research examines financial reports between 2005-2014. An econometric model with a logistical regression analysis technique is used. In this study, capital is measured by CAR, taking into account credit risk; CAR by taking into account market risk; Ratio of Obligation to Provide Minimum Capital for Credit Risk and Operational Risk; Ratio of Minimum Capital Adequacy Ratio for Credit Risk, Operational Risk and Market Risk; Capital Adequacy Requirements (CAR). Findings � The results show that the capital adequacy ratio for market ratio and capital adequacy ratio for credit ratio and operational ratio support the research hypothesis and can form a logit model. The test results of CAR by taking into account credit risk, Minimum Capital Requirement Ratio for Credit Risk, Operational Risk and Market Risk and Minimum Capital Provision Obligations do not support the research hypothesis. Novelty � This paper contribute to bank bankruptcy prediction models based on time dimension and bank groups using financial ratios which are expected can influence bank in bankrupt condition. Type of Paper: Empirical.

Suggested Citation

  • Lis Sintha, 2019. "Bankruptcy Prediction Model of Banks in Indonesia Based on Capital Adequacy Ratio," GATR Journals jfbr152, Global Academy of Training and Research (GATR) Enterprise.
    • Handle: RePEc:gtr:gatrjs:jfbr152
    as

    Download full text from publisher

    File URL: http://gatrenterprise.com/GATRJournals/pdf_files/JFBR%20Vol%204(1)/2.Lis%20Sintha.pdf
    Download Restriction: http://gatrenterprise.com/GATRJournals/online_submission.html
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    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. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    2. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    3. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    4. Christophe Godlewski, 2004. "Modélisation de la Prévision de Défaillance Bancaire Une Application aux Banques des Pays Emergents," Finance 0409026, University Library of Munich, Germany.
    5. Maurice Peat, 2007. "Factors Affecting the Probability of Bankruptcy: A Managerial Decision Based Approach," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 303-324, September.
    6. Shekar Shetty & Mohamed Musa & Xavier Brédart, 2022. "Bankruptcy Prediction Using Machine Learning Techniques," JRFM, MDPI, vol. 15(1), pages 1-10, January.
    7. Silvia Mariela Méndez-Prado & Ariel Flores Ulloa, 2022. "The Impact Analysis of Psychological Issues and Pandemic-Related Variables on Ecuadorian University Students during COVID-19," Sustainability, MDPI, vol. 14(20), pages 1-23, October.
    8. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    9. Quader, Syed Manzur, 2017. "Differential effect of liquidity constraints on firm growth," Review of Financial Economics, Elsevier, vol. 32(C), pages 20-29.
    10. 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.
    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. Catherine Refait, 2004. "La prévision de la faillite fondée sur l’analyse financière de l’entreprise : un état des lieux," Économie et Prévision, Programme National Persée, vol. 162(1), pages 129-147.
    13. Jeyhun A. Abbasov, 2017. "Financial ratios and the prediction of bankruptcy," Working Papers 1705, Central Bank of Azerbaijan Republic.
    14. Golaszewski, Richard & Sanders, Matthew, 1991. "Financial Stress in the U.S. Airline Industry," Transportation Research Forum Proceedings 1990s 319097, Transportation Research Forum.
    15. Malcolm Smith & Yun Ren & Yinan Dong, 2011. "The predictive ability of “conservatism” and “governance” variables in corporate financial disclosures," Asian Review of Accounting, Emerald Group Publishing Limited, vol. 19(2), pages 171-185, July.
    16. Roman Vavrek & Ivana Kravčáková Vozárová & Rastislav Kotulič, 2021. "Evaluating the Financial Health of Agricultural Enterprises in the Conditions of the Slovak Republic Using Bankruptcy Models," Agriculture, MDPI, vol. 11(3), pages 1-19, March.
    17. Chiuling Lu & Ann Yang & Jui-Feng Huang, 2015. "Bankruptcy predictions for U.S. air carrier operations: a study of financial data," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(3), pages 574-589, July.
    18. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    19. Suparatana Tanthanongsakkun & Sirimon Treepongkaruna, 2008. "Explaining Credit Ratings of Australian Companies—An Application of the Merton Model," Australian Journal of Management, Australian School of Business, vol. 33(2), pages 261-275, December.
    20. Harlan L. Etheridge & Kathy H. Y. Hsu, 2015. "Minimizing the Costs of Using Models to Assess the Financial Health of Banks," International Journal of Business and Social Research, LAR Center Press, vol. 5(11), pages 9-18, November.

    More about this item

    Keywords

    Banking crisis; Cost of bankruptcy; Adequacy Ratio; Financial ratios; Prediction models;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G39 - Financial Economics - - Corporate Finance and Governance - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:gtr:gatrjs:jfbr152. 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: Prof. Dr. Abd Rahim Mohamad (email available below). General contact details of provider: http://gatrenterprise.com .

    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.