IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v46y2015i1p83-102.html
   My bibliography  Save this article

A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime

Author

Listed:
  • M. Naresh Kumar
  • V. Sree Hari Rao

Abstract

Credit risk estimation and bankruptcy prediction methods have utilized Altman’s Z-score method for the last several years. It is reported in many studies that Z-score is sensitive to changes in accounting figures. Researchers have proposed different variations to conventional Z-score that can improve the prediction accuracy. In this paper, we develop a new multivariate nonlinear model for computing the Z-score. In addition, we develop a new credit risk index by fitting a Pearson type 3 distribution to the transformed financial ratios. The results of our study have shown that the new Z-score can predict the bankruptcy with an accuracy of 98.6 % as compared to 93.5 % by Altman’s Z-score. Also, the discriminate analysis revealed that the new transformed financial ratios could predict the bankruptcy probability with an accuracy of 93.0 % as compared to 87.4 % using the weights of Altman’s Z-score. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • 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.
    • Handle: RePEc:kap:compec:v:46:y:2015:i:1:p:83-102
    DOI: 10.1007/s10614-014-9452-9
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10614-014-9452-9
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10614-014-9452-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Degen, Matthias & Embrechts, Paul & Lambrigger, Dominik D., 2007. "The Quantitative Modeling of Operational Risk: Between G-and-H and EVT," ASTIN Bulletin, Cambridge University Press, vol. 37(2), pages 265-291, November.
    2. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
    3. Kexue Liu & Jean Salvati & Mr. Renzo G Avesani & Mr. Alin T Mirestean, 2006. "Review and Implementation of Credit Risk Models of the Financial Sector Assessment Program (FSAP)," IMF Working Papers 2006/134, International Monetary Fund.
    4. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
    5. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    6. David Ashton & Paul Dunmore & Mark Tippett, 2004. "Double Entry Bookkeeping and the Distributional Properties of a Firm's Financial Ratios," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(5‐6), pages 583-606, June.
    7. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    8. David Ashton & Paul Dunmore & Mark Tippett, 2004. "Double Entry Bookkeeping and the Distributional Properties of a Firm's Financial Ratios," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(5-6), pages 583-606.
    9. Taha Zaghdoudi, 2013. "Bank Failure Prediction with Logistic Regression," International Journal of Economics and Financial Issues, Econjournals, vol. 3(2), pages 537-543.
    10. Hideya Kubo & Yasuhiro Sakai, 2011. "On long-term credit risk assessment and rating: towards a new set of models," Journal of Risk Research, Taylor & Francis Journals, vol. 14(9), pages 1127-1141, October.
    11. Manfred Gilli & Evis këllezi, 2006. "An Application of Extreme Value Theory for Measuring Financial Risk," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 207-228, May.
    12. Stuart McLeay & Duarte Trigueiros, 2002. "Proportionate Growth and the Theoretical Foundations of Financial Ratios," Abacus, Accounting Foundation, University of Sydney, vol. 38(3), pages 297-316, October.
    13. F. Pizzutilo, 2012. "The behaviour of the distributions of stock returns: an analysis of the European market using the Pearson system of continuous probability distributions," Applied Financial Economics, Taylor & Francis Journals, vol. 22(20), pages 1743-1752, October.
    14. George J. Benston & Michael Bromwich & Alfred Wagenhofer, 2006. "Principles‐ versus rules‐based accounting standards: the FASB's standard setting strategy," Abacus, Accounting Foundation, University of Sydney, vol. 42(2), pages 165-188, June.
    15. 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.
    16. Concepción Bartual & Fernando García & Vicente Giménez & Agustín Romero-Civera, 2012. "Credit Risk Analysis: Reflections on the Use of the Logit Model," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 2(6), pages 1-1.
    17. Bertrand Maillet & Thierry Michel, 2003. "An index of market shocks based on multiscale analysis," Quantitative Finance, Taylor & Francis Journals, vol. 3(2), pages 88-97.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bhanu Pratap Singh & Alok Kumar Mishra, 2016. "Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-28, December.
    2. Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017. "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 67-80.
    3. Bhanu Pratap SINGH & Alok Kumar MISHRA, 2019. "Sensitivity of bankruptcy prediction models to the change in econometric methods," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(620), A), pages 71-86, Autumn.
    4. Eduardo Acosta-González & Fernando Fernández-Rodríguez & Hicham Ganga, 2019. "Predicting Corporate Financial Failure Using Macroeconomic Variables and Accounting Data," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 227-257, January.

    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. M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Papers 1502.00882, arXiv.org.
    2. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    3. Tang, Lingxiao & Cai, Fei & Ouyang, Yao, 2019. "Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 563-572.
    4. Mark Clintworth & Dimitrios Lyridis & Evangelos Boulougouris, 2023. "Financial risk assessment in shipping: a holistic machine learning based methodology," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 90-121, March.
    5. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    6. Farida Titik Kristanti, 2017. "Corporate Governance, Financial Ratios, Political Risk and Financial Distress, A Survival Analysis," GATR Journals afr130, Global Academy of Training and Research (GATR) Enterprise.
    7. Duc Hong Vo & Binh Ninh Vo Pham & Chi Minh Ho & Michael McAleer, 2019. "Corporate Financial Distress of Industry Level Listings in Vietnam," JRFM, MDPI, vol. 12(4), pages 1-17, September.
    8. Lee, Bong Soo & Li, Ming-Yuan Leon, 2012. "Diversification and risk-adjusted performance: A quantile regression approach," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2157-2173.
    9. John Nkwoma Inekwe, 2016. "Financial Distress, Employees’ Welfare and Entrepreneurship Among SMEs," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 129(3), pages 1135-1153, December.
    10. Inekwe, John Nkwoma & Jin, Yi & Valenzuela, Ma. Rebecca, 2018. "The effects of financial distress: Evidence from US GDP growth," Economic Modelling, Elsevier, vol. 72(C), pages 8-21.
    11. Vo, D.H. & Pham, B.V.-N. & Pham, T.V.-T. & McAleer, M.J., 2019. "Corporate Financial Distress of Industry Level Listings in an Emerging Market," Econometric Institute Research Papers EI2019-15, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    12. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    13. Sunghwa Park & Hyunsok Kim & Janghan Kwon & Taeil Kim, 2021. "Empirics of Korean Shipping Companies’ Default Predictions," Risks, MDPI, vol. 9(9), pages 1-17, September.
    14. Douglas, Ella & Lont, David & Scott, Tom, 2014. "Finance company failure in New Zealand during 2006–2009: Predictable failures?," Journal of Contemporary Accounting and Economics, Elsevier, vol. 10(3), pages 277-295.
    15. Waldemar Tarczynski & Malgorzata Tarczynska-Luniewska & Kinga Flaga-Gieruszynska, 2020. "The Problem of Bankruptcy in Listed Companies," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 3-15.
    16. Evangelos C. Charalambakis & Ian Garrett, 2019. "On corporate financial distress prediction: What can we learn from private firms in a developing economy? Evidence from Greece," Review of Quantitative Finance and Accounting, Springer, vol. 52(2), pages 467-491, February.
    17. Ha Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," Working Papers hal-04133309, HAL.
    18. Mahtani, Umesh S. & Garg, Chandra Prakash, 2018. "An analysis of key factors of financial distress in airline companies in India using fuzzy AHP framework," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 87-102.
    19. Pham Vo Ninh, Binh & Do Thanh, Trung & Vo Hong, Duc, 2018. "Financial distress and bankruptcy prediction: An appropriate model for listed firms in Vietnam," Economic Systems, Elsevier, vol. 42(4), pages 616-624.
    20. 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).

    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:kap:compec:v:46:y:2015:i:1:p:83-102. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.