IDEAS home Printed from https://ideas.repec.org/a/spr/reaccs/v14y2009i4d10.1007_s11142-008-9080-5.html
   My bibliography  Save this article

Bankruptcy prediction: the case of Japanese listed companies

Author

Listed:
  • Ming Xu

    (The Hong Kong Polytechnic University)

  • Chu Zhang

    (The Hong Kong University of Science and Technology)

Abstract

This paper investigates if bankruptcy of Japanese listed companies can be predicted using data from 1992 to 2005. We find that the traditional measures, such as Altman’s (J Finance 23:589–609, 1968) Z-score, Ohlson’s (J Accounting Res 18:109–131, 1980) O-score and the option pricing theory-based distance-to-default, previously developed for the U.S. market, are also individually useful for the Japanese market. Moreover, the predictive power is substantially enhanced when these measures are combined. Based on the unique Japanese institutional features of main banks and business groups (known as Keiretsu), we construct a new measure that incorporates bank dependence and Keiretsu dependence. The new measure further improves the ability to predict bankruptcy of Japanese listed companies.

Suggested Citation

  • Ming Xu & Chu Zhang, 2009. "Bankruptcy prediction: the case of Japanese listed companies," Review of Accounting Studies, Springer, vol. 14(4), pages 534-558, December.
    • Handle: RePEc:spr:reaccs:v:14:y:2009:i:4:d:10.1007_s11142-008-9080-5
    DOI: 10.1007/s11142-008-9080-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11142-008-9080-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11142-008-9080-5?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    2. Arturo Bris & Ivo Welch & Ning Zhu, 2006. "The Costs of Bankruptcy: Chapter 7 Liquidation versus Chapter 11 Reorganization," Journal of Finance, American Finance Association, vol. 61(3), pages 1253-1303, June.
    3. Robert C. Merton, 2005. "Theory of rational option pricing," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 8, pages 229-288, World Scientific Publishing Co. Pte. Ltd..
    4. Stephen A. Hillegeist & Elizabeth K. Keating & Donald P. Cram & Kyle G. Lundstedt, 2004. "Assessing the Probability of Bankruptcy," Review of Accounting Studies, Springer, vol. 9(1), pages 5-34, March.
    5. William H. Beaver & Maureen F. McNichols & Jung-Wu Rhie, 2005. "Have Financial Statements Become Less Informative? Evidence from the Ability of Financial Ratios to Predict Bankruptcy," Review of Accounting Studies, Springer, vol. 10(1), pages 93-122, March.
    6. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    7. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    8. 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.
    9. Hoshi, Takeo & Kashyap, Anil & Scharfstein, David, 1990. "The role of banks in reducing the costs of financial distress in Japan," Journal of Financial Economics, Elsevier, vol. 27(1), pages 67-88, September.
    10. 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.
    11. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 18(1), pages 109-131.
    12. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 22, pages 59-82.
    13. Takeo Hoshi & Anil K. Kashyap & David Scharfstein, 1990. "The role of banks in reducing financial distress in Japan," Finance and Economics Discussion Series 134, Board of Governors of the Federal Reserve System (U.S.).
    14. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    15. repec:bla:jfinan:v:59:y:2004:i:2:p:831-868 is not listed on IDEAS
    16. Sadahiko Suzuki & Richard W Wright, 1985. "Financial Structure and Bankruptcy Risk in Japanese Companies," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 16(1), pages 97-110, March.
    17. Beaver, Wh, 1968. "Market Prices, Financial Ratios, And Prediction Of Failure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 6(2), pages 179-192.
    18. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    19. repec:bla:jfinan:v:53:y:1998:i:3:p:1131-1147 is not listed on IDEAS
    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. Juha-Pekka Kallunki & Elina Pyykkö, 2013. "Do defaulting CEOs and directors increase the likelihood of financial distress of the firm?," Review of Accounting Studies, Springer, vol. 18(1), pages 228-260, March.

    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. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    2. Viral Acharya & Sergei A. Davydenko & Ilya A. Strebulaev, 2012. "Cash Holdings and Credit Risk," The Review of Financial Studies, Society for Financial Studies, vol. 25(12), pages 3572-3609.
    3. Giesecke, Kay & Longstaff, Francis A. & Schaefer, Stephen & Strebulaev, Ilya, 2011. "Corporate bond default risk: A 150-year perspective," Journal of Financial Economics, Elsevier, vol. 102(2), pages 233-250.
    4. 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.
    5. Nguyen, Ha, 2023. "An empirical application of Particle Markov Chain Monte Carlo to frailty correlated default models," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 103-121.
    6. 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.
    7. 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.
    8. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    9. Alam, Nurul & Gao, Junbin & Jones, Stewart, 2021. "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    10. Duan, Jin-Chuan & Sun, Jie & Wang, Tao, 2012. "Multiperiod corporate default prediction—A forward intensity approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 191-209.
    11. Zhang, Xuan & Ouyang, Ruolan & Liu, Ding & Xu, Liao, 2020. "Determinants of corporate default risk in China: The role of financial constraints," Economic Modelling, Elsevier, vol. 92(C), pages 87-98.
    12. Kay Giesecke & Francis A. Longstaff & Stephen Schaefer & Ilya Strebulaev, 2010. "Corporate Bond Default Risk: A 150-Year Perspective," NBER Working Papers 15848, National Bureau of Economic Research, Inc.
    13. Nawaf Almaskati & Ron Bird & Yue Lu & Danny Leung, 2019. "The Role of Corporate Governance and Estimation Methods in Predicting Bankruptcy," Working Papers in Economics 19/16, University of Waikato.
    14. Tomasz Berent & Radosław Rejman, 2021. "Bankruptcy Prediction with a Doubly Stochastic Poisson Forward Intensity Model and Low-Quality Data," Risks, MDPI, vol. 9(12), pages 1-24, December.
    15. Cathcart, Lara & Dufour, Alfonso & Rossi, Ludovico & Varotto, Simone, 2020. "The differential impact of leverage on the default risk of small and large firms," Journal of Corporate Finance, Elsevier, vol. 60(C).
    16. Huang, Hsing-Hua & Lee, Han-Hsing, 2013. "Product market competition and credit risk," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 324-340.
    17. Charitou, Andreas & Dionysiou, Dionysia & Lambertides, Neophytos & Trigeorgis, Lenos, 2013. "Alternative bankruptcy prediction models using option-pricing theory," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2329-2341.
    18. Anand Deo & Sandeep Juneja, 2021. "Credit Risk: Simple Closed-Form Approximate Maximum Likelihood Estimator," Operations Research, INFORMS, vol. 69(2), pages 361-379, March.
    19. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    20. Koresh Galil & Neta Gilat, 2019. "Predicting Default More Accurately: To Proxy or Not to Proxy for Default?," International Review of Finance, International Review of Finance Ltd., vol. 19(4), pages 731-758, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:spr:reaccs:v:14:y:2009:i:4:d:10.1007_s11142-008-9080-5. 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.