IDEAS home Printed from https://ideas.repec.org/a/mir/mirbus/v5y2015i3p41-56.html
   My bibliography  Save this article

Predicting Corporate Financial Distress in Sri Lanka: An Extension to Z-Score Model

Author

Listed:
  • K. G. M. Nanayakkara

    (Department of Commerce & Financial Management, Faculty of Commerce & Management Studies, University of Kelaniya, Sri Lanka.)

  • A. A. Azeez

    (Department of Finance, Faculty of Management & Finance, University of Colombo, Sri Lanka.)

Abstract

The main purpose of this study is to develop a better financial distress prediction model for the Sri Lankan companies using the Z-score model. Fourteen variables have been selected consisting of accounting, cash flow and market based variables. Multivariate Discriminate Analysis (MDA) was used as the analytical technique and stepwise method was used to select the variables with the best discriminating power to a dataset of sixty-seven matched pairs of failed and non-failed quoted public companies over the period 2002 to 2011. The final models are validated using the cross validation method. The results indicate that a model with four predictors of earnings before interest and taxes, cash flow from operations to total debts, retained earnings to total assets, and firm size have achieved the classification accuracy of 85.8% in one year prior to the distress with a very low type I error. Moreover, the model has correctly classified the cases by 79.9% and 69.4% in two year and three year prior to distress respectively. The study has further revealed that the companies with negative cutoff value fall into distress zone while the companies with positive cutoff values fall into safety area. Hence, the study concluded that the companies with cutoff values approximately zero should be considered on mitigating actions for financial distress not only on the accounting information but also on the cash flow and market data.

Suggested Citation

  • K. G. M. Nanayakkara & A. A. Azeez, 2015. "Predicting Corporate Financial Distress in Sri Lanka: An Extension to Z-Score Model," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 5(3), pages 41-56, March.
  • Handle: RePEc:mir:mirbus:v:5:y:2015:i:3:p:41-56
    as

    Download full text from publisher

    File URL: http://thejournalofbusiness.org/index.php/site/article/view/733/508
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Wang, Zhenpo & Hong, Jichao & Liu, Peng & Zhang, Lei, 2017. "Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles," Applied Energy, Elsevier, vol. 196(C), pages 289-302.
    2. 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.

    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:mir:mirbus:v:5:y:2015:i:3:p:41-56. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: M Kabir (email available below). General contact details of provider: https://edirc.repec.org/data/csmirus.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.