IDEAS home Printed from https://ideas.repec.org/a/ers/ijebaa/viy2013i1p117-136.html
   My bibliography  Save this article

Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence

Author

Listed:
  • Pablo de Llano Monelos
  • Manuel Rodríguez López
  • Carlos Piñeiro Sánchez

Abstract

This paper provides empirical evidence on the prediction of non-financial companies’ failure. We develop several models to evaluate failure risk in companies from Galicia. We check the predictive ability of parametric models (multivariate discriminant, logit) compared with auditor’s report. Models are based on relevant financial variables and ratios, in financial logic and a in financial distress situations. We examine a random sample of companies in cross-sectional perspective, checking the predictive capacity at any given time, also verifying is models give reliable signals to anticipate future events of financial distress. Findings suggest that our models are extremely effective when applied in medium and long term, and that they offer higher predictive capabilities than external audit.

Suggested Citation

  • Pablo de Llano Monelos & Manuel Rodríguez López & Carlos Piñeiro Sánchez, 2013. "Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 117-136.
  • Handle: RePEc:ers:ijebaa:v:i:y:2013:i:1:p:117-136
    as

    Download full text from publisher

    File URL: http://www.ersj.eu/repec/ers/pijeba/13_1_p6.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. McKee, Thomas E. & Lensberg, Terje, 2002. "Genetic programming and rough sets: A hybrid approach to bankruptcy classification," European Journal of Operational Research, Elsevier, vol. 138(2), pages 436-451, April.
    2. R. Slowinski & C. Zopounidis, 1995. "Application of the Rough Set Approach to Evaluation of Bankruptcy Risk," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(1), pages 27-41, March.
    3. repec:bla:joares:v:32:y:1994:i::p:1-30 is not listed on IDEAS
    4. Peel, MJ & Peel, DA & Pope, PF, 1986. "Predicting corporate failure-- Some results for the UK corporate sector," Omega, Elsevier, vol. 14(1), pages 5-12.
    5. repec:bla:joares:v:4:y:1966:i::p:71-111 is not listed on IDEAS
    6. Lam, Kevin C.K. & Mensah, Yaw M., 2006. "Auditors' decision-making under going-concern uncertainties in low litigation-risk environments: Evidence from Hong Kong," Journal of Accounting and Public Policy, Elsevier, vol. 25(6), pages 706-739.
    7. 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.
    8. Thomas E. McKee, 2003. "Rough sets bankruptcy prediction models versus auditor signalling rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(8), pages 569-586.
    9. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    10. repec:bla:joares:v:18:y:1980:i:1:p:109-131 is not listed on IDEAS
    11. repec:bla:joares:v:22:y:1984:i::p:59-82 is not listed on IDEAS
    12. repec:bla:joares:v:22:y:1984:i:2:p:679-702 is not listed on IDEAS
    13. Pindado, Julio & Rodrigues, Luis & de la Torre, Chabela, 2008. "Estimating financial distress likelihood," Journal of Business Research, Elsevier, vol. 61(9), pages 995-1003, September.
    14. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. " Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    15. William F. Messier, Jr. & James V. Hansen, 1988. "Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data," Management Science, INFORMS, vol. 34(12), pages 1403-1415, December.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Business Failure; Financial Distress; Prediction of Insolvency; Audit Reports;

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other

    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:ers:ijebaa:v:i:y:2013:i:1:p:117-136. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marios Agiomavritis). General contact details of provider: http://www.ijeba.com/ .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.