IDEAS home Printed from https://ideas.repec.org/a/wsi/ijfexx/v07y2020i03ns2424786320500310.html
   My bibliography  Save this article

A data mining approach to predict companies’ financial distress

Author

Listed:
  • Rasoul Tahmasebi

    (Department of Financial Management, UAE Branch, Islamic Azad University, Dubai, UAE)

  • Ali Asghar Anvary Rostamy

    (#x2020;Department of Planning and Management, Management Study and Technology Development Centre, Tarbiat Modares University, Tehran, Iran)

  • Abbas Khorshidi

    (#x2021;Department of Management, Eslamshahr Branch, Islamic Azad University, Tehran, Iran)

  • Seyyed Jalal Sadeghi Sharif

    (#xA7;Department of Management Shahid Beheshti University, Tehran, Iran)

Abstract

Financial distress and companies’ failure have always been a complicated and intriguing problem for businesses. Because of the unfavorable impacts of financial distress on companies and societies, accounting and finance researchers around the world are thinking of ways to anticipate corporate financial distress. Several models are provided in the literature for predicting financial distress. This research develops nonlinear decision tree and linear discriminant analysis models to predict financial distress of companies listed in Iranian Stock Exchange during 2010 to 2015. The drivers are firms’ financial ratios, intellectual capital and performance indicators. According to the results, intellectual capital and financial performance indices have no informational content in decision tree model. Comparing the result show that both models predict financial distress with 90.9% and 81.8% accuracy, respectively. Moreover, the difference between the accuracy of the models however is not meaningful. In other words, two models were very close to each other in terms of predictive power.

Suggested Citation

  • Rasoul Tahmasebi & Ali Asghar Anvary Rostamy & Abbas Khorshidi & Seyyed Jalal Sadeghi Sharif, 2020. "A data mining approach to predict companies’ financial distress," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(03), pages 1-13, September.
    • Handle: RePEc:wsi:ijfexx:v:07:y:2020:i:03:n:s2424786320500310
    DOI: 10.1142/S2424786320500310
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S2424786320500310
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S2424786320500310?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.

    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:wsi:ijfexx:v:07:y:2020:i:03:n:s2424786320500310. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscientific.com/worldscinet/ijfe .

    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.