IDEAS home Printed from https://ideas.repec.org/a/aka/aoecon/v65y2015isupplement2p3-16.html
   My bibliography  Save this article

Study on Early Warning of Enterprise Financial Distress – Based on Partial Least-squares Logistic Regression

Author

Listed:
  • Kun Xu

    (School of Management, Beijing Union University, Chaoyang District, Beijing, 100101)

  • Qilan Zhao

    (Economic and management school, Beijing Jiaotong University, Haidian District, Beijing, 100044)

  • Xinzhong Bao

    (School of Management, Beijing Union University, Chaoyang District, Beijing, 100101)

Abstract

Establishment of an effective early warning system can make the company operators make relevant decisions as soon as possible when finding the crisis, improve the operating results and financial condition of enterprise, and can also make investors avoid or reduce investment losses. This paper applies the partial least-squares logistic regression model for the analysis on early warning of enterprise financial distress in consideration of quite sensitive characteristics of common logistic model for the multicollinearity. The data of real estate industry listed companies in China are used to compare and analyze the early warning of financial distress by using the logistic model and the partial least-squares logistic model, respectively. The study results show that compared with the common logistic regression model, the applicability of partial least-squares logistic model is stronger due to its eliminating multicollinearity problem among various early warning indicators.

Suggested Citation

  • Kun Xu & Qilan Zhao & Xinzhong Bao, 2015. "Study on Early Warning of Enterprise Financial Distress – Based on Partial Least-squares Logistic Regression," Acta Oeconomica, Akadémiai Kiadó, Hungary, vol. 65(supplemen), pages 3-16, December.
  • Handle: RePEc:aka:aoecon:v:65:y:2015:i:supplement2:p:3-16
    as

    Download full text from publisher

    File URL: http://www.akademiai.com/doi/pdf/10.1556/032.65.2015.S2.2
    Download Restriction: subscription
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Shilpa Shetty H. & Theresa Nithila Vincent, 2024. "Corporate Default Prediction Model: Evidence from the Indian Industrial Sector," Vision, , vol. 28(3), pages 344-360, June.
    2. Sam Ngwenya, 2018. "Assessing the State of Financial Distress of Listed Gold and Platinum Mining Companies in South Africa," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 14(4), pages 655-677, AUGUST.

    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:aka:aoecon:v:65:y:2015:i:supplement2:p:3-16. 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: Kriston, Orsolya (email available below). General contact details of provider: https://akademiai.hu/ .

    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.