IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-268-2_17.html

Logistic Model-based Prediction of Financial Distress of Listed Chinese Real Estate Companies

In: Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023)

Author

Listed:
  • Yueru Chai

    (Fudan University)

  • Yixin Gan

    (Shanghai International Studies University)

  • Yiou Wang

    (Southwestern University of Finance and Economics)

Abstract

With the introduction of restrictive housing price policies in China contemporarily, real estate companies are facing new development challenges. China Evergrande Group’s debt crisis has made people emphasize more on the financial status of real estate companies. In this paper, 20 Chinese A-share companies facing financial distress and 20 companies without financial distress are selected as research samples. A total of 16 financial indicators and non-financial indicators are chosen to establish Logistic-based model. Finally, suitable early warning indicators were screened and a financial distress early warning model with high accuracy was derived. The percentage of administrative expenses has the greatest impact on whether a company is in financial distress, and real estate companies should strengthen their operating cost control. By analyzing the model of real estate companies, the company operators can pay attention to the problems that arise in the company’s operation in advance and can take corresponding measures to avoid the company from falling into financial distress. Overall, these results shed light on guiding further exploration of enterprise financial distress forecast.

Suggested Citation

  • Yueru Chai & Yixin Gan & Yiou Wang, 2024. "Logistic Model-based Prediction of Financial Distress of Listed Chinese Real Estate Companies," Advances in Economics, Business and Management Research, in: Faruk Balli & Hui Nee Au Yong & Sikandar Ali Qalati & Ziqiang Zeng (ed.), Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023), pages 136-145, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-268-2_17
    DOI: 10.2991/978-94-6463-268-2_17
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:advbcp:978-94-6463-268-2_17. 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: 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.