IDEAS home Printed from https://ideas.repec.org/a/mes/emfitr/v60y2024i4p678-687.html
   My bibliography  Save this article

Predicting Financial Distress Using a MIDAS Hazard Model: Evidence from Listed Companies in China

Author

Listed:
  • Xiangrong Li
  • Maojun Zhang
  • Jiangxia Nan
  • Qingyuan Yang

Abstract

This study aims to predict financial distress in an emerging country using data on ST listed companies in China from 2001 to 2021. A new Aalen hazard model with mixed data sampling (MIDAS) is adopted to investigate the impact of monthly macroeconomic variables and quarterly financial variables on financial distress. The empirical results show that the current ratio, operating profit ratio, current capital ratio, retention ratio, profit ratio and income ratio of listed companies have a significant impact on the time-varying intensity of financial distress. The consumer price index has a negative relation with the intensity of financial distress, while the production price index and credit spreads have a positive influence. Finally, the results of the robustness tests are consistent with those with different lag orders.

Suggested Citation

  • Xiangrong Li & Maojun Zhang & Jiangxia Nan & Qingyuan Yang, 2024. "Predicting Financial Distress Using a MIDAS Hazard Model: Evidence from Listed Companies in China," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 60(4), pages 678-687, March.
    • Handle: RePEc:mes:emfitr:v:60:y:2024:i:4:p:678-687
    DOI: 10.1080/1540496X.2023.2244140
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1540496X.2023.2244140
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/1540496X.2023.2244140?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 search for a different version of it.

    More about this item

    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:mes:emfitr:v:60:y:2024:i:4:p:678-687. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/MREE20 .

    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.