IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v57y2025i42p6734-6752.html
   My bibliography  Save this article

Bootstrap variable selection for corporate distress prediction: evidence from the Chinese manufacturing industry

Author

Listed:
  • Shan Li
  • Yajuan Huang
  • Kai Xing
  • Decai Zhou
  • Aifan Ling

Abstract

This study explores the corporate distress prediction of Chinese manufacturing firms over 2013–2018 by using a large number of financial predictors. We extend the bootstrap resampling procedure into corporate distress prediction, combining with the existing selection techniques such as best subset selection, forward stepwise selection, backward stepwise selection, and least absolute shrinkage and selection operator (LASSO). We provide empirical evidence of the advantage of LASSO in selecting key predictors. The models constructed by these four techniques all outperform the prominent two models in the literature. This study proves the effectiveness of combining the bootstrap resampling method with variable selection techniques to predict corporate distress.

Suggested Citation

  • Shan Li & Yajuan Huang & Kai Xing & Decai Zhou & Aifan Ling, 2025. "Bootstrap variable selection for corporate distress prediction: evidence from the Chinese manufacturing industry," Applied Economics, Taylor & Francis Journals, vol. 57(42), pages 6734-6752, September.
    • Handle: RePEc:taf:applec:v:57:y:2025:i:42:p:6734-6752
    DOI: 10.1080/00036846.2024.2386852
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2024.2386852
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2024.2386852?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

    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:taf:applec:v:57:y:2025:i:42:p:6734-6752. 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/RAEC20 .

    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.