IDEAS home Printed from https://ideas.repec.org/a/mes/emfitr/v58y2022i12p3324-3339.html
   My bibliography  Save this article

Controlling Shareholder Characteristics and Corporate Debt Default Risk: Evidence Based on Machine Learning

Author

Listed:
  • Di Wang
  • Zhanchi Wu
  • Bangzhu Zhu

Abstract

The influence of controlling shareholder characteristics on corporate risk has been a popular topic for discussion in academic and theoretical circles. However, current research lacks systematic and quantitative conclusions based on predictive ability, as it only focuses on the causal relationship between a single characteristic of the controlling shareholder and corporate risk. This paper utilizes the back propagation neural network based on gray wolf algorithm (GWO-BP) method in the machine learning algorithm for the first time and takes the listed companies that publicly issue bonds in the Chinese bond market as a research sample. It summarizes the qualities of controlling shareholders from the perspective of controlling shareholders’ risk-taking and benefits expropriation and examines multi-dimensional controlling shareholder characteristics for predicting the debt default risk of companies. This research established that: (1) Overall, the characteristics of controlling shareholders can improve the ability to predict the debt default of a company; (2) The features of the investment portfolio of the controlling shareholder have a higher degree of predicting the debt default risk of a company,while the properties of equity structure and related transactions have a lower degree of predicting the risk of corporate debt default.This research not only uses machine learning methods to study controlling shareholders in China from a more comprehensive perspective but also provides a useful incentive for bondholders to protect their interests.

Suggested Citation

  • Di Wang & Zhanchi Wu & Bangzhu Zhu, 2022. "Controlling Shareholder Characteristics and Corporate Debt Default Risk: Evidence Based on Machine Learning," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 58(12), pages 3324-3339, September.
  • Handle: RePEc:mes:emfitr:v:58:y:2022:i:12:p:3324-3339
    DOI: 10.1080/1540496X.2022.2037416
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1540496X.2022.2037416
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/1540496X.2022.2037416?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.

    Citations

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


    Cited by:

    1. Li, Tangrong & Sun, Xuchu, 2023. "Is controlling shareholders' credit risk contagious to firms? — Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    2. Wu, Haoyang & Jiao, Ziyan & Wang, Shipeng & Wu, Zhiruo, 2024. "Corporate mergers and acquisitions: A strategic approach to mitigate expected default frequency," Finance Research Letters, Elsevier, vol. 64(C).

    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:58:y:2022:i:12:p:3324-3339. 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.