IDEAS home Printed from https://ideas.repec.org/a/eee/pacfin/v95y2026ics0927538x25003178.html

USING neural networks and CEO remuneration to predict the debt-income ratio of New Zealand local councils

Author

Listed:
  • Chatterjee, Bikram
  • Bhattacharya, Sukanto
  • Mukherjee, Abhishek

Abstract

This is an exploratory study that takes a machine learning approach using artificial neural networks to investigate a conjectured relationship between CEO remuneration and debt of New Zealand local councils. While prior literature reported an inverse relationship between CEO remuneration and financial distress in the private sector, no study is known to have yet examined a relationship between CEO remuneration and debt in the public/government sector. Using data from seventy-eight New Zealand local councils, the results reveal that a neural network model can reliably predict whether a local council holds a high amount of debt with CEO remuneration as the key predictor variable. The results strongly indicate that an underlying statistical relationship likely exists between CEO remuneration and the amount of council debt in the presence of political competition. These results run contrary to previous findings in the private sector and offer a promising ground for future confirmatory studies to further investigate this relationship.

Suggested Citation

  • Chatterjee, Bikram & Bhattacharya, Sukanto & Mukherjee, Abhishek, 2026. "USING neural networks and CEO remuneration to predict the debt-income ratio of New Zealand local councils," Pacific-Basin Finance Journal, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:pacfin:v:95:y:2026:i:c:s0927538x25003178
    DOI: 10.1016/j.pacfin.2025.102980
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927538X25003178
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.pacfin.2025.102980?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

    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:eee:pacfin:v:95:y:2026:i:c:s0927538x25003178. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/pacfin .

    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.