IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v58y2026i22p4218-4233.html

Capital structure and product market performance: a Bayesian machine learning perspective

Author

Listed:
  • Kousik Guhathakurta
  • Sujay Mukhoti

Abstract

This paper presents a hierarchical Bayesian semi-parametric model leveraging adaptive splines to explore the non-linear relationship between leverage and output performance in firms. The model employs automated feature selection through spline-based knot detection and incorporates regularization techniques to address challenges like endogeneity, heteroscedasticity, and multicollinearity. Bayesian inference is performed using Markov Chain Monte Carlo (MCMC) methods, enabling robust estimation and uncertainty quantification. Applied to a panel of 164 Indian firms (2011–2020), the model reveals non-monotonic patterns in leverage-performance relationships. This study bridges econometrics and machine learning, offering a flexible, data-driven approach to analyse financial and market interactions in firms.

Suggested Citation

  • Kousik Guhathakurta & Sujay Mukhoti, 2026. "Capital structure and product market performance: a Bayesian machine learning perspective," Applied Economics, Taylor & Francis Journals, vol. 58(22), pages 4218-4233, May.
    • Handle: RePEc:taf:applec:v:58:y:2026:i:22:p:4218-4233
    DOI: 10.1080/00036846.2025.2492901
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2025.2492901
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2025.2492901?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:58:y:2026:i:22:p:4218-4233. 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.