IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v6y2024i4p48-984d1506850.html
   My bibliography  Save this article

Using Machine Deep Learning AI to Improve Forecasting of Tax Payments for Corporations

Author

Listed:
  • Charles Swenson

    (Marshall School of Business, University of Southern California, Los Angeles, CA 90089, USA)

Abstract

This paper aims to demonstrate how machine deep learning techniques lead to relatively accurate forecasts of quarterly corporate income tax payments. Using quarterly data from Compustat for all U.S. publicly traded corporations from 2000 to 2024, I show that neural nets, the tree method, and random forest models provide robust forecasts despite their encompassing COVID-19 pandemic time periods. The results should be of interest to corporate tax planners, stock analysts, and governments.

Suggested Citation

  • Charles Swenson, 2024. "Using Machine Deep Learning AI to Improve Forecasting of Tax Payments for Corporations," Forecasting, MDPI, vol. 6(4), pages 1-17, October.
    • Handle: RePEc:gam:jforec:v:6:y:2024:i:4:p:48-984:d:1506850
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/6/4/48/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-9394/6/4/48/
    Download Restriction: no
    ---><---

    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:gam:jforec:v:6:y:2024:i:4:p:48-984:d:1506850. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.