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

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  • 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
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    References listed on IDEAS

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    1. Michele Rabasco & Pietro Battiston, 2023. "Predicting the deterrence effect of tax audits. A machine learning approach," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 531-556, July.
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