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

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

Forecasting, 2024, vol. 6, issue 4, 1-17

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.

Keywords: machine learning; taxation; forecasts (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2024
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