Work

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

Year: 2024

Type: article

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.

Source: Forecasting

DOI: 10.3390/forecast6040048

Author: Charles Swenson

Institution: University of Southern California

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,046

OpenAlex ID: W4403749754

FWCI: 3.4

Cites: 34

Cited by: 11

Related to: 10