How Useful Are Tax Disclosures in Predicting Effective Tax Rates? A Machine Learning Approach

• 成果类型: Article
• 署名作者: Guenther, David A.; Peterson, Kyle; Searcy, Jake; Williams, Brian M.
• 署名单位: University of Oregon; University of Oregon; Indiana University System; Indiana University Bloomington; IU Kelley School of Business
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• DOI: 10.2308/TAR-2021-0398
• 发表日期: 2023
• 页码: 297-322
• 关键词: earnings management; analysts; recommendations; statements; complexity; access; IMPACT; income

摘要

We investigate (1) how well a machine learning algorithm can predict one-year ahead effective tax rates (ETRs) and (2) which items in the financial statements and notes are most useful for these predictions. We compare our machine-generated ETR predictions with those from ETRs implied by analysts' earnings forecasts and find the algorithm's predictions are less biased, more precise, and explain more of the variance in future ETRs. We then use Explainable AI (based on Shapley values) to measure the usefulness of each disclosure item in the algorithm's predictions. We find that while some tax-related items are useful, others offer minimal value. Using the machine learning algorithm's use of information as a benchmark, we then further use Shapley values to examine which information is underweighted or overweighted by analysts. Overall, our results help inform standard setters on the relevance of certain tax disclosures in achieving the objective of predicting future ETRs.