We investigate the relation between tax rates and pretax returns by showing how implicit tax, tax incidence, and tax capitalization change in response to a tax rate change. We examine these issues in the context of both financial assets and real investments made by corporations in a competitive equilibrium in which all investments earn the same after-tax rate of return. Results show that the pretax return increases in the statutory tax rate due to an explicit tax rate effect and decreases due to a cost of capital effect; the net effect is ambiguous. In contrast, the implicit tax rate is weakly increasing in the statutory tax rate. We also relate our findings to the empirical literature on the effects of taxes on pretax returns.

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.