Street versus <scp>GAAP</scp>: Which Effective Tax Rate Is More Informative?*
Bibliographic record
Abstract
ABSTRACT This study investigates how sophisticated market participants use tax‐based information by examining whether analysts' street effective tax rates (ETRs) are informative. When assessing firm performance, analysts exclude items they believe do not reflect current performance, resulting in “street” metrics such as street ETR. However, evidence on the properties of the components of street earnings is limited. Examining the informativeness of street ETRs is important because taxes are a significant component of earnings, and the extent to which analysts understand taxes and incorporate them into their analyses is not clear. Using a hand‐collected sample of analyst reports, we find that while approximately 35% of street ETRs have at least one tax‐specific exclusion, over 90% reflect the tax effects of pre‐tax exclusions. Further, both tax‐specific exclusions and the tax effects of pre‐tax exclusions significantly contribute to differences between GAAP and street ETRs. Consistent with analysts' understanding of the implications of tax and nontax exclusions, our results suggest that street tax metrics exhibit greater predictive ability about future tax outcomes and provide more information to investors than GAAP tax metrics. We also find that ETR exclusions are of higher quality when the magnitude of the potentially excluded item is greater and when managers disclose pro forma earnings. Collectively, our findings suggest that analysts understand taxes, but selectively exert effort to incorporate tax‐based information into their assessment of firm performance. Our study should be informative to regulators and users of financial information because it provides evidence regarding the usefulness of street earnings metrics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".