Forecasting Taxes: New Evidence from Analysts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT We provide new evidence about how analysts incorporate and improve on management ETR forecasts. Quarterly ETR reporting under the integral method provides mandatory point-estimate forecasts by management, but firms must record certain “discrete” tax items fully in the quarter in which they occur, polluting these forecasts. We investigate management ETR accuracy, analysts' decisions to mimic management's estimate, analysts' accuracy relative to each other or to management, and dispersion. Our comprehensive analysis reveals that analysts deviate from management more and are more accurate relative to management as complexity increases, with real effects on EPS accuracy and dispersion. In contrast to prior research that analysts ignore or are confused by taxes, we provide evidence that analysts pay attention to taxes and improve on management estimates. Based on our evidence that management's quarterly ETRs have less predictive value in the presence of discrete items, we suggest standard-setters reexamine the discrete item exception to require more disclosure.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it