Last‐Chance Earnings Management: Using the Tax Expense to Meet Analysts' Forecasts*
Bibliographic record
Abstract
Abstract We assert that the tax expense is a powerful context in which to study earnings management, because it is one of the last accounts closed prior to earnings announcements. Although many pre‐tax accruals must be posted in the year‐end general ledger, managers estimate and negotiate tax expense with their auditors immediately prior to earnings announcements. We hypothesize that changes from third‐ to fourth‐quarter effective tax rates (ETRs) are negatively related to whether and how much a firm's earnings absent tax expense management miss analysts' consensus forecast, a proxy for target earnings. We measure earnings absent tax expense management as actual pre‐tax earnings adjusted for the annual ETR reported at the third quarter. We provide robust evidence that firms lower their projected ETRs when they miss the consensus forecast, which is consistent with firms decreasing their tax expense if non‐tax sources of earnings management are insufficient to achieve targets. We also find that firms that exceed earnings targets increase their ETR, but this effect is less significant. By studying the tax expense in total, rather than narrow components of deferred tax expense, our results provide general evidence that reported taxes are used to manage earnings.
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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 | 0.003 | 0.033 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".