Do Firms Use Tax Reserves to Meet Analysts’ Forecasts? Evidence from the Pre‐ and Post‐<scp>FIN</scp> 48 Periods
Bibliographic record
Abstract
Abstract We examine whether firms decrease tax reserves to meet analysts’ quarterly earnings forecasts in the period prior to FIN 48, and whether that behavior changed following FIN 48. We use analysts’ forecasts of pretax and after‐tax income to impute premanaged earnings, or earnings before any tax manipulation. Pre‐FIN 48, we observe that firms reduce their tax reserves (i.e., increase income) when premanaged earnings are below analysts’ forecasts. Specifically, 78 percent of firm‐quarters that would have missed the analyst forecast if not for the tax reserve decrease, meet that target when the decrease is included. Furthermore, we find a significant positive association between the decrease in tax reserves and the deviation of premanaged earnings from analysts’ forecasts. In contrast, post‐FIN 48, we find no evidence that firms use changes in tax reserves to manage earnings to meet analysts’ forecasts. Thus, our results suggest that FIN 48 has, at least initially, curtailed firms’ use of tax reserves 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 | 0.001 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".