Do Firms Use Tax Reserves to Meet Analysts’ Forecasts? Evidence from the Pre‐ and Post‐<scp>FIN</scp> 48 Periods
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it