Regulation Fair Disclosure and Analysts' First-Forecast Horizon
Bibliographic record
Abstract
We examine the impact of Regulation Fair Disclosure (RFD) on the horizon of analysts' first earnings forecasts, that is, the first-forecast horizon. The first-forecast horizon is computed as the number of calendar days between the analysts' first earnings forecast for a quarter and the fiscal quarter-end date. We find that the first-forecast horizon has decreased by twelve days after RFD: a 6 percent decrease, on average. Analysts with average annual first-forecast horizon in the top 25 percent for each firm are classified as leaders. Leaders are our proxy for favored analysts who received guidance before RFD. The first-forecast horizon of both the leaders and the followers decreased after RFD. Examining whether the difference between the first-forecast horizon of leaders and followers decreased after RFD provides mixed evidence. This suggests that RFD may not have eliminated the timing advantage that few analysts enjoyed before RFD.
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How this classification was reachedexpand
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".