Regulation Fair Disclosure and Analysts' First-Forecast Horizon
Bibliographic record
Abstract
We examine the impact of Regulation Fair Disclosure (RFD) on first-forecast horizon of analysts' earnings forecasts. The first-forecast horizon is computed as the number of calendar days between the issue of the analysts' first earnings forecast for a quarter and the fiscal quarter-end date. We find that the first-forecast horizon has decreased by about 12 days after RFD: a 10 percent decrease. The top 25 percent of the analysts for each firm are classified as leaders based on the average first-forecast horizon over each year. Leaders are our proxy for favored analysts because obtaining private guidance before RFD would help such analysts provide forecasts earlier. We find that the first-forecast horizon of the leaders decreased by about 18 days, while that of the followers decreased by about 8 days, on average after RFD. This shows that the playing field has been made more level, in terms of eliminating the timing advantage that a select few analysts enjoyed prior to RFD. Specifically, the differential timing advantage between leaders and followers has decreased by about 10 days, out of a differential of 100 days prior to RFD.
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.
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 | 0.007 | 0.057 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".