Discussion—Regulation Fair Disclosure and Analysts' First-Forecast Horizon
Bibliographic record
Abstract
Surya Janakiraman, Suresh Radhakrishnan, and Rafal Szwejkowski (2007), hereafter JRS, examine the impact of regulation fair disclosure (RFD) on the number of days between analysts' first earnings forecasts for the quarter and the fiscal quarter-end (first-forecast horizon). JRS conclude that the first-forecast horizon decreased by twelve days post-RFD; it decreased for both analysts whose average annual first-forecast horizon put them in the top 25 percent for each firm (designated by JRS as leaders) and the bottom 25 percent for each firm (designated by JRS as followers); and it decreased about the same amount for both leaders and followers. JRS interpret their results as follows. RFD reduced the first-forecast horizon on average overall; it reduced the first-forecast horizon for both leaders and followers; and it did not eliminate the timing advantage of leaders versus followers. My discussion proceeds along the following lines. First, I examine whether RFD reduced the first-forecast horizon. Second, I examine whether RFD decreased the first-forecast horizon for both leaders and followers. Third, I examine whether RFD decreased the first-forecast horizon for leaders versus followers.
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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".