Bibliographic record
Abstract
ABSTRACT Consensus estimates, formed by taking an average of analyst forecasts, play an important role in capital markets (e.g., provide investors with a proxy for earnings expectations). We show I/B/E/S, a prominent information intermediary, removes 6% of one‐quarter‐ahead earnings forecasts before calculating the consensus, and among the 23% of firm‐quarters with at least one forecast removed, this figure rises to 16%. We provide evidence suggesting that I/B/E/S subjectively applies policies that govern its removal decisions and accepts feedback from firms that contributes to this subjectivity. Specifically, we find optimistic forecasts are removed more frequently than pessimistic forecasts, and such asymmetry increases further when removals allow firms to meet or beat the consensus. Furthermore, we find that these effects are more pronounced when managers’ incentives to just meet or beat the consensus are stronger (i.e., higher subsequent insider sales or higher compensation delta), or managers have greater ability to influence I/B/E/S. Lastly, we demonstrate that these subjective removals benefit I/B/E/S by improving consensus accuracy, explaining why I/B/E/S is willing to be influenced by firms.
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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 | 0.004 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".