Private Information Production and the Efficiency of Intra-Industry Information Transfers
Bibliographic record
Abstract
This paper challenges the prevailing view that intra-industry information transfers are primarily driven by public information. Contrary to conventional wisdom, I find that investors in late-announcing firms impound more private information after early-announcing peers report earnings. This increase is substantial, leading to an 18.2% decrease in analyst forecast consensus and a 24.9% increase in forecast precision. Moreover, the probability of informed trading rises by 2% on days with peer announcements. This finding is important because investors tend to overweight (underweight) private (public) signals, thereby exacerbating over- and underreaction anomalies. Our study confirms that these anomalies are more pronounced when early announcements stimulate private information production, offering a theoretical explanation for their puzzling coexistence. These findings have significant implications for investor behavior and market efficiency. Investors should diligently evaluate both public and private information, particularly following peer announcements. Policymakers can leverage these findings to design regulations that promote transparency and foster efficient information dissemination.
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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 | 0.006 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".