Information Coordination: Does Preannouncement Media Coverage Improve Earnings Expectations?*
Bibliographic record
Abstract
ABSTRACT This study examines whether and how media coverage improves price discovery before earnings announcements. Given the broad reach and timeliness of media coverage, understanding its role in forming earnings expectations is important. I posit that the media specialize in searching for and screening news, while institutional investors and financial analysts specialize in interpreting and integrating information. I expect these coordinated efforts to improve earnings expectations and preempt upcoming earnings announcements. Consistent with this prediction, I find (i) preannouncement media coverage is negatively associated with price revaluations during earnings announcements, (ii) preannouncement media coverage is positively associated with institutional investors' information acquisition and financial analysts' forecast frequency, and (iii) institutional information acquisition and analyst forecast activity are two mechanisms by which media coverage preempts upcoming earnings. My main results are robust to short‐window tests and identification concerns. Overall, my findings suggest that media coverage coordinates information efforts and accelerates the incorporation of information in prices, facilitating preannouncement price discovery.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".