Media Co-Coverage and Overreaction in Cross-Industry Information Transfers
Bibliographic record
Abstract
This study examines whether media co-coverage – a phenomenon where multiple firms are simultaneously mentioned in the same news article as contextual information – induces excessive inter-industry information transfers between two firms due to the increased saliency of their relationship. Using firms from different product market industries that are co-covered in the same Wall Street Journal article, we find that, after co-coverage, the stock price of a co-covered focal firm reacts positively to the earnings surprise of another early-announcing co-covered peer, followed by a reversal on the focal firm’s subsequent earnings announcement day, while there is no reaction to the peer’s earnings news in the pre-co-coverage period. Further analysis suggests that the transfer and the reversal are stronger when the co-coverage information is more salient to investors, and are concentrated among firms with more active retail trading. These findings suggest that co-coverage in financial media, through the saliency effect, can lead to inefficient cross-industry information transfers.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".