Media Co-Coverage and Overreaction in Cross-Industry Information Transfers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it