<i>The New York Times</i>and<i>Wall Street Journal</i>: Does Their Coverage of Earnings Announcements Cause “Stale” News to Become “New” News?
Bibliographic record
Abstract
Recent research suggests that the stock market reacts to stale information if it is reported in the media because it is gives the impression of being “new” news. The objective of this study is to provide a unique test of this hypothesis using the time-series properties of quarterly earnings. It is well documented that seasonally differenced quarterly earnings for adjacent quarters are positively correlated. Therefore a component of current quarter earnings when reported is news that was known or predictable at the end of the prior quarter and thus is old news. We find for those firms that receive media coverage in the Wall Street Journal and The New York Times that the price reaction at the time of the announcement of current earnings to past quarter's seasonally differenced quarterly earnings is greater than those firms that do not receive media coverage. The result is consistent with stale earnings information being given the appearance of new information resulting in a further price reaction. This suggests that the stale information hypothesis and media coverage could be a partial explanation for post-earnings announcement drift.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".