Bibliographic record
Abstract
ABSTRACT We quantify the relative importance of earnings announcements in providing new information to the share market, using the R2 in a regression of securities' calendar‐year returns on their four quarterly earnings‐announcement “window” returns. The R2, which averages approximately 5% to 9%, measures the proportion of total information incorporated in share prices annually that is associated with earnings announcements. We conclude that the average quarterly announcement is associated with approximately 1% to 2% of total annual information, thus providing a modest but not overwhelming amount of incremental information to the market. The results are consistent with the view that the primary economic role of reported earnings is not to provide timely new information to the share market. By inference, that role lies elsewhere, for example, in settling debt and compensation contracts and in disciplining prior information, including more timely managerial disclosures of information originating in the firm's accounting system. The relative informativeness of earnings announcements is a concave function of size. Increased information during earnings‐announcement windows in recent years is due only in part to increased concurrent releases of management forecasts. There is no evidence of abnormal information arrival in the weeks surrounding earnings announcements. Substantial information is released in management forecasts and in analyst forecast revisions prior (but not subsequent) to earnings announcements.
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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 | 0.005 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".