How Much New Information Is There in Earnings?
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
ABSTRACT We quantify the relative importance of earnings announcements in providing new information to the share market, using the R 2 in a regression of securities' calendar‐year returns on their four quarterly earnings‐announcement “window” returns. The R 2 , 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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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it