Earnings Announcement Disclosures and Changes in Analysts' Information
Bibliographic record
Abstract
Abstract This study examines how financial disclosures with earnings announcements affect sell‐side analysts' information about future earnings, focusing on disclosures of financial statements and management earnings forecasts. We find that disclosures of balance sheets and segment data are associated with an increase in the degree to which analysts' forecasts of upcoming quarterly earnings are based on private information. Further analyses show that balance sheet disclosures are associated with an increase in the precision of both analysts' common and private information, segment disclosures are associated with an increase in analysts' private information, and management earnings forecast disclosures are associated with an increase in analysts' common information. These results are consistent with analysts processing balance sheet and segment disclosures into new private information regarding near‐term earnings. Additional analysis of conference calls shows that balance sheet, segment, and management earnings forecast disclosures are all associated with more discussion related to these items in the questions‐and‐answers section of conference calls, consistent with analysts playing an information interpretation role with respect to these disclosures.
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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 | 0.004 | 0.086 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".