Timeliness of Firms’ Voluntary Disclosure of Good and Bad News. Working Paper
Bibliographic record
Abstract
Mixed views exist about whether firm managers voluntarily disclose good news more timely than they do bad news. Our study investigates this issue by inferring managers’ strategic disclosure behavior from the stock returns in four adjacent windows for a fiscal quarter, prior to and including the earnings announcement. We find that large firms disclose the same proportion of news in each examination window in good- and bad-news quarters; however, very bad news (i.e. severe negative returns) is more frequent during the quarter than after the quarter ends. In contrast, small firms disclose a larger proportion of news early in good- rather than bad-news quarters; however, very bad news is most frequent around earnings announcement. Our results suggest that the timeliness of firms ’ voluntary disclosure of good vs. bad news varies with firm size and the degree of news. Timeliness of Firms ’ Voluntary Disclosure of Good and Bad News
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".