Voluntary Disclosure of Good and Bad Earnings News in a Low Litigation Setting*
Bibliographic record
Abstract
ABSTRACT This study uses a historical setting in which expected litigation costs were low (i.e., Australia, from 1993 to 1996) to investigate whether companies with good news were more likely to preempt annual earnings than their counterparts with bad news. Empirical tests compare the probability of preemption conditional on having good news with the probability of preemption conditional on having bad news. The models control for other potential determinants of disclosure policy that have been documented in the literature. The results do not support the research hypothesis that companies with good news were more likely to preempt annual earnings than companies with bad news. This finding suggests that there may be other factors driving disclosure of bad news, in addition to those acknowledged in the extant literature. The evidence also indicates that in Australia during the investigation period, the probability of preemption was positively associated with firm size and analyst following and differed as a function of industry membership.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".