The Determinants and Information Effects of Earnings Announcement Date Variability
Bibliographic record
Abstract
Prior research finds mixed evidence that firms strategically manage their earnings announcement timing to either highlight or obscure financial information. While most prior studies focus on the specific timing and the nature of individual earnings announcements, we instead focus on the variability of firms’ annual earnings announcement dates (hereafter referred to as EADs) over a span of time. Using archival data collected from I/B/E/S and Compustat, we find that firms with fewer resources, weaker internal monitoring systems, and greater financial uncertainty are much more likely to exhibit increased EAD variability. Furthermore, we provide substantial evidence that the capital market’s response to earnings is noticeably weaker when a firm’s EAD variability is higher. Additional in-depth analysis reveals that firms exhibiting higher EAD variability tend to report significantly lower future performance in both the short- and long-term horizons. Consequently, while managers might intentionally alter an earnings announcement date to exploit variations in investor attention, this comprehensive study provides significant evidence that they should also consider how the market perceives and interprets the overall EAD variability. This understanding is crucial to improve strategic financial communication and maintain investor trust.
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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 | 0.051 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".