Why Firms Announce Good News Late: Earnings Management and Financial Reporting Timeliness*
Bibliographic record
Abstract
ABSTRACT Prior studies find that delayed earnings announcements tend to communicate unfavorable news, and investors react negatively when firms delay earnings announcements. However, these findings do not explain why investors discount delayed earnings, even after controlling for the earnings news, and why firms sometimes announce good news late. Motivated by theory from Trueman (1990) that attempts to explain these phenomena, we examine whether announcement delays indicate earnings management. We predict and find that good news firms with higher discretionary accruals are more likely to announce earnings late. Consistent with post fiscal year‐end activities driving announcement delays, we fail to find a relation between measures of real earnings management and late announcements. Using a last‐chance earnings management measure based on tax expense manipulation, we also predict and find strong evidence that good news firms engaging in last‐chance earnings management are more likely to delay earnings announcements. Consistent with Trueman's (1990) theory that earnings management explains why investors discount delayed earnings announcements, we find that, on average, earnings announcement returns are 1.4% lower for late announcers relying on last‐chance earnings management to report good news. Overall, our findings suggest that announcement delays provide information about not only the sign of the earnings news but also the potential for earnings management.
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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 | 0.003 | 0.032 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".