Do managers use a multi‐period, coordinated strategy involving accrual management choices and subsequent earnings forecasts to inflate expectations?
Bibliographic record
Abstract
Abstract We provide evidence that some managers use a multi‐period, coordinated strategy involving inflated current‐period discretionary accruals and optimistic forecasts of future earnings to delay the revelation of bad news. Inflating discretionary accruals increases investor expectations of future performance, and issuing optimistic earnings forecasts of future earnings supports the inflated accruals and extends the horizon for managers to benefit. This strategy is more pronounced for firms that engage in earnings management outside of GAAP, suggesting intentional behavior. Our evidence indicates that managers use this coordinated strategy when firms experience significant bad news and cannot delay revealing all of the bad news through accrual management. We also find that managers use this coordinated strategy when focusing on short‐term performance due to career concerns (i.e., dismissal) or retirement or when they have shorter stock option vesting schedules, which motivates them to inflate investor expectations for shorter‐term personal benefits. Furthermore, managers using this strategy do not hold deep in the money exercisable stock options, which is consistent with managers' private assessment of a higher (lower) likelihood of releasing bad (good) news in the future.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".