Would Managers Sacrifice Conservative Financial Reporting to Meet/Beat Market Earnings Expectations?
Bibliographic record
Abstract
Prior studies show that engaging in conservative financial reporting (CON) positively affects earnings quality. However, managers also manage earnings to meet/beat market earnings expectations (MBME). This study asks three questions regarding the earnings that MBME. First, it investigates whether managers are willing to sacrifice CON when adopting strategies to MBME. Second, it tests whether managers prefer to use other earnings management (EM) strategies to MBME instead of sacrificing CON. Third, it tests whether information asymmetry between managers and shareholders affects managers’ decisions to sacrifice CON. Results show that managers are more likely to sacrifice CON to MBME but are less likely to do so if they can manage earnings using accrual-based or real EM. Also, managers are more likely to do so when information asymmetry with shareholders is higher. These findings contribute to the literature by examining the circumstances in which managers would sacrifice CON to MBME.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".