Earnings Management: Do Firms Play “Follow the Leader”?
Bibliographic record
Abstract
Abstract In this study we examine whether the reported performance of one firm affects the discretionary reporting behavior of another firm. We do this by identifying the leader within each industry, defined as the first large announcing firm. We find that the discretionary performance of followers (those firms announcing after the leader) relates positively to the leader's reported performance. Specifically, when the leader misses analysts’ expectations, followers report lower discretionary accruals, have fewer income‐decreasing special items, and are less likely to meet analysts’ expectations. In contrast, when leaders report good news, followers report higher discretionary accruals and are more likely to meet expectations (although we do not find evidence of a positive association between leaders’ good news and followers’ income‐decreasing special items). Overall, the results are consistent with managers of followers perceiving that earnings news of the leader will affect investors’ and others’ performance expectations for their firms.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.006 |
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".