Earnings management and asymmetric sensitivity of bonus compensation to earnings for high-growth firms
Bibliographic record
Abstract
In this paper, we examine whether high-IOS (investment opportunity set) firms vis-à-vis non-growth (low-IOS) firms will not reduce discretionary expenditures, such as advertising expenses, research and development, and SG&A (selling, general and administrative) expenses, to further sustain the firm growth in a more conservative reporting environment (the post-Sarbanes-Oxley (SOX) period). We also investigate, as an extension of a prior paper, the sensitivity of CEO bonuses to earnings in the cases of high-IOS and low-IOS firms. We find a stronger association between incentive compensation and asymmetric sensitivity of bonus to earnings for high-IOS firms in the pre-SOX period, and this asymmetric sensitivity disappears even for high-IOS in the post-SOX period. As in a prior study, we also look into whether accounting conservatism is stronger in the post-SOX period for both high-IOS and low-IOS firms than in the pre-SOX period. The findings are consistent with our hypotheses that high-IOS firms vis-à-vis low-IOS firms will not reduce discretionary expenditures, asymmetric sensitivity bonus to earnings disappears in the post-SOX period for both high-IOS and low-IOS firms, and that accounting conservatism for both high-IOS and low-IOS firms are stronger in the post-SOX period. The documented evidence in this study shows how regulatory changes affect both accrual and real earnings management behaviors, how those regulatory changes affect the sensitivity of bonus compensation to earnings, and how accounting conservatism affects bonus compensation changes in the post-SOX period in relation to the pre-SOX period for both high-IOS and low-IOS firms
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".