Accrual‐Based Earnings Management and Regulation: A Literature Review*
Bibliographic record
Abstract
ABSTRACT This paper reviews how the accounting literature has investigated accrual‐based earnings management (AEM) in relation to regulation. After describing the development of accrual‐based models to measure earnings management, I provide evidence that the accounting literature has investigated AEM and regulation to answer six types of research questions. First, researchers investigate whether firms manage earnings before a regulatory event to benefit from it or to avoid its negative consequences. Second, they look at whether firms engage in AEM after the implementation of a new regulation to avoid the regulatory costs associated with lack of compliance or to respect regulatory industry ratios. Third, researchers use accruals quality metrics to investigate the change in quality of accounting after a change in regulation. Fourth, they use accruals quality metrics to analyze the impact of differences between regulatory environments. Fifth, researchers exploit Accounting and Auditing Enforcement Releases to clearly identify samples of low‐quality firms in order to develop new earnings management models, test the specifications of existing models, or identify new patterns linked to earnings management. Sixth, the accounting literature employs regulation to investigate potential complementarity or substitution effects between accrual‐based and real earnings management. I also discuss how the emergence of new technologies such as machine learning, the ongoing debate between single‐accrual and aggregate models, regulatory events other than laws, and recent regulations create opportunities for future research into AEM in relation to regulation.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".