The Effects of Firm Growth and Model Specification Choices on Tests of Earnings Management in Quarterly Settings
Bibliographic record
Abstract
ABSTRACT Commonly used Jones-type discretionary accrual models applied in quarterly settings do not adequately control for nondiscretionary accruals that naturally occur due to firm growth. We show that the relation between quarterly accruals and backward-looking sales growth (measured over a rolling four-quarter window) and forward-looking firm growth (market-to-book ratio) is non-linear. Failure to control for the effects of firm growth and performance on innate accruals leads to excessive Type I error rates in tests of earnings management. We propose simple refinements to Jones-type models that deal with non-linear growth and performance effects and show that the expanded models are well-specified and exhibit high power in quarterly settings where one is testing for earnings management. The expanded models are able to identify the presence of earnings management in a sample of restatement firms. Our findings have important implications for the use of discretionary accrual models in earnings management research. JEL Classifications: C15; M40; M41.
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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 | 0.077 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".