Throw the Baby Out with the Bath Water: Problems with Performance Matched Discretionary Accrual Measures
Bibliographic record
Abstract
Prior studies show that discretionary accruals estimated from Jones type models are higher (lower) than expected for firms with high (low) reported earnings, raising concerns that using these models to estimate discretionary accruals will bias the test result. To address the concern, Kothari et al. (2005) propose that researchers adjust estimates of discretionary accruals from Jones type models for ROA. Kothari et al. (2005) argue their ROA-adjusted models will not under-reject the null of no earnings management. I show in this study that they are wrong. Regardless of the direction of causation for the association of discretionary accruals estimated from Jones type models with reported earnings, the ROA-adjusted models will have a high frequency of Type II errors and under-reject the null of no earnings management; that is, to “throw the baby out with the bath water.” Moreover, I analyze the relation of discretionary accruals estimated from Jones type models with reported earnings, and empirically examine the relation of discretionary accruals estimated from these models with proxies of true earnings. The results suggest the empirical relation of discretionary accruals estimated from these models with reported earnings is largely, if not entirely, explained by a tendency of firms with high (low) discretionary accruals to have high (low) reported earnings. Therefore, it is wrong to control for reported earnings (ROA), as Kothari et al. (2005) suggest, when estimating discretionary accruals to test for event-induced earnings management. Rather, researchers should control for other events and firm attributes that are known to induce firms to manage earnings.
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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 | 0.084 | 0.363 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".