An Empirical Assessment of Alternative Discretionary Accrual Models: Evidence from Earnings Restatements
Bibliographic record
Abstract
Using a sample of firms that restated earnings, this study seeks to evaluate the performance of alternative discretionary accrual models along two dimensions: earnings management detection and accuracy (the ability to accurately estimate the magnitude of managed earnings). The findings of this study are important for three reasons. First, discretionary accrual models play a prominent role in several streams of accounting research, especially in earnings management research. Thus, the ability of discretionary accrual models to isolate the discretionary component from the non-discretionary component of total accruals is critical. Second, there is concern about earnings management inferences drawn from discretionary accrual estimates generated by existing discretionary accrual models. One major concern is that extant discretionary accrual models are mis-specified, which results in misleading inferences about earnings management behavior. Finally, there is lack of consensus in the literature on the relative performance of discretionary accrual models. Using earnings restatements data, I investigate the relative performance of four extant discretionary accrual models and a Modified Forward-Looking Model. The findings indicate that the Modified Forward-Looking Model is better specified and outperforms the other models both in terms of detecting earnings management and in estimating the magnitude of managed 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 | 0.052 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".