The Impact of a Heterogeneous Accrual-Generating Process on Empirical Accrual Models
Bibliographic record
Abstract
The cross-sectional approach that is typically used to estimate accrual models implicitly assumes that firms within the same industry have a homogeneous accrual-generating process (AGP). In this article, the authors examine this implicit assumption along three dimensions. First, they argue that the relationship between working-capital accruals and changes in sales is more complex than portrayed by existing empirical accrual models. In addition to sales changes, accruals are also affected by accrual determinants such as firms’ inventory and credit policies. Second, the authors provide evidence that the assumption of a uniform AGP is violated in industries whose firms’ accrual determinants are highly dispersed. Third, they document some implications of violating the assumption of a uniform AGP. Firms in industries with high variations in accrual determinants are likely to have large absolute abnormal accruals. The authors show that the previously documented increase in the absolute level of abnormal accruals over time could be attributed, in part, to the increased heterogeneity in industries with respect to their AGPs.
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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 | 0.068 | 0.326 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| 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".