Bibliographic record
Abstract
Abstract: This paper examines the impact of management discretion over accruals on conditional accounting conservatism, defined as the tendency of accountants to recognize bad news on a timelier basis than good news. Prior research suggests that conditional accounting conservatism reflected in earnings is mainly due to the accrual component of earnings, not the cash flow component of earnings. After decomposing total accruals into expected and unexpected accruals, I find that (1) conditional accounting conservatism reflected in accruals is mainly due to unexpected accruals; (2) the negative association between unconditional and conditional accounting conservatism is mainly attributable to unexpected accruals; and (3) firms with higher leverage exhibit conditionally more conservative accounting primarily through unexpected accruals. These results are robust to accrual models that take into account the systematic association between accruals and cash flows and their non‐linearity and to the asymmetric persistence of earnings changes specification of conditional accounting conservatism. Taken together, these results suggest that managers exercise their discretion over accruals to expedite the recognition of bad news rather than good news.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".