XBRL and Accruals: Empirical Evidence from China
Why this work is in the frame
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Bibliographic record
Abstract
We examine whether XBRL adoption by publicly traded firms on the Shanghai Stock Exchange and Shenzhen Stock Exchange is related to the level of total accruals that firms report in the pre-XBRL versus post-XBRL periods. Our results indicate that the level of total accruals in the post-XBRL period is lower relative to the pre-XBRL period. This finding is robust to several controls for macroeconomic conditions and firm fundamentals. Moreover, we find this main effect is most prominent for firms that are most likely to benefit from greater transparency: high-growth firms, small firms, and firms in high-technology industries. One interpretation of our results is that XBRL implementation decreases investor’s information acquisition costs and thereby improves their ability to detect earnings management; managers in turn reduce accruals.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it