Do Rapid Reversals of Prior-Quarter Asset Impairment Recognition and the Strength of Corporate Governance Influence Earnings Quality?
Bibliographic record
Abstract
This study primarily explores the influence of rapid and frequent reversals ofprior-quarter asset impairment recognition on the earnings quality of firms. Firmsthat recognize asset impairment losses in a quarter and reverse them in thesubsequent quarters of the same year and firms that recognize impairment reversalsin a quarter and recognize impairment losses in the subsequent quarters areexamined.We use the absolute level of abnormal accruals as a proxy for earnings qualityand compare the earnings quality of firms that reverse asset impairment losses orloss reversals recognized in the prior quarter of the year (the changed group) withthe earnings quality of firms that do not (the unchanged group). The empiricalresults reveal that the changed group has lower abnormal accruals than those of the unchanged group, implying that rapid and frequent reversals of prior-quarter assetimpairment recognition are used for reflecting changes in asset values rather thanearnings manipulation. The changed group has higher earnings quality relative tothe unchanged group.We classified the sample firms into two subgroups according to the strength oftheir corporate governance. We find that the aforementioned higher earningsquality in the changed group exists only in the strong governance subgroup. Thisfinding supports the hypothesis that strong corporate governance guarantees rapidand frequent reversals of prior-quarter asset impairment recognition for timelyreflection of asset value changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| 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".