Accounting Quality, Liquidity Risk, and Post‐Earnings‐Announcement Drift
Bibliographic record
Abstract
Abstract Recent microstructure research finds that liquidity risk, in particular its information component, plays an important role in explaining the post‐earnings‐announcement drift (PEAD). We decompose liquidity risk into an accounting‐associated component and a nonaccounting‐associated component and examine their relative importance in explaining PEAD. Our research is motivated by recent findings that liquidity risk is a systematic risk and earnings quality is negatively associated with liquidity risk. We find that the accounting‐associated component is more strongly related to PEAD returns than is its nonaccounting‐associated counterpart. Further analyses reveal that the relation between accounting‐associated liquidity risk and PEAD returns is weaker for firms with greater analyst following. We also find that in a significant market downturn, the relation between accounting‐associated liquidity risk and PEAD returns becomes more pronounced. Our study is the first to document a liquidity risk‐based role of accounting quality in explaining the PEAD phenomenon. It parses out the PEAD risk premia associated with accounting versus nonaccounting sources and, by so doing, sheds light on the role of accounting quality in shaping the liquidity risk‐PEAD returns relation.
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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 | 0.002 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".