Accounting Quality, Stock Price Delay, and Future Stock Returns*
Bibliographic record
Abstract
In frictionless capital markets with complete information and rational investors, stock prices adjust to new information instantaneously and completely. However, a substantial body of research studies information imperfections such as asymmetric information and incomplete information. Information imperfections potentially hinder timely price discovery and are likely associated with delayed stock price adjustment to information. Our first research question therefore is whether the quality of accounting information (or “accounting quality”) is one such information imperfection that is associated with cross‐sectional variation in stock price delay. We define accounting quality as the precision with which financial reports convey information to equity investors about the firm’s expected cash flows. Poor accounting quality is likely associated with higher expected returns through uncertainty about stock valuation parameters and incomplete information. Our second research question therefore is whether the accounting quality component of price delay is associated with higher future stock returns. Consistent with our hypotheses, the results show that poor accounting quality is associated with delayed price adjustment and higher future stock returns. Thus, accounting quality plays a role in timely stock price discovery.
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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 | 0.028 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".