Time-Varying Earnings Persistence and the Delayed Stock Return Reaction to Earnings Announcements
Bibliographic record
Abstract
This paper examines how investors assimilate firm-specific earnings persistence into prices in the context of delayed stock return reactions to earnings announcements (i.e., post-earnings-announcement drift, or PEAD). The literature predicts that if investors fail to recognize fully the time-series auto correlations in a firm’s earnings series (a time-series measure of earnings persistence, or time-series persistence), the magnitude of PEAD will increase with the level of the auto correlations. However, subsequent research demonstrates that the magnitude of PEAD is invariant to time-series persistence, which is inconsistent with the argument that PEAD reflects the market inefficiency caused by investors’ inability to differentiate earnings persistence across firms. I approach the issue from a new perspective, arguing that at the firm level, earnings persistence varies with changing accounting and economic fundamentals over time (a cross-sectional measure of earnings persistence, or time-varying persistence), which is an earnings attribute not captured by time-series persistence. I show that although the magnitude of PEAD is invariant to time-series persistence, it increases with time-varying persistence and investors appear to revise their expected persistence in the direction suggested by changing fundamentals after earnings announcements. Moreover, for firms that have complex information structures, I establish a link between investors’ trend-extrapolation heuristic and their recognition bias in time-varying persistence. Taken together, my results indicate that PEAD appears to arise from investors’ delay in incorporating the complex information about fundamentals necessary to estimate time-varying (but not time-series) persistence. This implies that information complexity triggers the market inefficiency in assimilating firm-specific time-varying earnings persistence into prices.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".