Unintended Effects of Preannouncements on Investor Reactions to Earnings News*
Bibliographic record
Abstract
Abstract This study uses an experiment to examine three alternative theoretical explanations for the unintended effects of preannouncements on investor reactions to earnings news. The theoretical explanations are cue consistency, recency effects, and diminishing marginal reactions. The experiment varies the amount of a management preannouncement at five different levels while holding constant consensus analyst expectations prior to the preannouncement and the subsequent earnings announcement. Participants provide preliminary forecasts of current‐ and next‐period earnings per share (EPS) prior to the preannouncement, after the preannouncement, and after the earnings announcement. The pattern of participants' final next‐year EPS forecasts and the results of follow‐up analyses appear most consistent with the predictions of diminishing marginal reactions and, to a somewhat lesser extent, cue consistency, suggesting that both mechanisms play a role in determining the effects of preannouncements. There is little evidence supporting recency effects. Finally, supplemental evidence indicates that participants are unaware that preannouncements influence their reactions to earnings news, suggesting that the effects are unintended. This study has implications for managers who make preannouncement disclosure decisions and for academics who wish to understand and interpret prior research on earnings preannouncements.
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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 | 0.005 | 0.038 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".