What Do Measures of Real-time Corporate Sales Tell Us about Earnings Surprises and Post-Announcement Returns?
Bibliographic record
Abstract
We develop real-time proxies of retail corporate sales from multiple sources, including ~50 million mobile devices.These measures contain information from both the earnings quarter ("within quarter") and the period between that quarter's end and the earnings announcement date ("post quarter").Our within-quarter measure is powerful in explaining quarterly sales growth, revenue surprises, and earnings surprises, generating average excess returns at announcement of 3.4%.However, surprisingly, our post-quarter measure is related negatively to announcement returns, and positively to post-announcement returns.When post-quarter private information is directionally strong, managers, at announcement, provide guidance and use language that points statistically in the opposite direction.This effect is more pronounced when, post-announcement, management insiders trade.We conclude managers do not fully disclose their private information and instead message to shareholders and analysts something of opposite sign.The data suggest they may be motivated in part by subsequent personal stock-trading opportunities.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".