The Efficiency of Weekly Option Prices around Earnings Announcements
Bibliographic record
Abstract
This study examines the efficiency of weekly option prices around firms’ earnings announcements. With most of the largest firms now having options that expire on a weekly basis, option traders can hedge or speculate on earnings news using options that expire very close to a firm’s earnings announcement date. For earnings announcements near an options expiration date, one can estimate a firm’s expected stock price move in response to its earnings news (i.e., its option implied earnings announcement move) as the price of its at-the-money straddle as a proportion of its stock price. This study tests whether differences between historical earnings announcement moves and option implied earnings announcement moves predict straddle returns. Through the analysis of portfolio returns and Fama–MacBeth regressions, this study finds that straddle returns are significantly higher (lower) when the historical earnings announcement move is high (low) relative to the option implied earnings announcement move. In contrast to prior research, this study does not find an association between straddle returns and historical volatility, historical earnings announcement volatility, implied volatility, or the difference between historical volatility and implied volatility. Overall, this study suggests that weekly straddle prices around earnings announcements are not optimally efficient.
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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 | 0.004 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".