Informed Options Trading Prior to Takeover Announcements: Insider Trading?
Bibliographic record
Abstract
We quantify the pervasiveness of informed trading activity in target companies’ equity options before the announcements of 1,859 U.S. takeovers between 1996 and 2012. About 25% of all takeovers have positive abnormal volumes, which are greater for short-dated, out-of-the-money calls, consistent with bullish directional trading before the announcement. Over half of this abnormal activity is unlikely due to speculation, news and rumors, trading by corporate insiders, leakage in the stock market, deal predictability, or beneficial ownership filings by activist investors. We also examine the characteristics of option trades litigated by the Securities and Exchange Commission (SEC) for alleged illegal insider trading. Although the characteristics of such trades closely resemble the patterns of abnormal option volume in the U.S. takeover sample, we find that the SEC litigates only about 8% of all deals in it. This paper was accepted by Lauren Cohen, finance.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".