Insider trading surrounding securities class action litigation and settlement announcements
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the trading patterns of corporate insiders, both managing and non-managing, around the announcement dates of securities class action lawsuits and related legal settlements. Design/methodology/approach The authors use market model event study methodology to examine the impact of class action litigation and settlement announcements on the stock prices of sued firms. The authors then determine the extent of abnormal insider trading surrounding such announcements by comparing insider trading activity (volume and transaction counts) to prior insider trading in the same firm, and to a matched sample of firms not experiencing such litigation announcements. A multivariate framework is utilized to provide further insight into the determinants of such abnormal insider trading. Findings The authors establish that class action litigation and settlement announcements have a significant impact on the stock prices of sued firms, and that foreknowledge of these events appears to be used by insiders to earn abnormal profits. Moreover, results indicate that managing insiders exhibit higher opportunistic abnormal trading activity than non-managing insiders. Multivariate analysis shows that size, prior firm returns, and the implementation of the Sarbanes-Oxley Act are important determinants of such insider trading. Originality/value This appears to be the first paper to analyze insider trading surrounding class action settlement announcements, and raises concerns about the ethical conduct of certain insider groups while highlighting the importance of access to private information, even amongst insiders themselves.
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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 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".