Litigation and information effects on private sales of securities
Bibliographic record
Abstract
We analyze PIPE (Private Investments in Public Equity) transactions in which the issuer experienced class action lawsuits. We explain the associated information effects measured by the announcement wealth effects and the discounts. Using a comprehensive, hand-gathered dataset, we show that the more severely litigated PIPEs are associated with higher announcement wealth effects and higher levels of discounts. We find that the issuer's voluntary disclosure positively influences PIPE information effects particularly when coupled with auditor changes. We report that certain mitigation actions affect the pricing of PIPEs along with their associated wealth effects while facing ongoing litigation. We posit that confidentiality in privately negotiated securities is the key in litigated transactions as issuers efficiently share the operational details of mitigation efforts. PIPE transactions are not necessarily costlier funding venues even when securities class action lawsuits are ongoing compared to the PIPE transactions that did not experience any prior litigation action.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".