Interpreting Equivocal Signals: Market Reaction to Specific-Purpose Poison Pill Adoption
Bibliographic record
Abstract
Signaling theory suggests that firms send signals to stakeholders to reduce information asymmetry. Research, however, has rarely examined how investors interpret signals that are equivocal. We suggest that sensemaking serves as an important process by which investors interpret firm signals, and salient contextual cues influence the sensemaking process. We examine an equivocal signal, the adoption of a poison pill, as a means of examining investor interpretation of the signal and the role of contextual cues in influencing interpretation. Using a sample of 578 poison pill adoptions and controlling for self-selection, we find that investors react negatively to poison pills adopted to protect net operating losses (NOL poison pills) but positively to poison pills adopted when the firm is in receipt of a takeover offer (in-play poison pills). Assessing the role of contextual cues, our results suggest that CEO duality, the proportion of inside directors on the firm’s board, the firm’s R&D investments, and industry concentration also condition investor response to specific-purpose poison pill adoption. Our study contributes to research on signaling theory, sensemaking, and corporate governance by examining how investors interpret a firm’s equivocal governance decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".