Toeholds, Bid Jumps, and Expected Payoffs in Takeovers
Why this work is in the frame
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Bibliographic record
Abstract
We estimate sequentially outcome probabilities and expected payoffs associated with first, second, and final bids in a large sample of tender offer contests. Rival bids arrive quickly and produce large bid jumps. Greater bidder toeholds (prebid ownership of target shares) reduce the probability of competition and target resistance and are associated with both lower bid premiums and lower prebid target stock price runups. The expected payoff to target shareholders is increasing in the bid premium and in the probability of competition, but decreasing in the bidder's toehold. The initial bidder's expected payoff is significantly positive in the “rival-bidder-win” outcome, in part reflecting gains from the pending toehold sale. Despite these dramatic toehold effects, only half of the initial bidders acquire toeholds.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it