Takeover premiums and the markup pricing puzzle∗
Bibliographic record
Abstract
Pre-offer target stock price runups are traditionally viewed as a consequence of market an-ticipation of the pending bid. Under this view, the runup should not impact the bid process. However, based on linear cross-sectional projections of offer premiums on runups, Schwert (1996) concludes that a dollar increase in the runup is followed by a dollar markup of the premium (dubbed ”markup pricing”). Since markup pricing means ”paying twice ” if the target runup is in fact driven by rational market anticipation, his conclusion is puzzling. We resolve the puzzle by first proving that market anticipation implies a highly non-linear relation between expected premiums, markups and runups. We then present strong evidence of this non-linearity, which rejects markup pricing. Rational market anticipation further implies that bidder takeover gains will be increasing in the target runup, which our evidence also supports. Finally, we study bid-der open market purchases of target shares during the runup period. Such ”toehold ” purchases reduce offer premiums, further contradicting the existence of markup pricing.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".