Acquirers gain twice as much as targets in M&As: a different perspective on a longstanding perception
Bibliographic record
Abstract
We propose a structural event study methodology, which explicitly models the interaction of two merger and acquisition (M&A) effects: synergy (total value) and dominance (bargaining power). This interaction simultaneously determines the acquirer's and the target's observed abnormal returns around the transaction announcement. Accordingly, we propose a structural estimation approach of which estimates suggest that acquirers get twice as much gains as targets. The structural parameters are uniquely identified with the reduced forms’ coefficients. We use this feature to validate our structural approach. Moreover, the reduced forms' estimates are consistent with the M&A literature. However, the interpretation/intuition from the structural estimates offers a new perspective on how acquirers and targets share synergies. More generally, the structural approach allows testing theories and hypotheses related to M&As under an empirical framework that captures the interdependency of the parties' abnormal returns. The efficiency of the empirical procedure is higher than the efficiency of methods that overlook this interdependency.
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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 | 0.003 | 0.013 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".