M&A deal initiation: the case of the unwelcome suitor
Bibliographic record
Abstract
Purpose Within the context of mergers and acquisitions, the purpose of this paper is to clarify the relationship between the deal initiator and various outcomes of the deal, particularly in consideration of the cash position of the acquiring firm. Design/methodology/approach Using hand-collected deal initiation data from various filings on the Securities Exchange Commission EDGAR online database, this paper performs a series of event study analyses, multivariate analyses, a Heckman two-step estimation procedure, and an instrumental variable approach to examine merger outcomes. Findings This paper finds that many merger and acquisition (M&A) outcomes (target and acquirer announcement returns, acquirer long-run returns, premiums, and the method of payment) are significantly related to deal initiation, particularly in consideration of the cash position of the acquiring firm. Overall, evidence is seen as consistent with the theory that “lemons” selectively approach cash-rich acquirers, often to the acquirers’ detriment. Originality/value This paper finds that target-initiated deals are not necessarily associated with poorer transaction outcomes for targets as contemporaneous studies suggest, and presents the first empirical evidence of M&A outcomes related to the deal initiator which are dependent on the cash position of the acquiring firm.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".