Market Misreaction? Evidence from Cross-Border Acquisitions
Bibliographic record
Abstract
Our goal in this paper is to answer this research question: Do investors understand the longer-term value-implications of cross border mergers and acquisitions, as at the time of their announcements? We examine acquirers’ operating efficiencies around and after cross-border acquisitions and relate this to the announcement-period stock-market reaction. Using a dataset of cross-border mergers and acquisitions (M&A) entailing U.S. acquirers over the period 1990–2013, and using a bootstrapped-DEA (Data Envelopment Analysis) model because any one indicator may not reflect the whole performance of the merger, we find that the operating efficiency of the acquirers decreases around the acquisition, and up to three years after. However, we document evidence of stock market mis-reaction at announcement: the announcement-period acquirer abnormal stock-price return is not significantly associated with acquirer’s operating efficiency post-acquisition. Therefore, investors should be careful interpreting the announcement-period stock-price reaction in cross-border mergers and acquisitions as indicative of merger efficiency gains.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".