When the Poor Buy the Rich: New Evidence on Wealth Effects of Cross-Border Acquisitions
Bibliographic record
Abstract
The growing trend of merging and acquisition (M&A) investments from emerging to developed market economies over the last two decades motivates the question on the long-run effects of M&A on the wealth of emerging markets. This paper contributes to the current literature on cross-border M&A (CBMA) by focusing on the long-term effects of this event on the bidder’s stock return in emerging markets. To address the challenges of finding an accurate measure for the effects, this study applies the propensity score matching framework in tandem with difference-in-differences (DID) on a comprehensive dataset over the 1990–2010 period. The analyses show evidence of systematic detrimental impacts of cross-border M&A on shareholders’ welfare in the long run, to a certain extent, diverging from the existing literature, which mainly highlights the positive effects for certain types of M&A. The striking finding is that such strong negative effects remain persistent even when various factors previously known as capable of suppressing underperformance are considered. Our study is in line with the growing landscape of cross-border mergers and acquisitions from the “poor” to the “rich” countries.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".