The Effect of Cross-Border Mergers and Acquisitions Performance on Shareholder Wealth: The Role of Advisory Services
Bibliographic record
Abstract
This study empirically examines the wealth effects of mergers and acquisitions (M&As) in the Indian capital market, focusing on cross-border M&As. This study considers a sample of 58 cross-border and 34 domestic M&As, comprising more than 50 percent of the shares acquired by the acquiring companies from 2004 to 2019. We analyzed the wealth effects of cross-border M&As by applying the event study methodology. The abnormal returns of domestic and cross-border mergers and acquisitions for various window periods were compared using an independent t-test. The wealth effects of the acquiring firm have been further investigated with the inclusion of top advisor services and without the inclusion of advisor services in mergers and acquisitions transactions. This result suggests that cross-border M&As do not create a significant positive return for shareholders. There is no considerable wealth gain for shareholders of acquiring companies in domestic and cross-border mergers and acquisitions. We also find that including top advisor services in the M&A process does not influence the acquiring firm’s wealth. The price-to-book value ratio of the acquiring firm is a significant determinant of its returns.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".