The Impact of M&A Announcement and Financing Strategy on Stock Returns: Evidence from BRICKS Markets
Bibliographic record
Abstract
In this paper, we examine if M&A announcements and methods of financing these deals affect stock returns. Data is used for BRICKS from the period 2005-2009 and standard event study methodology is used for this purpose. We find significant pre-event returns for 5 out of 6 sample countries. This indicates possible leakages in the information system, which may not be surprising, given the emerging nature of these markets. Three of the BRICKS countries, i.e. India, South Korea and China provide significantly negative post-event returns while strong positive returns are observed in case of South Africa. The extra normal post-event returns defy semi-strong efficiency for majority of sample markets. We further find that M&A announcements do not significantly alter the trading liquidity and pricing efficiency of the sample stocks. However, return volatility does decline on post event basis. It is also observed that while stock financed mergers are value creating, cash financed mergers seem to be value destroying in the short run. The study is extremely relevant for common shareholders, global fund managers as well as financial regulators. The present research contributes to corporate restructuring as well as market efficiency literature, especially for emerging markets.
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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) | 0.003 | 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".