Unmasking Short-Term Wealth Effects of M&A Deals in India: A Multi-Model Analysis
Bibliographic record
Abstract
This study analyzes the short-term capital market wealth effects of acquiring companies in India. The study has taken 449 cases of merger and acquisition announcement effects on shareholder wealth by using multiple models, including the market model, CAPM, and matched firm analysis. This study documents that the acquiring firm generates a positive and significant return in the pre-announcement period, suggesting possible market anticipation or possible market reaction, and that the acquiring firm tends to be negative in the post-announcement period. We also find that shareholder wealth is eroded by acquiring firms during the announcement period. These results are consistent with agency theory, which explains how managerial motivations and information asymmetries contribute to the observed erosion of shareholder wealth around M&A announcements, and signaling theory, which suggests that market reactions reflect investors’ interpretations of the quality of the signals. The results of this study point towards improving transparency and compliance standards in the case of Indian M&As, which can help in preventing speculative trading and information asymmetry, which can skew market reactions. The results also highlight the importance of adopting rigorous due diligence and enhanced transparency procedures by firms regarding the strategic rationale for mergers, which could help mitigate negative post-announcement returns and market skepticism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".