Merger Announcements, Financial Performance and Stock Price: A Test of Market Efficiency
Bibliographic record
Abstract
Can investors earn above-normal risk-adjusted returns by acting on public information defined by merger announcements? This study tests the effect of a sample of 14 merger announcements on stock price returns using the risk-adjusted event-study methodology. Results show that an investor is not able to make abovenormal risk-adjusted returns on the announcement of mergers in support of semi-strong form market efficiency. Merger announcements stimulate significant positive returns around the merger announcement. Results show market over- and under-reaction around the merger announcement well documented in the behavioral finance literature. The evidence shows a significant stock price return reaction up to 1 day prior to the announcement consistent with the existence of insider trading (Ross and others, 2016). Do mergers strengthen companies’ financial performance? Results show that mergers are not value-increasing based on the pre-post-merger financial performance in support of the agency problem where large firm use excess free cash flow to get “bigger” not “better” by going shopping for other firms. In such cases, the firm’s merger maximizes size, not stockholder wealth, the goal of the firm.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".