The effects of M&As on acquirer's pre and post company performance
Bibliographic record
Abstract
Mergers and acquisitions (M&As) are a global business strategy allowing organizations to enter new potential markets or new business sectors. M&As can have a significant effect on the performance of both the participating companies. In this work, we focus on full acquisitions, and we study how the acquisition affects the performance of the acquiror company. We constraint our study to the sector of Oil &Gas and in the geographical region of North America (i.e., USA, Canada). Towards that goal, we investigated the acquirors’ performance utilizing a number of financial indices such as return on assets (ROA), return on equity (ROE), earnings per share,net profit margin, etc. We employed statistical analysis methods (i.e., test hypothesis) and regression techniques to assess the impact of these indices on the companies’ performance. Through our analysis, we found that both ROA and ROE increased, on average, after the acquisition took place, thus supporting the hypothesis that acquisition have a positive impact on the companies’ performance.Our results are in line with existing research on the subject. The thesis concludes with a discussion of advantages and potential limitations of our findings.
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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 | 0.009 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".