Mergers and Acquisitions (M&AS) by R&D Intensive Firms
Bibliographic record
Abstract
In this study, we evaluate the impact of R&D intensity on acquiring firms’ abnormal returns by examining 925 Canadian completed deals between 1993 and 2002 that have information on R&D expenditures. While examining the returns to acquiring firm shareholders in the R&D intensive firms we evaluate two competing hypotheses: ‘growth potential hypothesis’ and ‘integration failure hypothesis’. According to the ‘growth potential hypothesis’, in light of the growth potential of the targets acquired by R&D intensive firms, investors are likely to react positively. ‘Integration failure hypothesis’ focuses on integration difficulties of a target by an R&D intensive firms and suggests that investor might be skeptical of such acquisitions and react negatively. Our results show that R&D intensity (i.e. R&D expenditure by sales) has a positive and significant effect on cumulative abnormal returns of the acquiring firms around the announcement dates. This implies that market generally favors the M&A deals by R&D intensive firms. An analysis of the differentiating characteristics reveal that R&D firms have a significantly higher growth potential and undertake more stock financed deals compared to the non R&D firms. Further, our results show that there is no significant change in long-term operating performance subsequent to the M&A deals for both R&D firms and non R&D firms. In general, our results show support for ‘growth potential hypothesis’.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".