Bibliographic record
Abstract
Global acquisitions were worth $6.5 billion in the first quarter of 2008, were down 44% from the previous quarter, and down 41% from the first quarter of 2007.1 But the value of strategic M&As dropped less, only 33%, while the number of deals rose 8%, and cross-border deals represented 38% of the total value of global M&As. Kroll argues that strategic M&As, while likely to experience some drop in volume in 2008 and until the credit crisis bottoms out, will withstand the pressures of financial markets much better than financial M&As. Strategic M&As are deals conducted by companies that want to integrate strategic and complementary additions to their firm’s existing competencies. Financial acquisitions are deals done by private equity or venture capital investors who are looking for portfolio picks that will bring a high return in a relatively short period of time. While the overall volume of M&A deals trended downward through 2007, strategic M&As took less of a hit and Asia was the only region that demonstrated an increase.2 In this article, we try to explain the reasons for this, including the current downturn in M&A activity, M&A trends today, success factors in financial M&As and then strategic M&As, and an update on IPO activity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".