Bibliographic record
Abstract
After a multi-year lull, merger and acquisition activity has started again to pick up speed. In 2004, the value of global M&A activity grew 40% to just under $2 trillion, compared to $833 billion in 2003. This is the first time the market has seen such growth since 1998 when it increased 80% from 1997. M&A activity in the post-millennium years weakened because investors were jittery after the dot.com bust and September 11 attacks in the United States. By 2005, it seemed that much of that fear had abated, as M&A activity was strong through June. There were more large deals in 2004 as well, such as Sprint Corporation’s purchase of Nextel Communications Inc. for $46 billion and J.P. Morgan Chase’s acquisition of Bank One for nearly $60 billion. But now in the last quarter of 2005, other shocks may affect business activity generally and M&A activity in particular. For example, what will be the effect of Hurricane Katrina and higher oil prices on future M&A deals? Depending on what turns the US and world economies take, the storm that crushed New Orleans and the Gulf Coast – and its ripple effects on the economy -- may reverse the M&A rebound. In this article, we discuss factors that encouraged M&A activity to bounce back, analyze why recent European M&A activity is more successful than past activity, revisit reasons CEOs pursue M&As, present reasons for cautions, review determinants of a successful merger or acquisition, make suggestions for CEOs considering M&A, and, finally, discuss possible effects of Hurricane Katrina on M&A activity in the next year or two.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".