A Lightweight Note on Success in Mergers and Acquisitions
Bibliographic record
Abstract
This two-page note discusses how and when mergers and acquisitions make sense. Written in 2004, the conclusions still hold in 2025. The note contains academic citations that may be of interest to researchers.<br><br>Merger and acquisition activity is picking up. The first quarter saw the highest level of global M&A activity since 2000. It may therefore be interesting to review what we have learned about the science of M&A lately. Perhaps surprisingly, there is growing evidence that making acquisitions is one of the best and safest ways to sustain shareholder value.<br>Yet should we not have learned that M&A usually does not make sense? That most acquisitions destroy value? That deal making is prompted by CEO vanity and not by economic reality? Not necessarily. Contrary to popular opinion, most M&A deals succeed and add value to share holders and society.Conventional wisdom holds that much less than half of all mergers succeed. The facts tell a different story. This story is well known in academic circles but is at best only anecdotally known among business executives. This review summarizes the most important research findings and explains why executives pursue acquisitions. It is not because of folly, but rather because it is in the interest of their shareholders.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 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; both teacher heads agree on what is shown here.
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".