The Determinants of Mergers and Acquisitions in the Oil & Gas Industry: Evidence from Canadian and American Transactions
Bibliographic record
Abstract
The study investigates the determinants of mergers and acquisitions in the oil and gas industry over the ten-year period from 2002 to 2011. Our large sample analysis results indicate that in the O&G industry: (1) U.S. acquirers are larger than Canadian acquirers overall; (2) value bidders generate greater abnormal returns relative to glamour bidders in Canadian market; (3) the geographical proximity of headquarters cannot generate pronounced synergies, and even destroys penny stock bidder’s value; and (4) there is no mispricing effect in the penny stocks, but they are more illiquid and have a higher level of idiosyncratic risk. We also examine three cases in 2012-2013 to verify our results and to identify several firm specific factors that are not considered in the large sample analysis. Consistent with our expectations, the Canadian transaction is more straightforward whereas the U.S. transactions depend more on pre-existing connections between the firms and suggest more corporate governance concerns.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".