The Foreign Practice of Large Merger & Acquisitions in the Public Sector of the Oil and Gas Industry
Bibliographic record
Abstract
Subject. The article analyzes 12 major M&A deals in the public sector of the oil and gas industry in 2000–2019. Industry indicators are measured on the basis of data from ExxonMobil, Chevron, ConocoPhillips, Occidental Petroleum, Devon Energy, Anadarko Petroleum, EOG Resources, Apache, Marathon Oil, Imperial Oil, Suncor Energy, Husky Energy, Canadian Natural Resources, Royal Dutch Shell, BP, TOTAL, Eni, Equinor (Statoil), PetroChina, Sinopec, CNOOC, Petrobras, Gasprom, Rosneft Oil Company and LUKOIL. Objectives. I study on what terms M&A are concluded in the public sector of the oil industry and analyze the approval and changes in the current premium for control over the ratio of share capital to market capitalization. The article also evaluates how the above deals influenced the market capitalization of companies. Methods. The study employs methods of statistical analysis and summarizing official annual reports on financial and business performance and news releases of major State-owned oil and gas corporations. Results. Having analyzed 12 major M&A in the public sector of the oil and gas industry comprehensively, I traced trends in terms on which such deals are concluded and determined their consequences. Conclusions and Relevance. In the public sector of the oil and gas industry, M&A are found to depend on capitalization, but also sometimes refer to the difference between the market value of assets and liabilities. In the industry, share capital control premium is noted to grow, thus exceeding half of capitalization. Therefore, the least acceptable factors include a combination of high oil prices, commensuration of companies’ capitalization, compensation for share capital and high control premium. On the contrary, market capitalization significantly improved in case of deals implying the compensation with stocks, which took placed during low oil prices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".