The foreign practice of large merger & acquisitions transactions in the stock market sector of the oil and gas industry
Bibliographic record
Abstract
Subject. This article analyzes 12 major M&A deals in the stock market 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, PJSC Gazprom, PJSC NK Rosneft and PJSC LUKOIL. Objectives. The article aims to examine the terms of M&A deals in the oil and gas stock market sector, as well as analyze the change and determine the current level of premium for control over equity capital in relation to market capitalization, and evaluate the consequences of concluded deals for the market capitalization of companies. Methods. For the study, I used the methods of comparative, financial and economic analyses, summarizing financial reporting data. Results. The article finds that market capitalization is the main benchmark for mergers and acquisitions in the stock market sector of the oil and gas industry, but the difference between the market valuation of assets and liabilities can also be used. An increase in the level of premium for capital control is noted in the industry, which exceeded half of the market capitalization. Thus, the least acceptable combination of conditions for entering into agreements is the totality of factors, such as high oil prices, commensurate capitalization of companies, cash compensation for equity capital and a high premium for control. On the contrary, transactions that took place during a period of low oil quotations with compensation in the form of shares had the most favorable effect on subsequent market capitalization. Conclusions and Relevance. It is necessary to pay close attention to the terms of mergers and acquisitions, in particular the control premium, as well as carefully calculate all the possible consequences of agreements concluded in the stock market sector of the oil and gas industry. The findings can help appraise the value of oil and gas assets as part of a comparative approach and decide on actions for raising the market capitalization of publicly traded oil and gas corporations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".