Main approaches to estimating the cost of shareholder equity of oil and gas producers
Bibliographic record
Abstract
Subject. The study analyzes generally accepted approaches to assessing the value of companies on the basis of financial statement data of 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, Gazprom, Rosneft, LUKOIL, and others, for 1999—2018. Objectives. The aim is to determine the specifics of using the methods of cost, DFC, and comparative approaches to assessing the value of share capital of oil and gas companies. Methods. The study employs methods of statistical analysis and generalization of materials of scientific articles and official annual reports on the results of financial and economic activities of the largest public oil and gas corporations. Results. Based on the results of a comprehensive analysis, I identified advantages and disadvantages of standard approaches to assessing the value of oil and gas producers. Conclusions. The paper describes pros and cons of the said approaches. For instance, the cost approach is acceptable for assessing the minimum cost of small companies in the industry. The DFC-based approach complicates the reliability of medium-term forecasts for oil prices due to fluctuations in oil prices inherent in the industry, on which the net profit and free cash flow of companies depend to a large extent. The comparative approach enables to quickly determine the range of possible value of the corporation based on transactions data and current market situation.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".