Biggest Public Oil Companies: Impact of External and Internal Factors on Capitalization
Bibliographic record
Abstract
Estimate and search for factors that influence the capitalization of public oil companies are of great interest to researchers. The impact of various external and internal factors on the value of oil companies’ stocks was considered. This includes changes in oil prices, stock market index movements, inflation fluctuations, financial and production indicators. The study includes building models with calculated standard errors by the Driscoll-Kraay method based on quarterly data for the eight biggest public oil companies operating in the upstream and downstream segments, from the first quarter of 2006 to the third quarter of 2017. Such indicators as total oil production by OPEC countries, greenhouse gas emissions by companies, and the sum of shareholder’s funds owned by large institutional investors were used for the first time when building the model to identify factors affecting the market capitalization of oil companies. One of the key results is the conclusion that quarterly production volumes turned out to be the most significant factor having a positive impact on the cost of oil firms. That is, investors are laying the idea of compensating for losses from lowering the cost of oil by increasing its production and selling a larger volume in the value of shares in companies. At the same time, such indicators of production efficiency as profitability in the upstream and downstream segments lose their significance depending on the period under consideration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".