The Impact of Self-Sufficiency in Basic Raw Materials of Metallurgical Companies on Required Return and Capitalization: The Case of Russia
Bibliographic record
Abstract
This article considers the impact of self-sufficiency in basic raw materials on the level of systematic risk, required return and capitalization on the example of Russian ferrous metallurgy companies. The methods applied include classical approaches to determining beta coefficient, required return and capitalization, as well as correlation–regression analysis performed in the Python programming language (version 3.0, libraries: Numpy, Pandas, Matplotlib, Datetime, Statistics, Scipy, Bambi). The study revealed an inverse relationship between the self-sufficiency of ferrous metallurgy companies in iron ore and coking coal and their systematic risk. That was confirmed by the developed regression model. The presence of this dependence directly indicates the need to consider self-sufficiency when assessing a company’s required return and capitalization. The acquisition of the Tikhov coal mine by PJSC Magnitogorsk Iron and Steel Works (MMK) led to an increase in capitalization not only due to additional profit from the new asset, but also due to a decrease in the required return caused by the growth of the company’s self-sufficiency in coking coal. The proposed approach contributes to a more accurate assessment of the company’s capitalization and creates additional incentives for vertical integration transactions.
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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 | 0.001 | 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".