Analysis of Impact of Non-financial Information Disclosure on Capitalization of Russian Oil and Gas Sector Companies
Bibliographic record
Abstract
Today, the problems and ways of improving the companies' corporate reporting and confirming their importance are among the most discussed topics in the academic world, both in Russia and globally. The existence of a wide range of research papers, as well as tools for evaluating non-financial information of companies, indicates the significant role of non-financial factors for the global society. However, it is still questioned whether these factors affect the market value of companies. According to the RSPP, the disclosure of non-financial information in the companies' annual reports allows users to identify leaders, helps to strengthen the reputation and investment attractiveness of these companies, and serves to promote the culture of responsible business conduct. In this work, the influence of non-financial factors on the market capitalization of companies in the oil and gas sector was studied using the model of correlation of factors with the calculation of the Pearson and Spearman coefficients. The data about the market capitalization of the three largest Russian companies in this sector, Gazprom, Gazprom Neft, and LUKOIL, were taken from publicly available sources. To find a correlation between the calculated indices and the market capitalization indicator, it was assumed that the company's market capitalization of the current year would be influenced by the indices of non-financial factors calculated according to the data of the previous year. It has been proved that there exists a certain connection between non-financial factors (index of ecological effectiveness; index of economic development; index of social influence) and the company's market value. However, the results of the analysis showed that political factors determine the capitalization of oil and gas companies in Russia to a greater extent at the present stage.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".