Shareholder Value of the Company and Financial Statements: Econometric Estimation of Value Creation Drivers
Bibliographic record
Abstract
In this work to evaluate the relationship between financial reporting indicators and shareholder value on the example of the Russian companies from seven sectors of the economy linear multiple regression model and classical least squares methods has been used. The results depict that it is expected that financial reporting indicators are one of the dominant determinants of evaluating the effectiveness of financial investment decisions. Also, it is shown that the financial drivers-financial leverage, return on assets, dividend payments, and the EVA driver – invested capital-are positively correlated with the company's shareholder value. The results represent that the size of a company has a positive impact on its shareholder value. It was found that the level of disclosure is negatively correlated with the company's shareholder value. Due to the fact that the article uses data from the financial statements of the 85 Russian companies for 2018 to measure the relationship between three groups of drivers and the company's shareholder value is an innovative work that can be used in scientific and practical activities by owners and investors of companies in order to improve the financial reporting of companies and make investment decisions.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".