The Concept of Fair Pricing in the Regulation Framework of the Russian Securities Market
Bibliographic record
Abstract
High risks and volatility in the stock market of Russia hinder the transformation of savings into investments and increase the speculative nature of transactions. Russian business does not use the securities market mechanism of financing investments widely enough. One of the causes of this is the faults in the model of securities market regulation in Russia and the legislative gaps. The aim of this article is developing suggestions concerning the improvement of government regulatory practice and self-regulation in securities market in Russia. The study is based on the methods of institutional, comparative and graphic analysis. Solving the problems connected with the set aim, the authors were the first to suggest the analysis of the relationship between the ability of the Russian capital markets to implement its allocative functions on the basis of fair pricing and the efficiency of the capital markets regulatory system. A relationship has been revealed between the speculative character of the Russian securities market and the faults in the model of its regulation. Suggestions have been grounded on the improvement of the existing rules and regulation in the Russian securities market, working out the foundations of long-term public policy for securities market regulation.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".