India : Role of Self-Regulatory Organizations in Securities Market Regulation
Bibliographic record
Abstract
This Note identifies four main \n theoretical options for securities markets self-regulation \n in India, based on precedents from international markets. \n There is no single 'right' approach - the note \n outlines three options that employ Self Regulatory \n Organizations (SROs), which could be viable solutions for \n Indian capital markets. These are: (1) Restructure the \n existing Exchange SRO system to create a joint SRO \n subsidiary of the National Stock Exchange (NSE) and the \n Bombay Stock Exchange (BSE). The new entity would be \n responsible for both market and member regulation. The \n Exchanges provide an existing platform for SRO functions \n that works reasonably well. This platform includes a \n governance structure, professional management, experienced \n staff, documented programs and procedures, and IT tools. But \n this option raises all of the issues on conflicts of \n interest at Exchange SROs. (2) Hybrid structure: NSE and BSE \n retain responsibility for market regulation, and create a \n new independent SRO for member regulation. This option is \n very similar to Association of National Stock Exchange \n Members of India's (ANMI's) proposal to create a \n member-based SRO (but the SRO should not be based on a trade \n association because of the significant conflicts of \n interest). It is based on the idea that supervising members \n is best done by a central regulator, but that regulating its \n own market is essential to an Exchange's market quality \n and brand. (3) New central independent SRO for both market \n and member regulation. A single independent SRO is \n theoretically the cleanest and efficient solution. But it is \n difficult to develop and in fact is only now in the process \n of being implemented in the USA and Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".