Stock Market Reforms and Stock Market Performance
Bibliographic record
Abstract
This paper analyses the impact of stock market reforms on the stock market performance in India using regression based event-study method. We consider nine stock market reforms introduced from 1998 to 2018. We find that the impact of stock market reforms on Nifty trading volume and Nifty return is different. This paper documents that the impact of the additional volatility measures, T+3 and T+2 settlement cycles, and margin provisions for intra-day crystallized losses reforms show a positive impact on trading volume post-reform. In contrast, internet trading, prohibition of fraudulent and unfair trade practices, delisting of equity shares, substantial acquisition of shares and takeovers listing obligations and disclosure requirements reforms decrease the trading volume post-reform. Our results of Nifty return reveal that the additional volatility measures, the T+2 settlement cycle, the prohibition of fraudulent and unfair trade practices, substantial acquisition of shares and takeovers, listing obligations and disclosure requirements have a significant and positive impact on return post-reform. It is evident that the impact of all nine stock market reforms is insignificant on Nifty return.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".