Bibliographic record
Abstract
A major topic in financial economics, stock market volatility reflects the unpredictability, swings, and dynamic behaviour of security prices. Due to its integration with international financial systems, sensitivity to domestic macroeconomic developments, and predominance of retail investor participation, the Indian stock market—one of the biggest and fastest-growing among emerging economies—displays distinctive volatility patterns.The rate of inflation, economic crises, social and political factors, shifts in economic policy, economic indicators, and other factors are some of the causes of stock market volatility. Many measures are taken, such as margin trading, pre-open sessions, price bands, circuit breakers, etc., to reduce the impact that these factors cause. This study offers a theoretical investigation of the volatility of the Indian stock market, emphasising its causes, consequences, and connections to behavioural and macroeconomic variables.A theoretical and secondary research approach is used in this study. The study emphasises how domestic policy decisions, global shocks, sectoral movements, and investor sentiment shape volatility in the Indian context.In addition to highlighting the ways in which volatility interacts with economic fundamentals, this paper develops a broad theoretical framework that explains how volatility arises and endures in India's equity markets. It does this by drawing on theories of volatility modelling, efficient market hypothesis, behavioural finance, and global contagion perspectives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".