Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it