Impact of economic policy uncertainty on Indian stock market returns: evidence from large-cap, mid-cap and small-cap stocks
Bibliographic record
Abstract
This study examines the impact of economic policy uncertainty (EPU) in the top 10 economies on the large-cap, mid-cap and small-cap (LMS) stock returns in India using a monthly dataset from January 2004 to May 2021. Our outcomes based on the quantile regression approach unveil interesting findings. First, Canada, France, Japan, the UK and the USA do not affect large, mid and small-cap stock returns. Second, Germany, India and Korea are the only countries that have a substantial and negative impact on LMS stock returns. Third, large and mid-cap stock returns are influenced positively only by Chinese EPU. The impact of the top 10 countries EPU is not homogeneous across LMS stock returns in India, implying that uncertainty regarding economic policy in these economies does not uniformly influence Indian LMS stock. The results derived from our study would be of substantial utility for investors, portfolio managers and policymakers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".