Impact of Geopolitical Risks on the Stock Market : Evidence from Large, Mid, and Small-Cap Stocks
Bibliographic record
Abstract
Purpose : This paper studied the impact of geopolitical risks (GPR) in the top 10 economies on India's large-cap, mid-cap, and small-cap stocks. Methodology : For the investigation, we employed the quantile regression technique using a monthly dataset from January 2004 to December 2020. Findings : We discovered some extremely interesting and valuable outcomes. First, geopolitical risk threats (GPRT) and China had a significant impact; additionally, GPR, geopolitical risks acts (GPRA), France, Germany, India, and the UK had a considerable impact; Canada, Japan, South Korea, and the USA had negligible impacts; and Italy had no impact on large-cap and mid-cap stocks. Second, GPRT and China had a significant impact; GPR, GPRA, India, and the United Kingdom had a considerable impact; and Canada, France, and Germany had a minimal impact on small-cap stocks. The aggregate impact of global and country-specific GPR on Indian LMS stocks was not homogeneous, showing that GPR in these economies did not uniformly influence Indian LMS stocks. Practical Implications : Our findings could assist investors in identifying market patterns, managing portfolio risk, anticipating probable stock market changes, and adjusting their investing plan accordingly. By implementing our results into their investing strategy, investors might be able to earn higher returns or minimize risk, and improve their total investment performance. Originality : As far as we know, this is the first study that looked at the impact of GPR in the top 10 economies on Indian large-cap, mid-cap, and small-cap equities.
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.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".