Do Country ETFs Influence Foreign Stock Market Index? Evidence from India ETFs
Bibliographic record
Abstract
We examine the influence of country exchange traded funds (ETFs) on the country’s stock market indices, irrespective of their underlying benchmark. A pooled ordinary least square (OLS) analysis of a sample of 28 India ETFs listed in the US, UK, Canada, France, Japan, Israel and Singapore reveals that India ETFs have a significant impact on the country’s stock indices. We also document reverse causal dynamics between country ETFs and the country’s stock indices. The results are robust even after controlling for global effects, stock market volatility, foreign institutional investor (FII) flows, foreign exchange rate and asset size of India ETFs. The findings of the study have implications for global investors and policymakers in both emerging and developed markets. Policymakers would find it compelling to monitor country ETFs’ fund flows into the underlying country, as withdrawal of country ETFs could have a cascading effect on the economy. JEL Classification: G11, G15, G23
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".