A Note on the Impact of the Canada-India Diplomatic Standoff on the Performance of Canadian Mutual Funds Investments in India
Bibliographic record
Abstract
On June 18, 2023, Mr. Hardeep Singh Nijjar, a Canadian citizen of Indian origin, was killed in Canada. On September 18, 2023, the Canadian Prime Minister, Mr. Justin Trudeau, accused the Indian Government of involvement in killing Mr. Nijjar in the Canadian Parliament. This accusation preceded and succeeded by other events, led to a significant diplomatic standoff that is still ongoing. We examine the impact of this event chain on the performance of daily returns of mutual funds based in Canada that are predominantly invested in Indian securities. Our results, controlled for general stock market returns, fund size, expense ratio, and interest rates, indicate that the events negatively impacted fund performance. Our study adds to the existing literature on the benefits of international diversification of mutual funds in disconnected markets. The findings of this paper suggest that political discord between two distinct and unrelated economies may impact profits and negate the advantages of international diversification. Our study is significant to professionals, particularly mutual fund managers, as it demonstrates the role of two countries’ political ties in affecting mutual fund returns.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 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".