Sensitivity of stock indices to global events: the perspective for Pakistani Canadians
Bibliographic record
Abstract
Purpose This paper compares the performance and volatility of the Toronto Stock Exchange in Canada and the Karachi Stock Exchange in Pakistan, as well as the sensitivities of the two stock exchanges to major global events. The purpose of this paper is to assist the Pakistani immigrants in Canada in their investment decisions. Design/methodology/approach This paper uses the generalized autoregressive conditional heteroskedasticity model to estimate volatility of the two stock exchanges. Moreover, the mean adjusted returns approach associated with the event study methodology is used to find out the impact of major global events on these stock exchanges. Findings The study finds that the Toronto Stock Exchange outperforms the Karachi Stock Exchange in the pre-September 11 attack period, while the latter outperforms the former in the post-September 11 attack period. The study also shows that there has been a significant improvement in the risk-adjusted return of the Karachi Stock Exchange in the post-September 11 attack period. Moreover, this paper finds that the impact of major global events is more significant on the Toronto Stock Exchange relative to the Karachi Stock Exchange on the event date. Originality/value This paper is one of the very few to analyze and compare stock performances from the perspective of immigrant communities. The paper is valuable for Pakistani immigrants living in Canada or any investors interested in Karachi Stock Exchange and its comparison with Toronto Stock Exchange. Moreover, the paper can be of value to the Pakistani Government in terms of their promotional activities.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".