China-Pakistan Economic Corridor Agreement: Impact on Shareholders of Pakistani Firms
Bibliographic record
Abstract
This paper investigates the effects on shareholders’ wealth of firms composed of the Karachi Stock Exchange 100 index, around events leading up to the signing of the China-Pakistan Economic Corridor (CPEC) agreement. We used standard event study methodology to measure the stock price reaction of KSE 100 Index (composed of all major sectors of Pakistan economy) around three key events related to CPEC agreement. Based on average security returns and cumulative average security returns, our results show significant and positive reaction of KSE 100 Index around all three key CPEC events. Our results capture market participants’ assessment of the CPEC agreement’s impact on future growth of Pakistani companies and the resultant effect of its shareholders’ wealth. These positive wealth effects are of significant predictive value as additional bilateral and multilateral agreements are contemplated in that region. Our research contributes to a research stream that sees valuable payoffs of bilateral trade agreements for developing economies and support the argument that bilateral agreements can promote and attract institutional and private foreign direct investment (FDI), which otherwise may not be forthcoming. The argument goes on to argue that these bilateral agreements also help raise the quality of institutional framework in the developing countries.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".