Three Major Crises and Asian Emerging Market Informational Efficiency: A Case of Pakistan Stock Exchange-100 Index
Bibliographic record
Abstract
Periods of economic turmoil distort the ability of stock prices to reflect the available information. In the last three decades, emerging markets experienced numerous crises. The major three of them are the Asian Financial Crisis (1997–1998), Global Financial Crisis (2007–2009) and Global Pandemic Crisis (2020–2022). The nature, intensity and duration of these crises differ significantly. This study investigates the impact of these varying natures of crises on the level of informational efficiency. The empirical evidence is based on the emerging stock market of Pakistan. Index-level data are collected from Pakistan Stock Exchange-100 Index for the period 1995–2022. The rebalancing is done each year to ensure that the final sample is composed of only 100 stocks with the highest market capitalization. The results based on the Variance Ratio (VR) test show that informational efficiency is time-varying. Among all the three crises, informational efficiency deters more in the COVID-19 pandemic, albeit the market efficiency recovers soon. This implies that the arbitrage opportunity is marginal in crisis periods, while investors prefer to invest in post-crisis periods. Finally, our results reveal that among all the crises, investors were more informed in the Global Financial Crisis. Investors must keep a close eye on market regimes for designing investment solutions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".