Investigating financial opportunities for traditional clothing industryin South Asia based on an analysis of internationally diversified portfoliousing ARCH and GARCH models
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the benefits of forming an internationally diversified portfolio in the stock markets of Bangladesh, India and Pakistan using the stock market indices data from April 2013 to March 2020. The portfolio comprises of three stock market indices from Pakistan, India and Bangladesh. The goal is to identify financial opportunities for traditional clothing industry in South Asia. Bangladesh, India and Pakistan are neighbouring countries in South Asia. Tradition, culture and specific ethnic elements influence traditional clothing in the case of the selected country cluster consisting of Bangladesh, India and Pakistan. Our empirical results indicate that internationally diversified portfolio does not reduce the risk due to global market integration in the background. Furthermore, ARCH and GARCH models reveal that large change in conditional variance is followed by large changes in conditional variance whereas small change in conditional variance is followed by small changes in conditional variance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it