International economic policy uncertainty and stock market returns of Bangladesh: evidence from linear and nonlinear model
Bibliographic record
Abstract
This paper explores the relationship between international economic policy uncertainty (EPU) and stock market return of Bangladesh. The study considers economic policy uncertainty of six big trading partners of Bangladesh: US, Canada, EU, China, Russia, and India. We apply time-varying linear (Break-least Square) and non-linear (Markov-Switching) regression approaches by using monthly data from January 2003 to April 2019. Our findings indicate the following. Firstly, The break-least square captured four structural breaks in the capital market of Bangladesh. Secondly, economic policy uncertainty from major importing countries (China and India) affect stock market returns of Bangladesh more significantly than major exporting countries (US and EU). Thirdly, EPU has a greater negative influence on stock returns during high volatility than low volatility regime. A number of policy measures have been recommended.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".