Does Bangladesh Need to be Established Derivatives Markets
Bibliographic record
Abstract
Derivatives are very cardinal agreement, not just for the purpose of investors but for the overall economies. They excellently affect the general execution of a nation's economy and along these lines the global economy. The world derivative markets are gigantic and it has developed widely in the last decades in both developed economy and rising economy. Derivative markets protect bonds, currency, equity, and short-term interest rate assets from various risks. Bangladesh is a developing country it has an emerging economy. It is necessary to establish derivative market in Bangladesh. In our article, we discuss about theoretical framework such as different derivative products, benefits, and limitations of derivative markets. We also analyze six countries (Australia, Canada, Hong Kong, India, Japan, and US) derivative annual turnover. We see both exchange-traded derivative turnover and over-the-counter turnover. We find that every countries annual turnover increase in present year as compared to previous year. In the last part of our study, we are recommending some essential requirements sequentially to establish derivative market in Bangladesh.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".