Risk Adjusted Performances of Conventional and Islamic Indices
Bibliographic record
Abstract
The paper examined the risk-adjusted performance of Dow Jones and FTSE conventional and Islamic indices. The daily closing stock prices of 22 indices from January 2006 to December 2017 were selected from 11 countries comprising US, Europe, Canada, Japan, Turkey, Malaysia, China India, Qatar, Kuwait, and Taiwan. The returns of the series were first computed and then Sharpe ratio and Treynor index were used to analyze the data. It was clear that in some countries conventional indices out performed Islamic indices (US, Malaysia and Taiwan) whereas in others Islamic indices were better (EU, Kuwait, China and Qatar). The last category had inconclusive result this was because whereas the Sharpe ratio suggests a better performance of the conventional indices, on the contrary the Treynor ratio suggested that the Islamic indices performed better (Canada, Japan, Turkey and India).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 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".