Ranking Investment Opportunities Across Risk-Aversion Levels: Application to Islamic and Conventional Indices
Bibliographic record
Abstract
We introduce the Reward–VaR curve, a novel framework for evaluating risk-adjusted investment performance across a range of investor risk preferences. When returns are normally distributed, the Reward–VaR curve yields the same asset ranking as the Sharpe ratio. However, when the third-order modified VaR is used, a new paradigm emerges beyond the simplistic “better/worse” ranking: if no asset dominates at all confidence levels, one becomes preferable for risk-averse investors, while the other is favored by the risk-tolerant. For empirical implementation, we incorporate bootstrapping to separate robust performance patterns from sampling noise. We apply the methodology to compare conventional equity indices and their Islamic counterparts from the S&P Dow Jones Global Index family across nine markets from 2000 to 2024: Asia-Pacific, Canada, Developed, Emerging, Europe, Japan, UK, US, and World. Our empirical results reveal market-condition dependent dominance patterns. During bull markets, conventional indices dominate in most regions, except the European and World markets, where no dominance is observed, and Japan, where the Islamic index outperforms. In bear markets, Islamic indices dominate in most regions, with the exception of Emerging Markets, where dominance is partial, and Japan, where no clear difference is observed. Over the full sample, most markets show no significant long-run dominance, except Canada and Emerging Markets, where conventional indices outperform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".