Does the Application of Smart Beta Strategies Enhance Portfolio Performance? The Case of Islamic Equity Investments
Bibliographic record
Abstract
Traditionally, passive portfolios are structured using an easy to implement market capitalization method albeit highly skewed towards large cap stocks. The introduction of smart beta strategies has allowed passive investors to structure equity portfolios using alternative strategies such as fundamental-weighting, equal-weighting, and low-risk weighting strategies. This paper investigates whether constrained portfolios such as Shariah-compliant equity portfolios (SCEPs) can benefit by adopting smart beta strategies. The sample consists of equities from the USA, Canada, Australia, Europe, Middle East, Indonesia, and Malaysia for the period January 2003 to December 2016. The empirical findings suggest that smart beta SCEPs outperform not only conventional market capitalization weighted portfolios but also SCEPs following a market capitalization-weighted strategy. Higher risk-adjusted returns and lower drawdown as a result of following smart beta strategies highlights the importance of considering smart beta portfolio weighting strategies for passive investors. The supremacy of smart beta strategies indicate the value proposition for investors and fund managers alike. We also found that geographical location affects the performance of smart beta SCEPs; countries with a Muslim majority report higher cardinality and lower drawdowns. The results remained robust with alternative Shariah screening guidelines and empirical estimation methodology.
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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 | 0.004 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".