Portfolio diversification benefits before and during the times of COVID-19: evidence from USA
Bibliographic record
Abstract
Abstract This paper investigates the portfolio diversification benefits for Islamic and conventional investors in the USA with its major trading partners (United Kingdom, Canada, China, Japan, Malaysia, and Turkey) before and during the COVID-19 crisis period. Using daily data from 2007 to 2020, we employ three relevant time-varying and timescale-dependent techniques, the continuous wavelet transform (CWT) analysis, the wavelet multiple correlation (WMC), and the wavelet multiple cross-correlation (WMCC). The findings suggest that conventional and Islamic US investors who invest with major trading partners may reap large diversification benefits for very short investment horizons (4–8), except for Sharia Malaysia index. However, they may not reap benefits for investment horizons of 8–16 and longer, except for China. In addition, COVID-19 crisis caused a poor diversification opportunity for US investors regardless of the regime they follow (conventional or Islamic). Moreover, the American industrial market depicts a state of impending perfect market integration. Finally, the UK and Canada seem to be the potential market leaders in different wavelet scales. These findings yield important policy implications.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".