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Record W3020549677 · doi:10.1142/s0219091520500022

Risk-Reward Trade-Off and Volatility Performance of Islamic Versus Conventional Stock Indices: Global Evidence

2020· article· en· W3020549677 on OpenAlexaff
Ahmad Abu-Alkheil, Walayet A. Khan, Bhavik Parikh

Bibliographic record

VenueReview of Pacific Basin Financial Markets and Policies · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsSharpe ratioVolatility (finance)EconomicsStock (firearms)Treynor ratioEconometricsAutoregressive conditional heteroskedasticityFinancial economicsStock market indexStock marketPortfolio

Abstract

fetched live from OpenAlex

In this paper, we compare the performance of Islamic stock indices (ISI) and conventional stock indices (CSI) from FTSE, DJ, MSCI, S&Ps and Jakarta series using common risk-return metrics. The sample consists of 64 ISI and CSI, and covers the period from 2002 to 2017. The majority of the stock indices are from the Pacific Rim countries’ stock markets. Additionally, we employ the GARCH-M model to examine the impact of past volatility on spot returns. Findings suggest that the ISI are less sensitive to the average market movements compared to the CSI, but surprisingly offer similar raw returns suggesting primary support for the low risk-high return paradox. On further examination, results reveal that M 2 , Omega, Sharpe and Treynor measures indicate that ISI underperform CSI while Jensen’s alpha and Sortino ratio put ISI ahead of CSI. Moreover, findings show that pre-crisis winners (CSI) were losers during the 2008 crisis but subsequently recovered and ended up with higher returns than ISI. Findings also show that the previous volatility of stock returns can be potentially used for predicting future returns.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.253
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2020
Admission routes1
Has abstractyes

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