MétaCan
Menu
Back to cohort
Record W4380609365 · doi:10.58205/fber.v1i4.1619

قیاس مخاطر الاستثمار في الأسواق المالیة وتأثیرها على سلوك المستثمرین

2017· article· en· W4380609365 on OpenAlexaboutno aff
عبد الرحمان نعجة, هواري مغنیة, زولیخة بختي

Bibliographic record

VenueFinance and Business Economies Review · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIslamVolatility (finance)Islamic financeIndex (typography)HedgeActuarial scienceInvestment (military)Financial economicsBusinessEconomicsPolitical scienceLawComputer sciencePhilosophyTheology

Abstract

fetched live from OpenAlex

Abstract: This study aims to analyse investment risks nature and sources in Islamic FinancialProducts and its Ethical counterpart arising from its investing, and also to demonstrate theimpact of their performance and risks measuring on the investor's behavior. Secondly, ThePerformance analysis method is used to compare the two selected benchmarks chosen fromamong the (DJIM) family of the Canadian Stock Market, utilising daily data of the close pricesof the Islamic index (CANI) and its Ethical counterpart (CAN) covering the period from(January 2005 to December 2015). The results show that the Islamic index (CANI) outperformand was more volatility sensible than its Ethical counterpart (CAN), particularly during theperiod of the Sub-prime Crisis in 2008. Finally, the analysis of the correlation rate shows thatthere was no significant link between the studied indices and US (T-Bill), which denies theexistence of the benchmark price risk. The study affords some of recommendations amongthem: improving the competitiveness in the Islamic Financial Markets (a), by using ModernInformation Systems (b) and the independence of Shariah Boards of Islamic Banks (c), tomanage and hedge risks.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.022
GPT teacher head0.237
Teacher spread0.215 · 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.

Study designNot applicable
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueFinance and Business Economies ReviewSame topicIslamic Finance and Banking StudiesFrench-language works237,207