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Record W2980559434 · doi:10.5430/ijfr.v11n1p25

Mudharabah Deposits Among Conventional Bank Interest Rates, Profit-Sharing Rates, Liquidity and Inflation Rates

2019· article· en· W2980559434 on OpenAlexvenueno aff
Caturida Meiwanto Doktoralina, Fikki Mutarotun Nisha

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersUniversitas Mercu Buana
KeywordsInterest rateProfit sharingMarket liquidityEconomicsInflation (cosmology)Reserve requirementCost of funds indexBusinessMonetary economicsFinanceMonetary policyCentral bank

Abstract

fetched live from OpenAlex

This paper aims to examine the effect of conventional bank interest rates (CBIR), profit-sharing rates (PS), the level of liquidity proxied in the finance-to-deposit ratio (FDR) and the inflation rate (IR) against mudharabah deposits (MDs). The sample comes from eight Islamic public banks registered in the Financial Services Authority (OJK) and Bank Indonesia (BI) for the period from 2013 to 2017. The research uses a data panel regression analysis using EViews 8 to test the significance of tribal-level influence on conventional bank interest, profit-sharing growth rate, liquidity level and inflation rate. The results provide evidence that conventional interest rates do not affect MDs; the profit-sharing rate has a significant positive effect on MDs; the FDR has a positive effect on MDs, and the IR does not affect MDs. The results can increase our understanding of the variables that affect the volume of MDs. The results of this research have practical implications for people who will invest, giving them a better basis for making deposit and investment decisions by looking at interest rates and profit-sharing systems that are in line with Islamic investment principles that apply no uncertainty (Garar), interest (Riba) and gambling (Maisir) investments to cover all aspects of life (way of life).

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)0.002
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 score0.077
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.343
Teacher spread0.288 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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