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Record W3037416709 · doi:10.3390/jrfm13060137

Life after Debt: The Effects of Overleveraging on Conventional and Islamic Banks

2020· article· en· W3037416709 on OpenAlexvenueno aff
Samar Issa

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIslamDebtFinancial systemFinancial crisisContext (archaeology)SAFERBusinessAsset (computer security)EconomicsFinanceMacroeconomics

Abstract

fetched live from OpenAlex

It is generally argued that Islamic banks are safer than conventional banks. The prime reason is that their product structure is essentially asset-backed financing, while conventional banks rely heavily on leveraging, which was considered one of the main causes of the 2008 global financial crisis. This paper examines the riskiness of Islamic and conventional banks during the 2008 global crisis by measuring overleveraging, defined as the difference between actual and optimal debt. This research conducted empirical analysis on the overleveraging of 20 banks (10 conventional and 10 Islamic banks) from five different countries, namely, Bahrain, Kuwait, Malaysia, the United States, and the United Kingdom. The analysis is double-folded: on the one hand, the results in this paper suggest that excess debt, rather than the mere holding of debt, was the reason behind the severe financial meltdown in 2007–2009; on the other hand, this paper shows that Islamic banks, in most of the countries in context, performed better during the recent crisis, but were subject to the second-round effect of the global crisis around the years of 2011–2013.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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