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Record W2074311585 · doi:10.5539/ijef.v5n3p152

Can Financial Ratios Reliably Measure the Performance of Banks in Bahrain?

2013· article· en· W2074311585 on OpenAlexvenueno aff
Naser Jamil Najjar

Bibliographic record

VenueInternational Journal of Economics and Finance · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial ratioBusinessProfitability indexFinancial crisisFinancial analysisMarket liquidityFinanceFinancial systemEquity (law)AccountingEconomics

Abstract

fetched live from OpenAlex

The aim of this study is to analyze the financial performance of major banks in Bahrain. This study covers the calculation of important financial ratios of major financial institutions in Bahrain as well as comparing their performance in the context of the global financial crisis. It also compares ratios of conventional banks with Islamic financial institutions in Bahrain. These ratios define profitability, financial performance, size and type of banks. The analysis of ratios shows the differences in financial management practices of banks in the respective areas. The study reveals that there are wide differences in the ratios used by different banks, especially before and after the financial crisis. This study helps identify best practice in the areas of profitability management, liquidity management, and interest rate risk management. The result of the analysis of ratios for measuring financial performance shows that there is corporate excellence in asset management and value equity shares. This analysis can be used as a basis for preventative actions for future bankruptcy and market risk. The components in financial statements for Islamic banks differ from conventional banks. The study recommends that banking institutions in Bahrain should use this ratio analysis to prevent unpredicted financial problems and take corrective measures or provisions to avoid such events for financial institutions.

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 score0.719
Threshold uncertainty score0.224

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 communication0.0000.001
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 head0.008
GPT teacher head0.181
Teacher spread0.174 · 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

Citations20
Published2013
Admission routes1
Has abstractyes

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