MétaCan
Menu
Back to cohort
Record W4328024679 · doi:10.5267/j.uscm.2023.2.005

The effect of capital and liquidity risks on financial performance: An empirical examination on banking industry

2023· article· en· W4328024679 on OpenAlexvenueno aff
Ruaa Binsaddig, Anis Ali, Basel J. A. Ali, Talal Al Alkawi

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMarket liquidityBusinessFinancial ratioFinancePanel dataLiquidity riskFinancial systemFinancial analysisReturn on equityEquity (law)VariablesEconomicsProfitability indexEconometrics

Abstract

fetched live from OpenAlex

The present study's primary goal is to examine selected financial risks and financial performance of commercial banks listed on the Bahrain Bourse from 2014 to 2021. However, as independent factors, chosen financial hazards include capital risk, liquidity, and bank size as a control variable, while financial performance as a dependent variable is assessed by return on equity. The panel regression analysis of data technique was used to attain the study goal. Whereas the statistics for the banks were gathered from their annual financial reports. A fascinating conclusion was the discovery of strong correlations between capital risks, bank size, and financial performance. The findings also revealed a negligible link between liquidity concerns and financial success. Due to the limitations of the present study, several ideas for future research may be suggested, such as performing research on other financial hazards, other financial institutions, and other financial performance metrics that are not included in the current research.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.274
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueUncertain Supply Chain ManagementSame topicIslamic Finance and Banking StudiesFrench-language works237,207