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Record W1965343583 · doi:10.5430/afr.v1n2p161

The Intervening Effect of Global Financial Condition on the Determinants of Bank Performance: Evidence from Nigeria

2012· article· en· W1965343583 on OpenAlexvenueno aff
James O. Alabede

Bibliographic record

VenueAccounting and Finance Research · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial systemOrder (exchange)Financial crisisCorporationBusinessCompetition (biology)Non-performing loanDeveloping countryQuality (philosophy)EconomicsFinanceEconomic growthMacroeconomicsLoan

Abstract

fetched live from OpenAlex

The global financial crisis had devastating effect on both developed and developing economies. In Nigeria, the effect of the crisis swerve through the major sectors of the economy with the banking sector greatly affected. This study investigates the determinants of Nigerian banks’ performance from 1999 to 2010 while taking into consideration the intervening effect of global financial condition. The data of the study, which were extracted from annual reports of the banks as well as various publications of Central Bank of Nigeria and Nigerian Deposit Insurance Corporation, were treated statistically using multiple regressions. The study provides evidence indicating that in the presence of the effect of global financial condition, only assets quality and market concentration are significant determinants of the Nigerian banks’ performance. By implications, these findings suggest the need to keep nonperforming assets at minimum and introduce a policy to encourage fair competition among the banks operating in Nigeria in order to check concentration of banking services among only few banks.

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)0.002
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.328
Teacher spread0.292 · 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

Citations15
Published2012
Admission routes1
Has abstractyes

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