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Record W3088329331 · doi:10.5430/ijfr.v11n5p115

Risk Management and the Financial Performance of Banks in Nigeria

2020· article· en· W3088329331 on OpenAlexvenueno aff
Henry Inegbedion, Bello Deva Vincent, Eseosa Obadiaru

Bibliographic record

VenueInternational Journal of Financial Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLiquidity riskMarket liquidityBusinessCredit riskFinancial risk managementLeverage (statistics)Profitability indexFinanceCapital adequacy ratioRisk managementFinancial systemEconomicsIncentive

Abstract

fetched live from OpenAlex

The study examined “risk management and financial performance of banks in Nigeria” with focus on commercial banks. The broad objective of the study was to ascertain the effect of risk asset management on the optimal financial performance of commercial banks in Nigeria. The study is a longitudinal survey, so the ex-post facto research design was applied. Research data were analysed using generalized method of moments (GMM) and vector Error Correction Model, after testing and adjusting the data for stationarity and Cointegration.The research findings were: Banks’ profitability is significantly influenced in the short run by liquidity risk and in the long-run by credit risk, capital adequacy risk, leverage risk and liquidity risk. Furthermore, profitability measured by ROaA was found to be positively related to liquidity risk but negatively related credit risk. Arising from the findings, there is the need for effective risk management, especially credit, capital adequacy, leverage and liquidity risks, to enhance the profitability of banks. By helping to enhance the going concern of banks, risk management will help to reduce retrenchment and unemployment and hence help to forestall the attendant social vices.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
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 head0.026
GPT teacher head0.293
Teacher spread0.267 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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