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Record W1982776605 · doi:10.5430/afr.v4n1p164

Assessment of Efficiency and Profitability of Listed Banks in Ghana

2015· article· en· W1982776605 on OpenAlexvenueno aff
John Kwaku Mensah Mawutor, Fred Awah

Bibliographic record

VenueAccounting and Finance Research · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexBusinessFinancial systemAccountingFinance

Abstract

fetched live from OpenAlex

The Ghanaian banking industry has had both good and bad times as far as profit is concerned. However, the industry remains good, as evidened from the rising number of other banks from the continent to either merge with the indigenous banks or take over the operation of some of the local banks. In this view this; the study assessed the efficiency and profitability of banks operating in Ghana by using listed banks between the years 2006 to 2011. This quantitative study employed panel data approach using regression analysis to assess the efficiency of banks operating on the Ghana Stock Exchange. The dependent variable is profitability, comprises of return on assets and the size of the firm. The independent variable efficiency also comprises leverage ratio, liquidity ratio, credit risk ratio and profitability ratio. The main source of data employed for this research is a secondary data. The study revealed that 60.74 percent of the variation or changes in the profitability of the banks are accounted for by the independent variables such as the liquidity level, leverage, productivity, credit risk and size of the banks. This was revealed by the coefficient of determination (R 2 ) which shows the amount of variation in the dependent variable as being explained by the independent variables

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.061
GPT teacher head0.348
Teacher spread0.287 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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