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Record W2578255987 · doi:10.5539/ijef.v9n2p180

The Bank Sector Performance and Macroeconomics Environment: Empirical Evidence in Togo

2017· article· en· W2578255987 on OpenAlexvenueno aff
Adama Combey, Apélété Togbenou

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsReturn on assetsMonetary economicsGross domestic productEconomicsReal gross domestic productReturn on equityExchange rateRate of returnInflation (cosmology)Financial systemProfitability indexMacroeconomicsFinance

Abstract

fetched live from OpenAlex

This article investigates short-run and long-run relationship between three main macroeconomic indicators (gross domestic product growth, real effective exchange rate, and inflation) and banking sector profitability (measured by return on assets and return on equity) in Togo, from 2006 to 2015, by using Pool Mean Group estimator. Results show that, in the short-run, banks’ return on assets and return on equity are not related to macroeconomic variables. But banks’ return on assets is determined positively by bank capital to assets ratio and bank size while banks’ return on equity is affected negatively by bank capital to assets ratio. However, in the long-run, real gross domestic product growth and real effective exchange rate affect negatively and statistically significant banks’ return on assets, while inflation rate has no effect. Concerning bank’s return on equity, in the long-run, results suggest that real gross domestic product growth, real effective exchange rate, and inflation affect negatively bank’s return on equity. These results imply that to stabilize bank profitability and make Togolese banking sector more resilient, policymakers and banking sector managers must, among others, try to improve real gross domestic product growth, real effective exchange rate, and inflation volatility anticipation.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.052
GPT teacher head0.258
Teacher spread0.206 · 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

Citations40
Published2017
Admission routes1
Has abstractyes

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