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Record W2984122860 · doi:10.5539/ijef.v11n12p15

Exchange Rate fluctuations and Financial Performance of Banks: Evidence from Sudan

2019· article· en· W2984122860 on OpenAlexvenueno aff
Nawal Hussein Abbas Elhussein, Osama Eltayeb Elfaki Osman

Bibliographic record

VenueInternational Journal of Economics and Finance · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurrencyDistributed lagExchange rateOrdinary least squaresPopulationEconomicsForeign direct investmentMonetary economicsBusinessFinancial systemEconometricsMacroeconomics

Abstract

fetched live from OpenAlex

This paper aims at investigating whether fluctuations in the exchange rate affect the financial performance of Sudanese banks and detecting the direction of the causal relationship relation between exchange rate and banks’ performance. The study targets a total population of 37 working banks in Sudan and covers the period 2002-2017. The sample comprises of the total set of the population. The paper depends mainly on secondary data, which is collected from consolidated financial reports of commercial banks and other official publications and documents. To test the hypotheses and accuracy and validity of models and data, a set of methods of data analysis are employed, namely, Ordinary Least Squared (OLS), Generalized Least Squares (GLS), Autoregressive Distributed Lag (ARDL) and a number of Diagnostic Tests. The study documents that foreign exchange rate fluctuations, contrary to empirical research findings, have a weak negative effect on Sudanese banks’ financial performance. This may be attributed to the tight economic embargo against Sudan during the period of this study, which isolates the country from the international financial system and adversely affects its ability to engage in cross border activities. Consequently, the banking sector in Sudan is insulated from the effect of international currency movements and its exposure to currency risk that may create unpredictable profits and losses is minimal. In addition, the continuous deterioration of the Sudanese Pound and the limited FDI flows to the country render the investment environment uncompetitive and incapable of attracting foreign funds and the banking system of completely domestic nature.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.014
GPT teacher head0.209
Teacher spread0.195 · 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
Published2019
Admission routes1
Has abstractyes

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