Modeling the efficiency of top Arab banks: A DEA–neural network approach

Mostafa, Mohamed M.

doi:10.1016/j.eswa.2007.09.001

Cited by 91 publications

(78 citation statements)

References 87 publications

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“…Every respondent describes his judgment about the innovation degree in his bank by the following linguistic terms; Not at all satisfied, unsatisfied, moderately satisfied, satisfied, and very satisfied. These linguistic expressions were converted into fuzzy numbers as (3,4,5), (6,7,8), (9,10,11), (12,13,14) and (15,16,17), respectively. In order to establish the imprecise value of the innovation level for each bank, we used the following aggregation function.…”

Section: Used Datamentioning

confidence: 99%

Assessing the Efficiency of commercial Tunisian Banks using Fuzzy Data Envelopment Analysis

Tlig¹,

Hamed²

2017

Data Envelopment Analysis and Decision Science

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The banking sector is of great importance to Tunisian's economy. Major commercial banks continue to spend high proportion of their budgets on new technologies and innovation in order to satisfy their customers and enhance their competitiveness. Consequently, performance analysis has become part of their management practices.This paper aims to evaluate the efficiency of commercial Tunisian banks in terms of several crisp and imprecise data. Two approaches of fuzzy data envelopment analysis (FDEA), the possibility approach and the approach based on relations between fuzzy numbers (BRONF), are used to obtain the efficiency score of each bank. The results show that, in a competitive environment, no-financial inputs and outputs should be taken into account in order to obtain credible and realistic efficiency scores.

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Section: Used Datamentioning

confidence: 99%

Assessing the Efficiency of commercial Tunisian Banks using Fuzzy Data Envelopment Analysis

Tlig¹,

Hamed²

2017

Data Envelopment Analysis and Decision Science

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“…8, No. 3;2014 conventional regression methods is that conventional regression need to specify the model structure in advance, which is mostly suboptimal. ANNS require the identification of the network structure and then the coefficients (weights) are calculated during the learning process.…”

Section: Genetic Programmingmentioning

confidence: 99%

“…8, No. 3;2014 ANNs is not capable of extracting interpolation equations. The ANN implementation is needed to be done by a computer program.…”

Section: Introductionmentioning

confidence: 99%

“…The ANN implementation is needed to be done by a computer program. The new hybrid approach combining DEA and ANNs (Athanassopoulos & Curram, 1996) has been applied in many fields (Mostafa, 2009;Pendharka, 2010;Çelebi & Bayraktar, 2008;Wu, 2009;Wu et al, 2006;Wang et al, 2009 (Kaboudan, 2003) such as forecasting electricity demand (Lee et al, 1997); forecasting long term energy consumption (Karabulut et al, 2008) in real-time runoff (Khu et al, 2001); predicting financial data (Iba & Sasaki, 2002); predicting stock prices (Kaboudan, 2000) in fault analysis of the diesel engine fuel (Sun et al, 2004); prediction of ski-jump bucket spillway scour (Azamathulla et al, 2008); river pipeline scour (Azamathulla & Ghani, 2010) and longitudinal dispersion coefficients in streams (Azamathulla & Ghani, 2011) and etc. This study presents a genetic programming procedure for performance evaluating of a set of homogeneous steam power plants and benchmarking.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Electric Power Plants Performance Assessment Technique Based on Genetic Programming Approach

Ghomi¹,

Ansarinejad²,

Razaghi³

et al. 2014

MAS

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This paper presents a novel nonparametric efficiency analysis technique based on the Genetic Programming (GP) in order to measure efficiency of Iran electric power plants. GP model was used to predict the output of power plants with respect to input data. The method, we presented here, is capable of finding a best performance among power plant based on the set of input data, GP predicted results and real outputs. The advantage of using GP over traditional statistical methods is that in prediction with GP, the researcher doesn't need to assume the data characteristic of the dependent variable or output and the independent variable or input. In this proposed methodology to calculate the efficiency scores, a novel algorithm was introduced which worked on the basis of predicted and real output values. To validate our model, the results of proposed algorithm for calculating efficiency rank of power plants were compared to traditional method. Real data was presented for illustrative our proposed methodology. Results showed that by utilizing the capability of input-output pattern recognition of GP, this method provides more realistic results and outperform in identification of efficient units than the conventional methods.

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“…Dentre os estudos com fator de impacto acima de 1 que avaliaram a eficiência de produção, são destaques os trabalhos voltados para oferecer informações de gestão para os bancos avaliarem suas próprias agências (BERGENDAHL, 1998;FETHI;PASIOURAS, 2010;GIOKAS, 1991;LIU, 2011;MOSTAFA, 2009;ORAL;YOLALAN, 1990 Além disso, pensando na sustentabilidade financeira das organizações, inclusive as bancárias, a rentabilidade é uma das dimensões essenciais para a perpetuidade das empresas.…”

Section: Abordagem De Produçãounclassified

Eficiência do sistema bancário brasileiro em 2014: uma análise DEA-SBM

Branco¹

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Modeling the efficiency of top Arab banks: A DEA–neural network approach

Cited by 91 publications

References 87 publications

Assessing the Efficiency of commercial Tunisian Banks using Fuzzy Data Envelopment Analysis

Assessing the Efficiency of commercial Tunisian Banks using Fuzzy Data Envelopment Analysis

A Novel Electric Power Plants Performance Assessment Technique Based on Genetic Programming Approach

Eficiência do sistema bancário brasileiro em 2014: uma análise DEA-SBM

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