Benchmarking bank branches : A dynamic DEA approach

Razipour-GhalehJough, Somayeh; Lotfı, Farhad Hosseinzadeh; Rostamy-Malkhalifeh, Mohsen; Sharafi, Hamid

doi:10.1080/02522667.2020.1833441

Cited by 8 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It calculates an empirically best-practice production frontier based on the observed inputs and outputs of individual DMUs, replicating their individual behavior rather than relying on average sample estimates from conventional production functions or assigning prior weight to the input and output (Cook et al 2014). DEA has been empirically applied in the banking sectors for appraisal of performance (Haslem et al 1999;Thanassoulis 1999;Siriopoulos and Tziogkidis 2010;Defung 2018;Shah et al 2019a;Razipour-GhalehJough et al 2021).…”

Section: Dea (1st Stage Analysis)mentioning

confidence: 99%

“…The definition of outputs and inputs for financial firms lacks a unanimous consensus, as pointed out by Benston (1972) and Clark (1984). After reviewing the literature on banking industry related DEA studies (e.g., Haslem et al 1999;Thanassoulis 1999;Siriopoulos and Tziogkidis 2010;Defung 2018;Shah et al 2019b;Razipour-GhalehJough et al 2021) and considering the data availability, the researcher has chosen (1) total cost, total liability, and total deposit as input variables, and (2) total assets and total equity as output variables. These data were retrieved from the association of banks in Jordan.…”

Section: Input/output Variablesmentioning

confidence: 99%

See 1 more Smart Citation

Efficiency Assessment and Determinants of Performance: A Study of Jordan’s Banks Using DEA and Tobit Regression

Istaiteyeh,

Milhem,

Elsayed

2024

Economies

View full text Add to dashboard Cite

This comprehensive study explored the efficiency landscape of the Jordanian banking industry from 2006 to 2021, utilizing a dual-pronged approach. First, we assessed the efficiency scores of 15 commercial banks, comprising 13 conventional and 2 Islamic institutions, through data envelopment analysis (DEA). Secondly, we investigated the determinants influencing relative efficiency using the Tobit regression model. Our dataset, spanning 240 observations over 16 years, provides a nuanced examination of industry dynamics. DEA, specifically focusing on variable return to scale (VRS), unveils efficiency scores by accounting for scale inefficiencies. The research contributes insights into the operational efficacy of Jordanian banks and provides a robust methodology for understanding efficiency dynamics in the broader financial landscape. The results reveal significant relationships between return on assets, return on equity, GDP growth, and efficiency. Furthermore, it is noteworthy that Islamic banks demonstrate higher efficiency compared to conventional banks. Additionally, non-significant associations were observed with credit risk, bank size, and the ratio of loan loss provision over net income. The findings hold implications for policymakers, industry stakeholders, and researchers aiming to bolster the resilience and competitiveness of Jordan’s banking sector.

show abstract

Section: Dea (1st Stage Analysis)mentioning

confidence: 99%

Section: Input/output Variablesmentioning

confidence: 99%

Efficiency Assessment and Determinants of Performance: A Study of Jordan’s Banks Using DEA and Tobit Regression

Istaiteyeh,

Milhem,

Elsayed

2024

Economies

View full text Add to dashboard Cite

show abstract

“…En el primer enfoque, los bancos utilizan mano de obra y capital para proporcionar préstamos y servicios de depósito (Cofré et al, 2019), mientras que en el segundo enfoque los bancos fungen como intermediarios entre prestatarios y prestamistas. Sin embargo, ninguno de los enfoques es perfecto y la selección de entradas y salidas es flexible, ya que, por ejemplo, lo usual es que los depósitos se empleen como entradas (Amin & Ibn-Boamah, 2020;Razipour-GhalehJough et al, 2021) y los préstamos como salidas (Lartey et al, 2021), pero también hay casos en que los depósitos se utilizan como productos (Maudos et al, 2002) o como insumos y productos (Li et al, 2021).…”

Section: Issn Online 2007-9621unclassified

Contribución de las inversiones en recursos humanos según el tipo de eficiencia técnica de la banca múltiple peruana

Flores Gutiérrez,

Castillo Inche,

Tejada Portilla

2023

Acta Universitaria

View full text Add to dashboard Cite

Se evaluó la contribución de las inversiones en recursos humanos (RRHH) a la eficiencia técnica de la banca múltiple peruana durante el 2021. Además, se demostró qué tipo de frontera de producción prevalece y se generó un plan benchmarking. A una muestra censal de 16 bancos se les midieron tres inputs (remuneraciones al personal, gastos del directorio y activos) y dos outputs (total de depósitos y total de créditos vigentes). Se identificaron siete bancos en la frontera de eficiencia técnica global (ETG), 12 bancos en la de eficiencia técnica pura (ETP) y siete bancos en la de eficiencia de escala (EE). La media de la ETG fue 0.86, la de ETP fue 0.95 y la de EE fue 0.91. Se determinó que prevalecen los rendimientos variables a escala, que la eficiencia de los bancos no varía con el nivel de inversión en recursos humanos, y se formuló un plan de mejoras potenciales.

show abstract

“…In the first approach, banks use labor and capital to provide loans and deposit services, whereas in the second approach, banks serve as intermediaries between borrowers and lenders. However, while neither approach is perfect, the selection of inputs and outputs is nevertheless very flexible, where generally deposits are used as inputs (Amin & Ibn Boamah, 2020 ; Razipour-GhalehJough et al, 2021 ) and loans are used as outputs (Lartey et al, 2021 ), but there are also examples where deposits are used as outputs (Maudos et al, 2002 ) or both inputs and outputs (Li et al, 2021 ).…”

Section: Data and Variable Selectionmentioning

confidence: 99%

Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis

Chen

Tang

2022

Ann Oper Res

View full text Add to dashboard Cite

For decades, the prediction of bank failure has been a popular topic in credit risk and banking studies. Statistical and machine learning methods have been working well in predicting the probability of bankruptcy for different time horizons prior to the failure. In recent years, bank efficiency has attracted much interest from academic circles, where low productivity or efficiency in banks has been regarded as a potential reason for failure. It is generally believed that low efficiency implies low-quality management of the organisation, which may lead to bad performance in the competitive financial markets. Previous papers linking efficiency measures calculated by Data Envelopment Analysis (DEA) to bank failure prediction have been limited to cross sectional analyses. A dynamic analysis with the updated samples is therefore recommended for bankruptcy prediction. This paper proposes a nonparametric method, Malmquist DEA with Worst Practice Frontier, to dynamically assess the bankruptcy risk of banks over multiple periods. A total sample of 4426 US banks over a period of 15 years (2002–2016), covering the subprime financial crisis, is used to empirically test the model. A static model is used as the benchmark, and we introduce more extensions for comparisons of predictive performance. Results of the comparisons and robustness tests show that Malmquist DEA is a useful tool not only for estimating productivity growth but also to give early warnings of the potential collapse of banks. The extended DEA models with various reference sets and orientations also show strong predictive power.

show abstract

Benchmarking bank branches : A dynamic DEA approach

Cited by 8 publications

References 34 publications

Efficiency Assessment and Determinants of Performance: A Study of Jordan’s Banks Using DEA and Tobit Regression

Efficiency Assessment and Determinants of Performance: A Study of Jordan’s Banks Using DEA and Tobit Regression

Contribución de las inversiones en recursos humanos según el tipo de eficiencia técnica de la banca múltiple peruana

Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis

Contact Info

Product

Resources

About