Estimating cost efficiency of Turkish commercial banks under unobserved heterogeneity with stochastic frontier models

Güneş, Hakan; Yıldırım, Dilem

doi:10.1016/j.cbrev.2016.12.001

Cited by 10 publications

(9 citation statements)

References 41 publications

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“…The finding is also consistent with Xiang, Shamsuddin, & Worthington (2015) finding that converting deposits into loans increase cost efficiency. It is also comparable to Gunes & Yildirim (2016) and Daude & Pascal (2017) outlook that increasing loan to deposit ratio improves cost efficiency. Non-interest expense to total assets (NIETA) variable depicts the influence of administrative expense (Vu & Turnell, 2011), providing insights on how expense control by management affects cost efficiency.…”

Section: Determinants Of Cost Efficiencysupporting

confidence: 63%

“…Studies (e.g., Kumar, 2013;Balasubramanyan, Stefanou, & Stokes, 2010) that have investigated the banking industry express that cost inefficiency signals poor performance. Given the importance of the industry's stability to the overall economy (Daude & Pascal, 2017;Gunes & Yildirim, 2016), iota of concerns has economic implications that could agitate depositors and investors, emboldening regulators and policy makers to intervene in the affairs of the industry. Interestingly, studies have examined banking industry efficiency through the lens of technical efficiency (e.g., Horvatova, 2018;Saha, Ahmad, & Dash, 2015;Moradi-Motlagh & Saleh, 2014;Banker, Chang & Lee, 2010;Casu & Giradone, 2006), and cost and allocative efficiencies (e.g., Kasman & Carvallo, 2013;Akeem & Moses, 2014;Kumar, 2013;Feng & Serletis, 2009;Hartman, Storbeck, Byrnes, 2001).…”

Section: Review Of Literaturementioning

confidence: 99%

“…Another frontier approach that studies rely on is data envelopment analysis (DEA), which is a nonparametric approach that is sensitive to measurement error, but it does not require that the production function be specified (Coeli et al, 2005). The findings in single country SFA (e.g., Gunes & Yildirim, 2016;Stanek, 2015;Rouissi & Bouzgarrou, 2012;Vu & Turnell, 2011;Kalluru & Bhat, 2009) and DEA (e.g., Batir, Volkman, & Gungor, 2017;Hassan & Jreisat, 2016;Kocisova, 2014;Kumar, 2013;Daley, Matthews & Zhang, 2013) studies reveal cost inefficiency. However, while some of these studies (e.g., Batir, Volkman, & Gungor, 2017;Gunes & Yildirim, 2016;Daley, Mathews & Zhang, 2013) present evidence that show improvement in cost efficiency over time, others (e.g., Hassan & Jreisat, 2016;Feng & Serletis, 2009;Kalluru & Bhat, 2009) point to deterioration.…”

Section: Review Of Literaturementioning

confidence: 99%

“…The findings in single country SFA (e.g., Gunes & Yildirim, 2016;Stanek, 2015;Rouissi & Bouzgarrou, 2012;Vu & Turnell, 2011;Kalluru & Bhat, 2009) and DEA (e.g., Batir, Volkman, & Gungor, 2017;Hassan & Jreisat, 2016;Kocisova, 2014;Kumar, 2013;Daley, Matthews & Zhang, 2013) studies reveal cost inefficiency. However, while some of these studies (e.g., Batir, Volkman, & Gungor, 2017;Gunes & Yildirim, 2016;Daley, Mathews & Zhang, 2013) present evidence that show improvement in cost efficiency over time, others (e.g., Hassan & Jreisat, 2016;Feng & Serletis, 2009;Kalluru & Bhat, 2009) point to deterioration. Similarly, cross-country SFA (e.g., Rekik & Kalai, 2018;Spulbar & Nitoi, 2014;Perera, Skully, & Wickramanayake, 2007) and DEA (e.g., Belas, Kocisova, & Gavurova, 2019;Banya & Biekpe, 2018;Kacisova, 2014;Casu and Giradone, 2006) studies indicate cost inefficiency and variations among the sample of countries examined.…”

Section: Review Of Literaturementioning

confidence: 99%

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Cost Efficiency Determinants: Evidence from the Canadian Banking Industry

Oredegbe¹

2019

IJBM

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This study examines the cost efficiency of the banking industry in Canada. Utilizing 12 years of data (i.e., 2006 to 2017), and a two-stage data envelopment analysis (DEA), it provides insight on the determinants of the industry’s cost efficiency. It finds that the industry is cost inefficient, and that it could reduce costs by 11.52 percent. The cost inefficiency is due to technical and allocative inefficiencies, with technical inefficiency playing a dominant role. The technical efficiency decomposition shows that pure technical efficiency improved, but the scale efficiency deteriorated. The analysis of the determinants of cost efficiency reveals that deposit conversion into loans, high capitalization, and managerial tolerance for increase in administrative expense drive cost efficiency. On the other hand, market power and diversification diminish cost efficiency. In addition, the impact of profitability and credit risk are inconsequential to cost efficiency. This study contributes to literature by providing insights unique to Canada. Managers in the industry, policy makers, and regulators can point to these findings as empirical evidence supporting measures aimed at increasing the industry’s competitiveness and resilience.

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Section: Determinants Of Cost Efficiencysupporting

confidence: 63%

Section: Review Of Literaturementioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

See 2 more Smart Citations

Cost Efficiency Determinants: Evidence from the Canadian Banking Industry

Oredegbe¹

2019

IJBM

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“…An efficiency measurement of the Brazilian banking system using non-dynamic panel data models is implemented to compute the efficiency level [21]. A study of Turkish commercial bank performance finds the cost efficiency scores using the true fixed effects model [22]. An evaluation of bank efficiency in Slovakia employs BCC models [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing Bank Performance Using Dynamic SBM Model

et al. 2019

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Global economic growth has led banks to expand their operations all over the world. The purpose of this research was to understand the efficiency of 18 large bank from all over the world during the period from 2013 to 2017. The performance was estimated by a dynamic slacks-based measure (SBM) model in data envelopment analysis (DEA). This model could be solved using inputs, outputs, and links. The banks variables were considered as follows: Assets, capitalization, and liabilities as inputs; revenue as output; and net interest income as a good link. The final empirical results exhibit the efficiency for each term, and the overall score. The data analysis recommends a feasible solution to refine inefficient terms based on the projections (slacks). This study visually observed the proficiency of the banking industry to equip enterprises with the best choice for their finances.

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