Relative Efficiency of Canadian Banks: A Three-Stage Network Bootstrap DEA

Abstract

In this study, we focus on how banks can enhance their efficiency in the utilization of resources to ensure their economic sustainability. We propose a novel three-stage (production, investment, and revenue generation) network Data Envelopment Analysis (DEA) with bootstrapping to evaluate the performance of the six big Canadian banks for the period 2000-2017, amid the 2007 financial crisis and the increasing competition level due to new technologies. We identify the best practices in each stage that can be used as benchmarks by other banks to improve their economic sustainability. Our results indicate that the 2007 financial crisis resulted in lower efficiencies in the performance of Canadian banks. This decline was not substantial for the production and investment stages when the revenue generation stage received the greatest hit. In addition, we observed that the individual banks did not have consistent performance in the different stages. Finally, we compared our model with the black box DEA model and concluded that the network DEA provides more insightful and accurate results in terms of banks' efficiencies.

Performance measuring of wood-processing microenterprises through Data Envelopment Analysis: A case study of Slovakia, Poland, and Bulgaria

“…Tsolas (2020) measured performance differences between 62 precious metal mutual funds using weighted additive data envelopment analysis (DEA). Other authors like Dia et al (2020), Neves et al (2020), and Qayyum and Riaz (2018) used DEA for benchmarking and performance measurement in the banking sector in various ways, which proved the applicability of this approach. Horváthová et al (2021) used DEA for benchmarking and performance measurement to provide necessary information for improving business performance.…”

Opening the Black-box of Bank Efficiency in Turkey with Two-stage Data Envelopment Analysis: A Study on Capital Adequacy Ratio

“…Unfortunately, not for the two-stage framework adopted in this study. Because defining deposits as an intermediate variable, in its most general form, requires a flow where bank resources turn into deposits and then deposits turn into investments and loans (see Fukuyama and Weber, 2010;Fukuyama and Matousek, 2011;Degl'Innocenti et al, 2017;Dia et al, 2020).…”

The Efficiency Analysis of Large Banks Using the Bootstrap and Fuzzy DEA: A Case of an Emerging Market

“…The results of the BDEA approach applied to a sample of Vietnamese banks in the period 1999-2009 indicate that large and very large banks are more efficient than small and medium-sized banks, with small banks having the lowest efficiency scores in the system; non-state-owned commercial banks are more efficient than state-owned commercial banks in terms of overall efficiency (Stewart et al 2016). Network BDEA and the black box DEA were used on a sample of six big Canadian banks for the period 2000-2017 in order to examine which of the two models provided more accurate results on how the 2007 financial crisis and the increasing competition level due to new technologies affected efficiency [15]. The results indicated that the 2007 financial crisis resulted in lower performance efficiencies.…”

“…However, this decline was not substantial for the production and investment stages when the revenue generation stage experienced the largest impact. A comparison of BDEA with the black box DEA model concluded that the network BDEA provides more insightful and accurate results in terms of banks' efficiencies [15]. Samad [16] obtained overall bias-corrected technical efficiency (OTEBC), bias-corrected pure technical efficiency (PTEBC) and biascorrected scale efficiency (SE) of the Islamic banks of the Gulf Cooperating Countries (GCC) during 2014-2016 using the Simar and Wilson [17] BDEA.…”