Sahin, Gokdemir, & Ozturk (2016) examined the effects of the global financial crisis on public, private, and foreign-capitalized commercial banks in the Turkish banking sector using an input-oriented approach. Adopting a study span of eight years and relying on the variable returns to scale DEA model (BCC), they found an increasing trend of efficiency during the global financial crisis. Also, they found that private banks were responsible for the decrease in the average efficiency of the Turkish banking sector as their input-oriented efficiency scores plummeted in the post-global financial crisis period.
Moradi-Motlagh & Babacan (2015) examined the efficiency of eight Australian banks using the bootstrap DEA method within the period of 2006 – 2012. The study found that the efficiency of Australian banks dropped considerably during the global financial crisis. They also pointed out that the efficiency of the examined Australian banks did not improve until in 2012 as all the banks showed low-efficiency levels in 2009.
Gulati & Kumar (2016) assessed the impact of the global financial crisis on the Indian banking sector. The study employed a DEA-based meta profit frontier framework that accounted for technological heterogeneity across different groups of banks. The results indicated that the efficiency level of Indian banks dropped mildly during the global financial crisis but recovered immediately after the crisis. The study, however, noted that the global financial crisis had a differentiated impact across ownership groups. The DEA results showed that private banks were the worst hit by the global financial crisis, while foreign banks performed better because of their adherence to best practices and access to superior technology.
The DEA model has been employed as a benchmarking tool to identify the most efficient institutions within a sample. However, very few studies have used it as a forward-looking alternative method to predict future bank failures and distress (Avkiran & Lin, 2012). Avkiran & Lin (2012) found out that the DEA can be used to identify distressed banks up to two years in advance. They indicated that the robustness test they carried out revealed that the DEA technique produces an efficient frontier and the discriminatory and predictive powers of the technique do not change even after perturbations. The further opined that the DEA technique could be employed as an off-site screening tool by regulators to gauge the
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likelihood of financial distress. Simply put, the DEA technique can assist regulatory agencies, investors and managers in making vital economic decisions.
Other than using the DEA to examine bank performance and predict financial distress, other scholars used the technique for similar purposes. Pille & Paradi (2002) developed DEA four models alongside a Z-Score model modified by government regulators and the equity-to-asset ratio to detect financial failures in credit unions in Ontario (Canada). The foci of the study were the needs of government regulators, and they tested which models were best competent in predicting bankruptcy. Following the input-oriented model and using a pool of input and output variables, they suggested that the DEA technique was an adequate tool for detecting financial failures. Nonetheless, the study failed to present the preferred model or combinations of inputs and output variables that best detect financial failures.
Conversely, in a bid to extend the application of DEA in the banking sector, Kwon & Lee (2015) explored an innovative performance model for a two-stage sequential production process by combining the data envelopment analysis (DEA) and back-propagation neural network (BPNN). Contrary to several banking studies and the studies reviewed in this section, Kwon & Lee followed the DEA output-oriented model. They opined that DEA alone lacked predictive capacity, hence the combination with BPNN. They found that by combining DEA and BPNN, managers can predict the performance of DMUs previously not seen by the DEA alone. To that end, this study justifies the adoption of qualitative content analysis and panel data regression to increase the reliability of the research outcomes.
Similarly, Premachandra, Chen & Watson (2011) employed the DEA additive super- efficiency model to predict corporate failures and success in the United States. Using a sample of 50 large U.S bankrupt firms and 901 healthy matching firms, the findings of the study demonstrated that the DEA model was relatively weak in predicting corporate failures, as they found it to be better suited to predicting corporate success.
Furthermore, Alam (2013) calculated the technical efficiency of 70 Islamic banks from 11 countries using the DEA model and the seemingly unrelated regression (SUR) approach simultaneity to ascertain the relation between bank regulation and supervision on risk and efficiency. The results obtained indicate that regulations and strict monitoring of banking operations and higher supervisory powers of regulatory agencies translate to increased technical efficiency in Islamic banks, with higher restrictions resulting in reduced risk taking in Islamic banks. The study also revealed that even though Basel II and Basel III suggested
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that very strict regulations and supervision may hamper banking efficiency. Nevertheless, the DEA scores in the study suggest that Islamic banks do not follow this trend, as they appear to be technically efficient in stricter regulatory climes.
In like manner, Pasiouras (2007) employed a two-stage DEA model to provide international evidence on the impact of regulations and supervision approaches on banks’ efficiency using a sample of 715 banks from 95 countries. Using an input-oriented model, the results obtained show evidence in line with the view of the three pillars of Basel II, that banking institutions should adopt strict capital adequacy regimes, develop powerful supervisory agencies, and create disciplining mechanisms.
Furthermore, in addition to the studies reviewed in this section, Pasiouras (2007) suggest that most DEA studies in banking follow the input-oriented model. The input-oriented model identifies the efficiency of DMUs as a proportional reduction in input usage for a particular level of output (examples of studies that followed the input-oriented model include Raphael, 2013; Herrera-Restrepo., et al 2016; Paradi et al., 2004; Seelanatha, 2012; Sufian, 2011). Whereas the output-oriented model identifies DMUs can increase their output while keeping inputs at particular levels (Coelli et al., 1999). In addition to Premachandra et al. (2011), examples of studies that adopted the output-oriented model includes Attaullah et al., (2004); Ataullah & Le (2006). Hence, the large number of studies that employ the input-oriented model indicates that it is preferred above the output-oriented model. Moreover, it is claimed that the intermediary role of banking institutions suits the input-oriented model (Pasiouras, 2007).
Finally, the studies in this section buttress the ability of the DEA technique as an adequate measure for ascertaining the efficiency level of banking institutions. Additionally, some of the reviewed studies also relied on the DEA approach to predict financial distress. Therefore, the DEA technique is used to examine the efficiency of Nigerian DMBs as they went through the 2005 and 2009 Nigerian banking reforms, and ascertain whether it was able to identify DMBs that were in danger of collapse. None of the studies reviewed above adopted the DEA window technique, however, the few banking studies that made use of it are presented in the methodology section. To that end, the use of the DEA window technique, which is a variant of the conventional DEA technique, contributes to the literature on predicting financial distress.
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The next section of this thesis centres on the review of regression analysis studies that made use of bank-specific CAMELS variables to show the determinants of efficiency, performance and stability. In relation to the studies reviewed above, the efficiency scores obtained from the DEA technique were used as dependent variables in the second stage regression analysis in several banking studies like in Pasiouras (2007), Premachandra., et al. (2011), and Moradi- Motlagh & Babacan (2015). In like manner, DEA efficiency scores are used as dependent variables in this study. Hence, the section below covers some recent banking performance (CAMELS) regression studies in various jurisdictions and periods.