DEA methodologies for assessing the efficiency profiles of commercial banks under heterogeneity conditions

Abstract

Since the publication of the seminal paper by Charnes, Cooper and Rhodes in 1978, where the conventional CCR model of Data Envelopment Analysis (DEA) has been proposed, DEA as a field has substantially evolved both methodologically and in terms of applications. So far, efficiency and productivity studies in the banking sector proved to be amongst the most popular application areas. The popularity of DEA in this field, amongst others, is due to its unique features such as its non-parametric nature, it benchmarks against the best practice performers rather than the average performers. It allows one to identify targets for improvement; it does not need any functional specification of the relationship between inputs and outputs, and provides a variety of efficiency measures most suitable for a variety of applications. Moreover, it provides a wide range of models to perform analyses at the aggregate level and the detailed level. In addition, DEA models allow one to perform both Static and Dynamic analyses. In this thesis, DEA is used to assess the efficiency profiles of commercial banks under heterogeneity conditions. First, a new DEA-based analysis framework with a regression-based feedback mechanism is proposed to deal with the particular features of the UK banking sector, where regression analysis provides DEA with feedback that informs about the relevance of the inputs and the outputs chosen by the analyst. Unlike previous studies, the DEA models used within the proposed framework could use both inputs and outputs, only inputs, or only outputs, which proved necessary with UK data. Second, to the best of our knowledge, no attempt has been made to investigate the relative efficiency of operating environments of banks. This thesis aims at filling this gap by analysing the efficiency of HSBC in different operating environments or countries over time. The choice of a single bank; namely, HSBC, is motivated by isolating the operating environment effect on efficiency and thus avoiding any bias that would result from the relative efficiency of different banks within the same operating environment. From a methodological perspective, this analysis is performed using a variety of framework; namely, A four-stage analysis is performed with Static black box SBM, Dynamic SBM, Network SBM, and Dynamic-Network SBM DEA frameworks. Overall, this thesis contributes to both the DEA field, through its methodological contributions, and the banking sector, through its application of the methodological contributions in assessing banks’ efficiency profiles.