Assessing the performance of banks through an improved sigma-mu multicriteria analysis approach

Abstract

The $\sigma$-$\mu$ efficiency methodology has been recently introduced for multicriteria evaluation problems, based on the framework of Stochastic Multi-Attribute Acceptability analysis (SMAA), to address the uncertainty in the performance of a set of decision alternatives. The methodology builds, iteratively, a set of Pareto-Koopmans efficiency frontiers, which are used to assess the alternatives with respect to their expected performance and its variability, measured across different scenarios for the weights of the evaluation criteria. This paper presents an improved algorithmic implementation of this methodology that provides results that are consistent with the Pareto dominance relation between the alternatives. The proposed approach is employed to evaluate the performance of a sample of European banks which participated in the European stress tests conducted by the European Banking Authority, over the last five years available (2017–2021). The performance and efficiency of the banks is analyzed using financial criteria along with environmental, social, and governance (ESG) factors. Results from comprehensive and disaggregated analysis reveal performance disparities among banks in financial and ESG factors, highlighting the influence of country-specific green policies and individual bank practices. Valuable for the banking sector and regulators, the findings help identify operational inefficiencies and propose areas for performance enhancement, operational improvement, and innovation, with a focus on green practices.