A Model for Bank Performance Measurement Integrating Multivariate Factor Structure with Multi-Criteria PROMETHEE Methodology

Abstract

The global financial crisis and the subsequent Euro-zone crises have resulted in widespread failure of banking systems worldwide. The Indian banking system, which was initially hailed to be unaffected by the crises, was affected indirectly, mainly on account of growing trade and financial integration with the global economy. Although Indian banks were not pushed to the point of insolvency, bank performance benchmarking and evaluation have become important in the dynamic banking environment in India in order to ensure sustained profitability and avoid undue risks.

The CAMELS model is one of the most widely-used frameworks for bank performance evaluation (Sahajwala and van der Bergh, 2000). The CAMELS methodology provides a broader view of bank performance than single ratios such as return on equity, particularly as it takes account of both profitability and risk factors in representing bank performance. Several studies have proposed multi-criteria decision models for bank performance measurement (Doumpos and Zopounidis, 2011).

The objective of the present study is to integrate multivariate and multi-criteria decision models in bank performance measurement. The study uses the factor structure of the CAMELS model to derive weights for the different criteria in the PROMETHEE methodology. The resulting PROMETHEE scores are used to rank banks under different dimensions, and to compare the performance of public sector and private sector banks in India.