An integrated multi-criteria decision making model with Self-Organizing Maps for the assessment of the performance of publicly traded banks in Borsa Istanbul

摘要

The major role of banks is to play financial intermediation function between funding needs and funding surpluses. Any problem in the banking system directly influences not only the stakeholders but also the general economy. There have been many attempts to measure the performance of banks by using Multi-Criteria Decision Making Tools (MCDM). This study aims to assess the performance of the publicly traded banks in Borsa Istanbul operating in the Turkish banking sector for the four quarter of 2018. Contribution of this study can be summarized as follow. Dataset is consisted of three dimensions: (i) financial ratios, (ii) branch and personnel network, (iii) daily stock market returns and standard deviation of daily returns. Using a multi-dimensional dataset compiled from three different data sources enabled an objective assessment of bank performance. Features are selected from the financial ratio dataset by using Self-Organizing Maps technique. Instead of using single weight set, a million weight combinations are calculated to monitor the score distributions of MCDM tools. EDAS, MOORA, OCRA and TOPSIS techniques are selected due to their similarity in calculation steps. It is found that OCRA technique produced consistent rankings for different periods. Highest correlation coefficients observed between OCRA and TOPSIS techniques. (C) 2020 Elsevier B.V. All rights reserved.