ANALYSIS OF THE FINANCIAL PERFORMANCE OF THE DEPOSIT BANKING SECTOR IN THE COVID-19 PERIOD WITH LMAW-DNMA METHODS

Abstract

Banks, as one of the most important actors in the money and capital markets, play a critical role in the financial intermediation process for every country’s economy. The deposit banking sector is critical to the banking system’s effective and efficient operation. The goal of this research is to develop a multi-criteria model for measuring and assessing the performance of the deposit banking sector during the COVID-19 pandemic. In this context, an integrated model consisting of the Logarithm Methodology of Additive Weights (LMAW) and Double Normalization-based Multiple Aggregation (DNMA) methods is proposed. In the first stage of the proposed performance evaluation model, the performance indicators determined based on the previous literature were weighted with the LMAW method. In the second stage, the DNMA method was used to measure and evaluate the financial performance of the deposit banking sector during the COVID-19 pandemic period. In this context, an integrated model consisting of the Logarithm Methodology of Additive Weights (LMAW) and Double Normalization-based Multiple Aggregation (DNMA) methods are proposed. In the first stage of the proposed performance evaluation model, the performance indicators determined based on the previous literature were weighted with the LMAW method. In the second stage, the DNMA method was used to measure and evaluate the financial performance of the deposit banking sector during the COVID-19 pandemic period. When the DNMA ranking findings were analyzed generally, it was determined that the deposit banking sector was severely impacted by the COVID-19 pandemic. This suggests that the sector is inefficient at managing pandemic-related risks. This study’s findings may assist bank management, investors, and regulatory and supervisory agencies in making more accurate decisions regarding the stability of the banking system. Various sensitivity analyses were also utilized to verify the model’s dependability and robustness within the scope of the study. The results of sensitivity analyses indicate that the suggested methodology generates consistent and reliable sequencing outcomes.Banks, as one of the most important actors in the money and capital markets, play a critical role in the financial intermediation process for every country’s economy. The deposit banking sector is critical to the banking system’s effective and efficient operation. The goal of this research is to develop a multi-criteria model for measuring and assessing the performance of the deposit banking sector during the COVID-19 pandemic. In this context, an integrated model consisting of the Logarithm Methodology of Additive Weights (LMAW) and Double Normalization-based Multiple Aggregation (DNMA) methods is proposed. In the first stage of the proposed performance evaluation model, the performance indicators determined based on the previous literature were weighted with the LMAW method. In the second stage, the DNMA method was used to measure and evaluate the financial performance of the deposit banking sector during the COVID-19 pandemic period. In this context, an integrated model consisting of the Logarithm Methodology of Additive Weights (LMAW) and Double Normalization-based Multiple Aggregation (DNMA) methods are proposed. In the first stage of the proposed performance evaluation model, the performance indicators determined based on the previous literature were weighted with the LMAW method. In the second stage, the DNMA method was used to measure and evaluate the financial performance of the deposit banking sector during the COVID-19 pandemic period. When the DNMA ranking findings were analyzed generally, it was determined that the deposit banking sector was severely impacted by the COVID-19 pandemic. This suggests that the sector is inefficient at managing pandemic-related risks. This study’s findings may assist bank management, investors, and regulatory and supervisory agencies in making more accurate decisions regarding the stability of the banking system. Various sensitivity analyses were also utilized to verify the model’s dependability and robustness within the scope of the study. The results of sensitivity analyses indicate that the suggested methodology generates consistent and reliable sequencing outcomes.