An Optimal Model for Managing Assets, Liabilities, and Equity in Commercial Banks under the Supervisory Regulations of the Central Bank of Iran

This study aimed to develop an optimal model for managing assets, liabilities, and equity in Iranian commercial banks in compliance with Central Bank supervisory regulations. This applied research used audited financial statements of ten listed commercial banks (Mellat, Tejarat, Saderat, Parsian, Pasargad, Eghtesad Novin, Sina, Dey, Karafarin, and Middle East) during ۲۰۱۹–۲۰۲۳. Data were collected from CODAL, the Central Bank of Iran, and related financial databases. The methodology integrated the Best-Worst Method (BWM) for weighting decision variables and a fuzzy goal programming approach to manage uncertainty and set realistic target ranges. Six key decision criteria were evaluated: return on assets (ROA), return on equity (ROE), liquidity risk ratio (LRR), capital adequacy ratio (CAR), non-performing assets (NPA), and market share of deposits and credits (MSDL). The findings revealed that “capital adequacy ratio” ranked as the most critical criterion (average weight ۰.۴۶۹۹), followed by liquidity risk ratio and reduction of non-performing assets. In contrast, market share of deposits and credits had the lowest priority. Results of the optimization model indicated that all banks achieved full compliance with Central Bank requirements after modest adjustments. The most recurrent deviations were observed in return on equity, which consistently required reduction across banks, averaging ۱.۱ percentage points below optimal values. Liquidity ratios and capital adequacy remained within acceptable ranges across institutions, while non-performing assets showed only minor deviations. Banks such as Mellat and Sina required minimal reforms, whereas Parsian, Middle East, and Eghtesad Novin demanded more extensive adjustments to balance their financial structures. The proposed fuzzy goal programming model provides a robust framework for balancing profitability, risk, and compliance. This study aimed to develop an optimal model for managing assets, liabilities, and equity in Iranian commercial banks in compliance with Central Bank supervisory regulations. This applied research used audited financial statements of ten listed commercial banks (Mellat, Tejarat, Saderat, Parsian, Pasargad, Eghtesad Novin, Sina, Dey, Karafarin, and Middle East) during ۲۰۱۹–۲۰۲۳. Data were collected from CODAL, the Central Bank of Iran, and related financial databases. The methodology integrated the Best-Worst Method (BWM) for weighting decision variables and a fuzzy goal programming approach to manage uncertainty and set realistic target ranges. Six key decision criteria were evaluated: return on assets (ROA), return on equity (ROE), liquidity risk ratio (LRR), capital adequacy ratio (CAR), non-performing assets (NPA), and market share of deposits and credits (MSDL). The findings revealed that “capital adequacy ratio” ranked as the most critical criterion (average weight ۰.۴۶۹۹), followed by liquidity risk ratio and reduction of non-performing assets. In contrast, market share of deposits and credits had the lowest priority. Results of the optimization model indicated that all banks achieved full compliance with Central Bank requirements after modest adjustments. The most recurrent deviations were observed in return on equity, which consistently required reduction across banks, averaging ۱.۱ percentage points below optimal values. Liquidity ratios and capital adequacy remained within acceptable ranges across institutions, while non-performing assets showed only minor deviations. Banks such as Mellat and Sina required minimal reforms, whereas Parsian, Middle East, and Eghtesad Novin demanded more extensive adjustments to balance their financial structures. The proposed fuzzy goal programming model provides a robust framework for balancing profitability, risk, and compliance.

کلیدواژه ها:

Optimal model ، Asset management ، Liability management ، ، Equity management ، Central Bank regulations ، Commercial banks ، Fuzzy goal programming