Inverse Multi-Objective Optimization for Portfolio Allocation in Commercial Banks

Optimal portfolio allocation in commercial banks is a critical decision for financial institutions. This paper proposes a multi-objective linear programming model to address this challenge. To ensure the model's feasibility and efficiency, we employ a generalized inverse optimization approach, replacing regular optimality with Pareto optimality. We apply our proposed models to real data from Bank Misr, an Egyptian bank, during the finance year 2020/2021. The multi-objective model was solved using LINGO 19, while the inverse multi-objective model was solved using R programming. Our analysis of the results provides valuable insights into optimal portfolio distribution for commercial banks.