Portfolio optimization of bank credits with interval returns: Empirical evidence from Iran

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Bank credit is one of the main sources of spending on productivity and economic services. However, because of the limitations in its amount, accurate planning is essential to optimize its allocation to applicants. Despite the total volume of credits allocated to the agricultural sector, as well as the large number of applicants and sub-sectors applying for these facilities, there is still no clear pattern for the optimal allocation of agricultural bank credits in Iran. It is bank managers who must decide on the distribution of financial capital in a competitive environment. Based on this fact, the paper investigates the optimum portfolio composition of the Agricultural Bank credits in accordance with optimistic, pessimistic, and collaborative strategies by using an interval non-linear multi-objective programming model and considering three different states in determining the rate of return using a genetic algorithm. The results showed that the current pattern of the distribution of bank credits is estimated as different from the optimal one. In the optimum patterns estimated in all states, the agriculture, agricultural services, animal husbandry, aviculture and greenhouses sections were assigned the largest shares in their optimum portfolio combination. Managers can choose their desired model according to three studied strategies and depending on the importance, different estimates of return, and risk of each of them.

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    • Figure 1. Relationship between historical average returns and expected interim returns according to economic conditions
    • Table A1. The amount of facilities paid to different sectors
    • Table A2. The returns of different sectors during a six-year period (%)
    • Table A3. Upper and lower limit of returns in different states (%)
    • Table 4. Normality test
    • Table 5. The contribution of each sector to the objective function 1 of the genetic algorithm in Mode 1
    • Table 6. Summary of optimal results in objective function 1 of the genetic algorithm in Mode 1
    • Table 7. The contribution of each sector to the objective function 2 of the genetic algorithm in Mode 1
    • Table 8. Summary of optimal portfolio results in objective function 2 of the genetic algorithm in Mode 1
    • Table 9. The contribution of each sector to the objective function 3 of the genetic algorithm in Mode 1
    • Table 10. Summary of optimal portfolio results in objective function 3 of the genetic algorithm in Mode 1
    • Table 11. The contribution of each sector to the objective function 1 of the genetic algorithm in Mode 2
    • Table 12. Summary of optimal portfolio results in objective function 1 of the genetic algorithm in Mode 2
    • Table 13. The contribution of each sector to the objective function 2 of the genetic algorithm in Mode 2
    • Table 14. Summary of optimal results in objective function 2 of the genetic algorithm in Mode 2
    • Table 15. The contribution of each sector to the objective function 3 of the genetic algorithm in Mode 2
    • Table16. Summary of optimal portfolio results in objective function 3 of the genetic algorithm in Mode 2
    • Table 17. The contribution of each sector to the objective function 1 of the genetic algorithm in Mode 3
    • Table 18. Summary of optimal results in objective function 1 of the genetic algorithm in Mode 3
    • Table 19. The contribution of each sector to the objective function 2 of the genetic algorithm in Mode 3
    • Table 20. Summary of optimal portfolio results in objective function 2 of the genetic algorithm in Mode 3
    • Table 21. The contribution of each sector to the objective function 3 of the genetic algorithm in Mode 3
    • Table 22. Summary of optimal portfolio results in objective function 3 of the genetic algorithm in Mode 3
    • Conceptualization
      Abouzar Nahvi, Mohammad Ghorbani, Mahmoud Sabouhi Sabouni, Arash Dourandish
    • Data curation
      Abouzar Nahvi
    • Formal Analysis
      Abouzar Nahvi, Mohammad Ghorbani, Mahmoud Sabouhi Sabouni, Arash Dourandish
    • Investigation
      Abouzar Nahvi, Mohammad Ghorbani, Mahmoud Sabouhi Sabouni, Arash Dourandish
    • Methodology
      Abouzar Nahvi, Mohammad Ghorbani, Mahmoud Sabouhi Sabouni, Arash Dourandish
    • Resources
      Abouzar Nahvi
    • Software
      Abouzar Nahvi, Mohammad Ghorbani, Mahmoud Sabouhi Sabouni, Arash Dourandish
    • Validation
      Abouzar Nahvi, Mohammad Ghorbani, Mahmoud Sabouhi Sabouni, Arash Dourandish
    • Visualization
      Abouzar Nahvi, Mohammad Ghorbani, Mahmoud Sabouhi Sabouni, Arash Dourandish
    • Writing – original draft
      Abouzar Nahvi
    • Writing – review & editing
      Abouzar Nahvi, Mohammad Ghorbani, Mahmoud Sabouhi Sabouni, Arash Dourandish
    • Project administration
      Mohammad Ghorbani
    • Supervision
      Mohammad Ghorbani