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Optimization of Bank Portfolio Investment Decision Considering Resistive Economy

Author

Listed:
  • Fekri, Roxana

    (Department of Industrial Engineering, Payamenoor University)

  • Amiri, Maghsoud

    (Faculty of Management & Accounting, Allameh Tabatabaei University)

  • Sajjad, Rasoul

    (Department of Financial Engineering, University of Science and Culture)

  • Golestaneh, Ramin

    (Department of Industrial Engineering, Payamenoor University)

Abstract

Increasing economy's resistance against the menace of sanctions, various risks, shocks, and internal and external threats are one of the main national policies which can be implemented through bank investments. Investment project selection is a complex and multi-criteria decision-making process that is influenced by multiple and often some conflicting objectives. This paper studies portfolio investment decisions in Iranian Banks. The main contribution of this paper is the creation of a project portfolio selection model that facilitates how Iranian banks would make investment decisions on proposed projects to satisfy bank profit maximization and risk minimization, while focus on national policies such as Resistance Economy Policies. The considered problem is formulated as a multi-objective integer programming model. A framework called Multi-Objective Electromagnetism-like (MOEM) algorithm, is developed to solve this NP-hard problem. To further enhance MOEM, a local search heuristic based on simulated annealing is incorporated in the algorithm. In order to demonstrate the efficiency and reliability of the proposed algorithm, a number of test are performed. The MOEM results are compared with two well-known multi-objective genetic algorithms in the literature, i.e. Non-dominated Sorting Genetic Algorithm (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA-II) based on some comparison metrics. Also, these algorithms are compared with an integer linear programming formulation for small instances. Computational experiments indicate the superiority of the MOEM over existing algorithms.

Suggested Citation

  • Fekri, Roxana & Amiri, Maghsoud & Sajjad, Rasoul & Golestaneh, Ramin, 2016. "Optimization of Bank Portfolio Investment Decision Considering Resistive Economy," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 11(4), pages 375-400, October.
  • Handle: RePEc:mbr:jmonec:v:11:y:2016:i:4:p:375-400
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    References listed on IDEAS

    as
    1. Pi-Fang Hsu & Mei-Ghing Hsu, 2008. "Optimizing the Information Outsourcing Practices of Primary Care Medical Organizations Using Entropy and TOPSIS," Quality & Quantity: International Journal of Methodology, Springer, vol. 42(2), pages 181-201, April.
    2. Huang, Xiaoxia & Xiang, Lan & Islam, Sardar M.N., 2014. "Optimal project adjustment and selection," Economic Modelling, Elsevier, vol. 36(C), pages 391-397.
    3. Robert L. Carraway & Robert L. Schmidt, 1991. "Note---An Improved Discrete Dynamic Programming Algorithm for Allocating Resources Among Interdependent Projects," Management Science, INFORMS, vol. 37(9), pages 1195-1200, September.
    4. Mavrotas, G. & Diakoulaki, D. & Caloghirou, Y., 2006. "Project prioritization under policy restrictions. A combination of MCDA with 0-1 programming," European Journal of Operational Research, Elsevier, vol. 171(1), pages 296-308, May.
    5. Medaglia, Andres L. & Graves, Samuel B. & Ringuest, Jeffrey L., 2007. "A multiobjective evolutionary approach for linearly constrained project selection under uncertainty," European Journal of Operational Research, Elsevier, vol. 179(3), pages 869-894, June.
    6. Debels, Dieter & De Reyck, Bert & Leus, Roel & Vanhoucke, Mario, 2006. "A hybrid scatter search/electromagnetism meta-heuristic for project scheduling," European Journal of Operational Research, Elsevier, vol. 169(2), pages 638-653, March.
    7. Karl Doerner & Walter Gutjahr & Richard Hartl & Christine Strauss & Christian Stummer, 2004. "Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection," Annals of Operations Research, Springer, vol. 131(1), pages 79-99, October.
    8. James C. Bean, 1994. "Genetic Algorithms and Random Keys for Sequencing and Optimization," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 154-160, May.
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    More about this item

    Keywords

    Project Portfolio Selection; Bank Investment; Resistive Economy; Multi-objective optimization; Electromagnetism-like algorithm; ɛ-constraint method.;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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