Volume 11, Issue 4 (Fall 2016)                   J. Mon. Ec. 2016, 11(4): 375-400 | Back to browse issues page

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Fekri R, Amiri M, Sajjad R, Golestaneh R. Optimization of Bank Portfolio Investment Decision Considering Resistive Economy . J. Mon. Ec.. 2016; 11 (4) :375-400
URL: http://jme.mbri.ac.ir/article-1-263-en.html
1- Department of Industrial Engineering, Payamenoor University
2- Faculty of Management & Accounting, Allameh Tabatabaei University
3- Department of Financial Engineering, University of Science and Culture
Abstract:   (2737 Views)

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.

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Type of Study: Original Research - Theoric | Subject: Monetary Economics
Received: 4 Apr 2018 | Accepted: 4 Sep 2018 | Published: 9 Dec 2018

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