Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Artificial Intelligence-Based Multi-Objective Stochastic Optimization Model for Risk Management in Cryptocurrency Investments

Document Type : Research Paper

Authors
Somayyeh mohammadpour 1
Maryam Rahmaty 2
Fereydoon Rahnama roodposhti 3
reza ehteshamrasi 4
1 PhD student, industrial management department, Science and Research Branch, Islamic Azad University, Tehran,Iran.
2 Department of Management, Cha.C., Islamic Azad University, Chalus, Iran
3 Professor of all finance and accounting department, science and Research Branch, Islamic Azad University, Tehran, Iran
4 Assistant Professor Assistant Prof, Department Of Industrial management, Qazvin Branch, Islamic Azad University , Qazvin, Iran.
Abstract
In this study, a multi-objective stochastic optimization model is presented for managing investment portfolios in the cryptocurrency market. The main goal of this model is to maximize returns and minimize investment risk by considering realistic constraints such as budget and asset liquidity. To solve the proposed model, two meta-heuristic algorithms, Greedy Man Optimization (GMOA) and Non-Dominated Genetic Algorithm-II (NSGA-II), have been used. The performance of these algorithms has been investigated in 10 different problem instances with different sizes and their results have been compared in terms of returns, risk, and computational time. Also, sensitivity analysis has been performed to changes in key parameters such as the expected rate of return. The results showed that the proposed model and the algorithms used are effective tools for risk management and portfolio optimization in volatile cryptocurrency markets. Suggestions for future research are also provided.
Keywords
Cryptocurrency portfolio optimization
multi-objective stochastic model
risk management
greedy man optimization algorithm
NSGA-II
Subjects
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Journal of Industrial and Systems Engineering
Volume 17, Issue 3 - Serial Number 3
Summer 2025
Pages 154-166

  • Receive Date 04 February 2024
  • Revise Date 03 April 2024
  • Accept Date 11 May 2024