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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Jonson Manurung
Indonesia Defence University
Computer Science & IT Engineering

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Dynamic portfolio optimization using differential evolution: a Markowitz modern portfolio theory approach Hengki Tamando Sihotang; Jonson Manurung; Bambang Saras Yulistiawan; Galih Prakoso Rizky A.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2449-2458

Abstract

An optimal investment portfolio is one of the main focuses in the financial world to minimize risk while maximizing returns. However, the challenge that arises is how to choose the right asset allocation amidst dynamic market uncertainty. This study aims to optimize portfolios based on Markowitz modern portfolio theory (MPT) by using the differential evolution (DE) algorithm as an optimization technique. The data used includes stocks, bonds, and other financial instruments taken from trusted data sources, such as Bloomberg and Yahoo finance, with an observation period of the last five years. The results show that this approach succeeds in finding optimal portfolios with the right asset weights, higher expected returns, and minimized risks compared to conventional approaches. The implication of this research is that the DE algorithm can be effectively used to address portfolio optimization problems in complex and volatile market environments, offering a more adaptive solution for investors to maximize their returns.
Co-Authors Bambang Saras Yulistiawan Galih Prakoso Rizky A. Hengki Tamando Sihotang