Decentralized Robust Portfolio Optimization Based on Cooperative-Competitive Multiagent Systems

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

8 Scopus Citations
View graph of relations


Original languageEnglish
Pages (from-to)12785-12794
Journal / PublicationIEEE Transactions on Cybernetics
Issue number12
Online published14 Jul 2021
Publication statusPublished - Dec 2022


This article addresses decentralized robust portfolio optimization based on multiagent systems. Decentralized robust portfolio optimization is first formulated as two distributed minimax optimization problems in a Markowitz return-risk framework. Cooperative-competitive multiagent systems are developed and applied for solving the formulated problems. The multiagent systems are shown to be able to reach consensuses in the expected stock prices and convergence in investment allocations through both intergroup and intragroup interactions. Experimental results of the multiagent systems with stock data from four major markets are elaborated to substantiate the efficacy of multiagent systems for decentralized robust portfolio optimization.

Research Area(s)

  • Conditional value-at-risk (CVaR), decentralized robust portfolio selection, distributed minimax optimization, Investment, Multi-agent systems, multiagent systems (MASs), Optimization, Portfolios, Reactive power, Uncertainty, Urban areas