Decentralized Robust Portfolio Optimization Based on Cooperative-Competitive Multiagent Systems

• Published in: IEEE transactions on cybernetics Vol. 52; no. 12; pp. 12785 - 12794
• Main Authors: Leung, Man-Fai, Wang, Jun, Li, Duan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Allocations; Conditional value-at-risk (CVaR); decentralized robust portfolio selection; distributed minimax optimization; Investment; Minimax technique; Multi-agent systems; Multiagent systems; multiagent systems (MASs); Optimization; Portfolios; Reactive power; Robustness; Uncertainty; Urban areas
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2021.3088884

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.