Decentralized Robust Portfolio Optimization Based on Cooperative-Competitive Multiagent Systems
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 12785-12794 |
Journal / Publication | IEEE Transactions on Cybernetics |
Volume | 52 |
Issue number | 12 |
Online published | 14 Jul 2021 |
Publication status | Published - Dec 2022 |
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Abstract
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
Citation Format(s)
Decentralized Robust Portfolio Optimization Based on Cooperative-Competitive Multiagent Systems. / Leung, Man-Fai; Wang, Jun; Li, Duan.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 12, 12.2022, p. 12785-12794.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 12, 12.2022, p. 12785-12794.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review