Decentralized Robust Portfolio Optimization Based on Cooperative-Competitive Multiagent Systems
将分散鲁棒投资组合优化建模为两个分布式极小极大优化问题，用合作竞争多智能体系统求解，实验用四大市场股票数据验证了系统在预期股价和投资分配上的收敛效果。
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.