Decentralized Robust Portfolio Optimization Based on Cooperative-Competitive Multiagent Systems

Man Fai Leung, Jun Wang, Duan Li

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)12785-12794
Number of pages10
JournalIEEE Transactions on Cybernetics
Volume52
Issue number12
DOIs
Publication statusPublished - 1 Dec 2022

Keywords

  • Conditional value-at-risk (CVaR)
  • decentralized robust portfolio selection
  • distributed minimax optimization
  • multiagent systems (MASs)

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