Decentralized Robust Portfolio Optimization Based on Cooperative-Competitive Multiagent Systems

Man Fai Leung; Jun Wang; Duan Li

doi:10.1109/TCYB.2021.3088884

Decentralized Robust Portfolio Optimization Based on Cooperative-Competitive Multiagent Systems

Man Fai Leung
, Jun Wang
, Duan Li

Hong Kong Metropolitan University

Research output: Contribution to journal › Article › peer-review

34 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 language	English
Pages (from-to)	12785-12794
Number of pages	10
Journal	IEEE Transactions on Cybernetics
Volume	52
Issue number	12
DOIs	https://doi.org/10.1109/TCYB.2021.3088884
Publication status	Published - 1 Dec 2022

Keywords

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

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10.1109/TCYB.2021.3088884

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title = "Decentralized Robust Portfolio Optimization Based on Cooperative-Competitive Multiagent Systems",

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.",

keywords = "Conditional value-at-risk (CVaR), decentralized robust portfolio selection, distributed minimax optimization, multiagent systems (MASs)",

author = "Leung, \{Man Fai\} and Jun Wang and Duan Li",

note = "Funding Information: This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China, under Grant 11208517, Grant 11202019, and Grant 11200219; in part by the Open University of Hong Kong Research Grant (No. 2020/1.4); and in part by the Laboratory for AI-Powered Financial Technologies. Publisher Copyright: {\textcopyright} 2013 IEEE.",

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N1 - Funding Information: This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China, under Grant 11208517, Grant 11202019, and Grant 11200219; in part by the Open University of Hong Kong Research Grant (No. 2020/1.4); and in part by the Laboratory for AI-Powered Financial Technologies. Publisher Copyright: © 2013 IEEE.

PY - 2022/12/1

Y1 - 2022/12/1

N2 - 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.

AB - 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.

KW - Conditional value-at-risk (CVaR)

KW - decentralized robust portfolio selection

KW - distributed minimax optimization

KW - multiagent systems (MASs)

UR - https://www.scopus.com/pages/publications/85110873566

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DO - 10.1109/TCYB.2021.3088884

M3 - Article

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JO - IEEE Transactions on Cybernetics

JF - IEEE Transactions on Cybernetics

IS - 12

ER -

Decentralized Robust Portfolio Optimization Based on Cooperative-Competitive Multiagent Systems

Abstract

Keywords

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