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Predictable Relative Forward Performance Processes: Multi-Agent and Mean Field Games for Portfolio Management

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  • Gechun Liang
  • Moris S. Strub
  • Yuwei Wang

Abstract

We consider a new framework of predictable relative forward performance processes (PRFPP) to study portfolio management within a competitive environment. Each agent trades a distinct stock following a binomial distribution with probabilities for a positive return depending on the market regime characterized by a binomial common noise. For both the finite population and mean field games, we construct and analyse PRFPPs for initial data of the CARA class along with the associated equilibrium strategies. We find that relative performance concerns do not necessarily lead to more investment in the risky asset. Under some parameter constellations, agents short a stock with positive expected excess return.

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  • Gechun Liang & Moris S. Strub & Yuwei Wang, 2023. "Predictable Relative Forward Performance Processes: Multi-Agent and Mean Field Games for Portfolio Management," Papers 2311.04841, arXiv.org, revised Dec 2023.
    • Handle: RePEc:arx:papers:2311.04841
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    References listed on IDEAS

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    Cited by:

    1. Bahman Angoshtari & Shida Duan, 2024. "Rank-Dependent Predictable Forward Performance Processes," Papers 2403.16228, arXiv.org.

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