Authors: Zhenglong Li, Vincent Tam, Kwan L. Yeung

Published on: February 01, 2024

Impact Score: 8.3

Arxiv code: Arxiv:2402.00515

Summary

• What is new: The introduction of a multi-agent and self-adaptive framework (MASA) that uses multiple reinforcement learning agents to balance portfolio returns and risks, with an additional market observer agent for trend analysis.
• Why this is important: Existing deep or reinforcement learning agents in portfolio management often focus on maximizing returns without adequately considering the potential risks, especially under volatile market conditions.
• What the research proposes: A sophisticated multi-agent reinforcement learning approach known as MASA, featuring cooperating agents to balance returns and risks, and an observer agent to provide market trend insights.
• Results: The MASA framework outperformed traditional RL-based approaches in managing portfolios across the CSI 300, Dow Jones, and SP 500 indexes over ten years, illustrating its effectiveness in a volatile market.

Technical Details

Technological frameworks used: MASA (Multi-agent and Self-adaptive framework)

Models used: Multi-agent reinforcement learning (RL)

Data used: CSI 300, Dow Jones Industrial Average, SP 500 indexes data over the past 10 years

Potential Impact

Financial services and investment firms could leverage these insights for better risk-adjusted returns. This innovation might also influence fintech companies developing trading algorithms and wealth management tools.

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