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<p>Traditional portfolio optimization methods face significant limitations in capturing complex asset relationships and adapting to dynamic market conditions. This paper proposes a novel graph attention-based heterogeneous multi-agent deep reinforcement learning framework that addresses these challenges through innovative integration of graph neural networks and specialized agent architectures. The framework employs graph attention networks to model time-varying asset correlations and dependencies, while utilizing three heterogeneous agents specialized in risk assessment, return prediction, and market environment perception. An adaptive optimization strategy dynamically adjusts parameters based on real-time market conditions and regime changes. Comprehensive experiments on S&amp;P 500, NASDAQ 100, and Russell 2000 datasets demonstrate superior performance, achieving 16.8% annualized returns, 1.34 Sharpe ratio, and 8.2% maximum drawdown, significantly outperforming traditional mean–variance optimization, equal-weight portfolios, and existing deep learning approaches. Ablation studies confirm the critical contributions of each framework component, while sensitivity analysis validates robustness across varying market conditions. The proposed framework represents a significant advancement in computational finance, offering enhanced adaptability and risk management capabilities for modern portfolio optimization challenges.</p>

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Graph attention-based heterogeneous multi-agent deep reinforcement learning for adaptive portfolio optimization

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  • 出版模式：

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  • 发表日期：

    2025年12月27日

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  • Bing Zhang

摘要

Traditional portfolio optimization methods face significant limitations in capturing complex asset relationships and adapting to dynamic market conditions. This paper proposes a novel graph attention-based heterogeneous multi-agent deep reinforcement learning framework that addresses these challenges through innovative integration of graph neural networks and specialized agent architectures. The framework employs graph attention networks to model time-varying asset correlations and dependencies, while utilizing three heterogeneous agents specialized in risk assessment, return prediction, and market environment perception. An adaptive optimization strategy dynamically adjusts parameters based on real-time market conditions and regime changes. Comprehensive experiments on S&P 500, NASDAQ 100, and Russell 2000 datasets demonstrate superior performance, achieving 16.8% annualized returns, 1.34 Sharpe ratio, and 8.2% maximum drawdown, significantly outperforming traditional mean–variance optimization, equal-weight portfolios, and existing deep learning approaches. Ablation studies confirm the critical contributions of each framework component, while sensitivity analysis validates robustness across varying market conditions. The proposed framework represents a significant advancement in computational finance, offering enhanced adaptability and risk management capabilities for modern portfolio optimization challenges.