A Survey on Multi Agent Framework for Asset Allocation and Portfolio Optimization

The rapid evolution of global financial markets, driven by increased data availability, market volatility, and the participation of diverse investors, has intensified the complexity of asset allocation and portfolio optimization. Traditional portfolio management techniques, while well established, rely on static assumptions and centralized decision-making, limiting their effectiveness in dynamic and non-stationary market environments. Recent advances in Artificial Intelligence (AI), particularly Reinforcement Learning (RL) and Multi-Agent Systems (MAS), have opened new avenues for adaptive, decentralized, and intelligent financial decision-making. This review paper presents a systematic survey of Multi-Agent Artificial Intelligence frameworks for dynamic asset allocation and portfolio optimization. It synthesizes and analyzes recent research spanning multi-agent reinforcement learning, deep reinforcement learning, large language model (LLM)-based agents, and hybrid AI architectures applied to financial markets. The review examines agent cooperation and competition mechanisms, learning paradigms, reward formulations, and risk-aware optimization strategies employed across existing studies. Additionally, emerging trends such as hierarchical multi-agent learning, role-based agent specialization, and LLM-driven financial reasoning are discussed. By critically evaluating the strengths, limitations, and practical challenges of current methodologies, this paper identifies key research gaps related to scalability, interpretability, transaction cost modeling, and real-time deployment. The survey aims to provide a comprehensive understanding of the current landscape of multi-agent AI in portfolio management and to serve as a foundation for future research toward robust, adaptive, and intelligent financial systems.