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Large Language Model Agents for Investment Management: Foundations, Benchmarks, and Research Frontiers
Abstract
Recent advances in Large Language Models (LLMs) have triggered a new wave of intelligent financial agents capable of complex reasoning, tool use, and autonomous decision-making. This survey presents a comprehensive review of LLM-based agents in the context of investment and trading, focusing on applications such as portfolio optimization, risk management, information retrieval, and automated strategy generation. We systematically categorize the literature by use case and architectural innovations including multi-agent collaborations, reflection mechanisms, and tool-augmented pipelines. Additionally, we review emerging evaluation frameworks and benchmark datasets tailored to finance-specific agent tasks. The survey identifies current trends, technical limitations, and open challenges related to robustness, explainability, and real-world deployment. We conclude with emerging directions for building more capable, adaptive, and trustworthy financial AI agents aligned with the demands of modern investment ecosystems.
Curator notes
Discovery status: round1_promoted_seed_for_round2. Source links: A Comprehensive Review of Gen AI Agents: Applications and Frameworks in Finance, Investments and Risk Domains; A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist; A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges; Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews; Automate Strategy Finding with LLM in Quant investment; Can LLM-Based Financial Investing St