AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions

• URL: http://arxiv.org/abs/2508.11152v1
• Date: Fri, 15 Aug 2025 01:49:56 GMT
• Title: AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions
• Authors: Tianjiao Zhao, Jingrao Lyu, Stokes Jones, Harrison Garber, Stefano Pasquali, Dhagash Mehta,
• Abstract summary: Multi-agent collaboration has emerged as a promising approach to solve complex challenges.<n>This study investigates the application of role-based multi-agent systems to support stock selection in equity research and portfolio management.
• Score: 1.1957417530954946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The field of artificial intelligence (AI) agents is evolving rapidly, driven by the capabilities of Large Language Models (LLMs) to autonomously perform and refine tasks with human-like efficiency and adaptability. In this context, multi-agent collaboration has emerged as a promising approach, enabling multiple AI agents to work together to solve complex challenges. This study investigates the application of role-based multi-agent systems to support stock selection in equity research and portfolio management. We present a comprehensive analysis performed by a team of specialized agents and evaluate their stock-picking performance against established benchmarks under varying levels of risk tolerance. Furthermore, we examine the advantages and limitations of employing multi-agent frameworks in equity analysis, offering critical insights into their practical efficacy and implementation challenges.

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