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

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  • Tianjiao Zhao
  • Jingrao Lyu
  • Stokes Jones
  • Harrison Garber
  • Stefano Pasquali
  • Dhagash Mehta

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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  • Tianjiao Zhao & Jingrao Lyu & Stokes Jones & Harrison Garber & Stefano Pasquali & Dhagash Mehta, 2025. "AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions," Papers 2508.11152, arXiv.org.
    • Handle: RePEc:arx:papers:2508.11152
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    References listed on IDEAS

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    1. Yuqi Nie & Yaxuan Kong & Xiaowen Dong & John M. Mulvey & H. Vincent Poor & Qingsong Wen & Stefan Zohren, 2024. "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges," Papers 2406.11903, arXiv.org.
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