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arXiv · 2508.11152

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

Tianjiao Zhao·Jingrao Lyu·Stokes Jones·Harrison Garber·Stefano Pasquali·Dhagash Mehta
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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. 2025-08-15. AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions. https://arxiv.org/abs/2508.11152

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