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.
@article{arxiv.2508.11152,
title = {AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions},
author = {Tianjiao Zhao and Jingrao Lyu and Stokes Jones and Harrison Garber and Stefano Pasquali and Dhagash Mehta},
journal= {arXiv preprint arXiv:2508.11152},
year = {2025}
}