{"ID":2881760,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.11152","arxiv_id":"2508.11152","title":"AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions","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.","short_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 agen...","url_abs":"https://arxiv.org/abs/2508.11152","url_pdf":"https://arxiv.org/pdf/2508.11152v1","authors":"[\"Tianjiao Zhao\",\"Jingrao Lyu\",\"Stokes Jones\",\"Harrison Garber\",\"Stefano Pasquali\",\"Dhagash Mehta\"]","published":"2025-08-15T01:49:56Z","proceeding":"q-fin.ST","tasks":"[\"q-fin.ST\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}