@inproceedings{kundu-etal-2025-multi,
    title = "A Multi-Agent Framework for Quantitative Finance : An Application to Portfolio Management Analytics",
    author = "Kundu, Sayani  and
      Sahoo, Dushyant  and
      Li, Victor  and
      Rabowsky, Jennifer  and
      Varshney, Amit",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.55/",
    pages = "812--824",
    ISBN = "979-8-89176-333-3",
    abstract = "Machine learning and artificial intelligence have been used widely within quantitative finance. However there is a scarcity of AI frameworks capable of autonomously performing complex tasks and quantitative analysis on structured data. This paper introduces a novel Multi-Agent framework tailored for such tasks which are routinely performed by portfolio managers and researchers within the asset management industry. Our framework facilitates mathematical modeling and data analytics by dynamically generating executable code. The framework{'}s innovative multi-agent architecture includes specialized components and agents for reflection, summarization, and financial expertise which coordinate to enhance problem solving abilities. We present a comprehensive empirical evaluation on portfolio management-specific tasks, addressing a critical gap in current research. Our findings reveal that the proposed Multi-Agent framework vastly outperforms Single-Agent frameworks, demonstrating its practical utility across various task categories. By using dynamic code generation with the agent{'}s multi-step reasoning capabilities, we broaden the range of tasks that can be successfully addressed."
}