A Multi-Agent Framework for Quantitative Finance : An Application to Portfolio Management Analytics

2025-12-30 · Source: Paper Index on ACL Anthology · Field: Finance & Economics — Capital Markets & Investment Management, FinTech & Digital Financial Services · Depth: Advanced, quick

Summary

A novel Multi-Agent framework has been developed for quantitative finance, specifically targeting complex tasks and quantitative analysis on structured data within the asset management industry. This framework addresses a gap in current AI capabilities by autonomously performing tasks routinely handled by portfolio managers and researchers. It facilitates mathematical modeling and data analytics through dynamic executable code generation. The architecture incorporates specialized agents for reflection, summarization, and financial expertise, which coordinate to enhance problem-solving. Empirical evaluation on portfolio management tasks demonstrates that this Multi-Agent framework significantly outperforms Single-Agent frameworks, proving its practical utility across various task categories by combining dynamic code generation with multi-step reasoning.

Key takeaway

For AI Scientists and Research Scientists developing solutions for quantitative finance, this Multi-Agent framework offers a robust approach to automating complex analytical tasks. You should consider integrating specialized agents and dynamic code generation to improve performance over single-agent systems. This can significantly broaden the scope of financial problems your AI solutions can effectively address, enhancing efficiency and accuracy in asset management.

Key insights

A Multi-Agent framework autonomously performs complex quantitative finance tasks using dynamic code generation and specialized agents.

Principles

• Multi-agent systems enhance problem-solving in complex domains.
• Dynamic code generation broadens task applicability for AI agents.

Method

The framework uses specialized agents for reflection, summarization, and financial expertise that coordinate to generate executable code dynamically, enabling mathematical modeling and data analytics.

In practice

• Automate portfolio management analytics.
• Enhance financial research with autonomous agents.

Topics

• Multi-Agent Systems
• Quantitative Finance
• Portfolio Management
• Dynamic Code Generation

Best for: AI Scientist, Research Scientist, AI Researcher, Machine Learning Engineer, AI Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.