Artificial Intelligence for Quantitative Finance: A RAG-Augmented Multi-Agent Framework for Robust Equity Strategy Discovery
Abstract
This paper introduces an AI-driven multi-agent framework for automated quantitative strategy generation and validation, integrating large language model (LLM) agents with Retrieval-Augmented Generation (RAG) to enhance factual accuracy and research reliability. The system orchestrates specialized agents for market analysis, financial knowledge retrieval, feature engineering, strategy construction, backtesting, and performance interpretation, establishing a comprehensive autonomous investment research pipeline. By grounding each agent's reasoning in a curated financial knowledge base, the framework mitigates hallucination risks, improves decision consistency, and provides transparent explanations of trading logic and risk characteristics. Experimental evaluation on simulated equity market data demonstrates that the RAG-enhanced multi-agent system achieves superior performance, generating a +3.25% return with improved consistency compared to single-agent (+2.15%) and non-RAG multi-agent (+1.82%) variants. The RAG-augmented approach exhibits higher win rates (51.2% vs 48-49%) and more robust risk-adjusted returns, validating its effectiveness in producing interpretable and reliable trading strategies. These results underscore the transformative potential of knowledge-grounded multi-agent AI systems in modern quantitative finance, while highlighting the framework's adaptability across different market environments without dependency on specific asset classes or historical…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.