QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance

2025-09-25

AI Summary

Quantitative finance research heavily relies on unstructured textual sources—such as earnings call transcripts and analyst reports—but existing large language models (LLMs) and retrieval-augmented generation (RAG) methods suffer from critical limitations in temporal precision, evidence traceability, and integration into analytical workflows. To address these challenges, we propose QuantKIR: an intelligent knowledge extraction and retrieval framework tailored for quantitative finance. QuantKIR employs a two-stage architecture: (1) fine-grained structuring via multimodal parsing (text, tables, formulas), adaptive summarization, and domain-specific tagging; and (2) knowledge-enhanced retrieval combining semantic search, cross-source multi-hop reasoning, and knowledge-aware generation to improve interpretability and accuracy. Its core innovation lies in domain-specific contextual engineering, enabling auditable and traceable knowledge services. User studies demonstrate that QuantKIR significantly outperforms baseline methods in factual accuracy and research efficiency, validating its effectiveness and practical utility in financial applications.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Quantitative research increasingly relies on unstructured financial content such as filings, earnings calls, and research notes, yet existing LLM and RAG pipelines struggle with point-in-time correctness, evidence attribution, and integration into research workflows. To tackle this, We present QuantMind, an intelligent knowledge extraction and retrieval framework tailored to quantitative finance. QuantMind adopts a two-stage architecture: (i) a knowledge extraction stage that transforms heterogeneous documents into structured knowledge through multi-modal parsing of text, tables, and formulas, adaptive summarization for scalability, and domain-specific tagging for fine-grained indexing; and (ii) an intelligent retrieval stage that integrates semantic search with flexible strategies, multi-hop reasoning across sources, and knowledge-aware generation for auditable outputs. A controlled user study demonstrates that QuantMind improves both factual accuracy and user experience compared to unaided reading and generic AI assistance, underscoring the value of structured, domain-specific context engineering for finance.

Problem

• Extracting structured knowledge from unstructured financial documents
• Ensuring point-in-time correctness and evidence attribution
• Integrating domain-specific retrieval into quantitative research workflows

Innovation

• Two-stage architecture for knowledge extraction and retrieval
• Multi-modal parsing and adaptive summarization for scalability
• Semantic search with multi-hop reasoning for auditable outputs