QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912606195810304 |
|---|---|
| author | Wang, Haoxue Wen, Keli Li, Yuante Qu, Qiancheng Mu, Xiangxu Shen, Xinjie Gao, Jiaqi Chang, Chenyang Xie, Chuhan Cheung, San Yu Hu, Zhuoyuan Wang, Xinyu Bi, Sirui Du, Bi'an |
| author_facet | Wang, Haoxue Wen, Keli Li, Yuante Qu, Qiancheng Mu, Xiangxu Shen, Xinjie Gao, Jiaqi Chang, Chenyang Xie, Chuhan Cheung, San Yu Hu, Zhuoyuan Wang, Xinyu Bi, Sirui Du, Bi'an |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21507 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance Wang, Haoxue Wen, Keli Li, Yuante Qu, Qiancheng Mu, Xiangxu Shen, Xinjie Gao, Jiaqi Chang, Chenyang Xie, Chuhan Cheung, San Yu Hu, Zhuoyuan Wang, Xinyu Bi, Sirui Du, Bi'an Computational Engineering, Finance, and Science 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. |
| title | QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2509.21507 |