Information Retrieval in Finance: Industry and Academic Perspectives on Innovation
Information retrieval (IR) plays a critical role in financial decision-making across investment research, trading, risk management, and reporting. With the rise of large language models (LLMs), IR systems have evolved to support more natural, context-aware workflows. In this tutorial, we survey recent advances in applying IR and LLM technologies in finance, covering agent-based simulations, investor recommender systems, retrieval-augmented research management, and LLM-driven portfolio construction. We highlight practical challenges and propose future research directions at the intersection of IR, LLMs, and financial innovation. More materials can be found at http://irfin.nlpfin.com/.
Authors
- Chanyeol Choi (ORCID: https://orcid.org/0000-0003-3304-3253)
- Richard McCreadie (ORCID: https://orcid.org/0000-0002-2751-2087)
- Javier Sanz-Cruzado (ORCID: https://orcid.org/0000-0002-7829-5174)
- Alejandro Lopez-Lira (ORCID: https://orcid.org/0000-0002-5133-7776)
- Yongjae Lee (ORCID: https://orcid.org/0000-0002-5411-4340)
- Chung-Chi Chen (ORCID: https://orcid.org/0000-0003-3680-9277)
Institutions
- University of Florida (US)
- Arthur Ashe Learning Center (US)
- Ulsan National Institute of Science and Technology (KR)
- National Institute of Advanced Industrial Science and Technology (JP)
- University of Glasgow (GB)
Publication Details
- Published
- 2025-07-13
- DOI
- https://doi.org/10.1145/3726302.3731685
- Primary Topic
- Stock Market Forecasting Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institute of Advanced Industrial Science and Technology