11 papers
When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis
Hoyoung Lee, Suhwan Park, Seunghan Lee +15
Financial decision-makers face more information than they can directly inspect, making context compression necessary. Yet when large language models (LLMs) compress financial sourc…
Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data
Suhwan Park, Hoyoung Lee, Zhangyang Wang +5
Demand for personalized financial advising is growing, but consistent advisor expertise is difficult to obtain, scale, and encode in LLM systems. Simple persona prompts rarely spec…
FinTexTS: Financial Text-Paired Time-Series Dataset via Semantic-Based and Multi-Level Pairing
Jaehoon Lee, Suhwan Park, Taeyoon Lim +9
The financial domain involves a variety of important time-series problems. Recently, time-series analysis methods that jointly leverage textual and numerical information have gaine…
From Text to Alpha: Can LLMs Track Evolving Signals in Corporate Disclosures?
Chanyeol Choi, Yoon Kim, Yu Yu +10
Natural language processing (NLP) has been widely used in quantitative finance, but traditional methods often struggle to capture rich narratives in corporate disclosures, leaving…
Forecasting Future Language: Context Design for Mention Markets
Sumin Kim, Jihoon Kwon, Yoon Kim +9
Mention markets, a type of prediction market in which contracts resolve based on whether a specified keyword is mentioned during a future public event, require accurate probabilist…
LLM as a Risk Manager: LLM Semantic Filtering for Lead-Lag Trading in Prediction Markets
Sumin Kim, Minjae Kim, Jihoon Kwon +7
Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead-lag relationships remains…