1 citations · 2 across the 9 of their papers we have counts for
9 papers
Making Alternative Data Work: Context-Augmented LLMs for Financial Forecasting
Jihoon Kwon, Lawrence Liu, Daekyung Park +15
When forecasting a firm's future financial performance, alternative data - data collected from non-traditional sources such as consumer transactions, web traffic, and prediction ma…
Your AI, On a Dial: Controlling Investment Bias in LLMs with a Single Neuron
Sahong Park, Suhwan Park, Hoyoung Lee +8
Large language models (LLMs) are increasingly used in investment decision-making, yet prior work shows that they exhibit systematic, model-specific investment preferences. We study…
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 advice is growing, yet current LLM-based advisors often fail to provide consistent and specialized guidance. Simple persona prompts rarely specify…
Evaluating LLMs in Finance Requires Explicit Bias Consideration
Yaxuan Kong, Hoyoung Lee, Yoontae Hwang +7
Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases can inflate performance, contami…
THEME: Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics
Hoyoung Lee, Wonbin Ahn, Suhwan Park +6
Thematic investing, which aims to construct portfolios aligned with structural trends, remains a challenging endeavor due to overlapping sector boundaries and evolving market dynam…
Your AI, Not Your View: The Bias of LLMs in Investment Analysis
Hoyoung Lee, Junhyuk Seo, Suhwan Park +5
In finance, Large Language Models (LLMs) face frequent knowledge conflicts arising from discrepancies between their pre-trained parametric knowledge and real-time market data. Thes…
Quantifying Qualitative Insights: Leveraging LLMs to Market Predict
Hoyoung Lee, Youngsoo Choi, Yuhee Kwon
Recent advancements in Large Language Models (LLMs) have the potential to transform financial analytics by integrating numerical and textual data. However, challenges such as insuf…
LLM-as-a-Judge & Reward Model: What They Can and Cannot Do
Guijin Son, Hyunwoo Ko, Hoyoung Lee +2
LLM-as-a-Judge and reward models are widely used alternatives of multiple-choice questions or human annotators for large language model (LLM) evaluation. Their efficacy shines in e…