6 papers
FinAI Data Assistant: LLM-based Financial Database Query Processing with the OpenAI Function Calling API
Juhyeong Kim, Yejin Kim, Youngbin Lee +1
We present FinAI Data Assistant, a practical approach for natural-language querying over financial databases that combines large language models (LLMs) with the OpenAI Function Cal…
Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization
Juhyeong Kim, Sungyoon Choi, Youngbin Lee +3
We propose Decision by Supervised Learning (DSL), a practical framework for robust portfolio optimization. DSL reframes portfolio construction as a supervised learning problem: mod…
LLM-Enhanced Black-Litterman Portfolio Optimization
Youngbin Lee, Yejin Kim, Juhyeong Kim +2
The Black-Litterman model addresses the sensitivity issues of tra- ditional mean-variance optimization by incorporating investor views, but systematically generating these views re…
GuruAgents: Emulating Wise Investors with Prompt-Guided LLM Agents
Yejin Kim, Youngbin Lee, Juhyeong Kim +1
This study demonstrates that GuruAgents, prompt-guided AI agents, can systematically operationalize the strategies of legendary investment gurus. We develop five distinct GuruAgent…
A Temporal Graph Network Framework for Dynamic Recommendation
Yejin Kim, Youngbin Lee, Vincent Yuan +2
Recommender systems, crucial for user engagement on platforms like e-commerce and streaming services, often lag behind users' evolving preferences due to static data reliance. Afte…
Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Mean-Variance Efficient Sampling
Youngbin Lee, Yejin Kim, Javier Sanz-Cruzado +2
Recommender systems can be helpful for individuals to make well-informed decisions in complex financial markets. While many studies have focused on predicting stock prices, even ad…