김우창 교수
Woo Chang Kim
KAIST 산업및시스템공학과 · 경제학
연구실 소개
우우 창 김 교수의 연구실은 금융시장의 복잡한 구조를 해석하고 이해할 수 있도록 통계적 상관관계와 설명 가능한 인공지능(xAI) 기반의 시각화 기법을 활용한 금융리터러시 향상에 초점을 맞추고 있습니다. 특히 한국의 금융 상품 이해 부족 문제를 해결하고자, 정치적 요소가 반영된 주식 네트워크 분석, ETF 가격 예측을 위한 네트워크 기반 기계학습 모델링, 그리고 연금 및 소비 패턴 변화 분석을 통해 실생활 금융 의사결정 지원에 기여하고자 합니다. 연구는 실증적 데이터 기반의 정교한 분석과 AI의 해석 가능성에 중점을 두어 실용성과 신뢰성을 확보합니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15In South Korea, the lack of understanding of financial products has emerged as a significant challenge, contributing to a gap in financial literacy. This research proposes a novel approach to bridge this gap by employing statistical interdependence and explainable AI (xAI) to enhance comprehension of the interconnectedness of economic variables. By translating complex financial information into intuitive visual formats, the methodology empowers individuals to make informed decisions. Collaborati
Politically-themed stocks mainly refer to stocks that benefit from the policies of politicians. This study gave the empirical analysis of the politically-themed stocks in the Republic of Korea and constructed politically-themed stock networks based on the Republic of Korea's politically-themed stocks, derived mainly from politicians. To select politically-themed stocks, we calculated the daily politician sentiment index (PSI), which means politicians' daily reputation using politicians' search v
Click to increase image sizeClick to decrease image size Notes †Both defined benefit and defined contribution pension plans are included. When retirement plans in public sectors are included, the scope of style investment becomes even greater, covering approximately half of 10 trillion dollars (Board of Governors of the Federal Reserve System 2008). ‡See, for example, Fama and French (Citation1993, 1995, 1996), Lakonishok et al. (Citation1994), and Teo and Woo (Citation2004). †For instance, Hens
In this study, we observed the changes in dietary patterns among Korean adults in the previous decade. We evaluated dietary intake using 24-h recall data from the fourth (2007-2009) and seventh (2016-2018) Korea National Health and Nutrition Examination Survey. Machine learning-based methodologies were used to extract these dietary patterns. Particularly, we observed three dietary patterns from each survey similar to the traditional and Western dietary patterns in 2007-2009 and 2016-2018, respec
In this study, we propose a uniformly distributed random portfolio as an alternative benchmark for portfolio performance evaluation. The uniformly distributed random portfolio is analogous to an enumeration of all feasible portfolios without any prior on the market. Therefore, the relative ranking of a portfolio can be evaluated without peer group information. We derive a closed-form expression for the probability distribution of the Sharpe ratio of a uniformly distributed random portfolio, and
In the complex landscape of financial markets, accurately predicting Exchange-Traded Fund (ETF) price movements requires advanced methodologies. This research introduces a practical approach that integrates network analysis with graph embeddings, specifically utilizing Node2Vec, to enhance financial prediction models' performance and interpretability . By representing the intricate relationships within financial markets in a lower-dimensional space, we improve the efficiency of AI-driven predict