Korea Advanced Institute of Science and Technology · 経済学
Professor Woo Chang Kim's research lab specializes in the intersection of financial econometrics, artificial intelligence, and data science, focusing on enhancing financial literacy, modeling politically influenced stock dynamics, and improving portfolio evaluation through advanced statistical and machine learning techniques. The lab develops explainable AI (xAI) frameworks to interpret complex financial models, particularly in the context of South Korea’s financial markets and pension systems. It also applies network analysis and graph embeddings to predict ETF price movements and uncover hidden market structures. A key emphasis is on creating interpretable, data-driven solutions that align with real-world regulatory and institutional frameworks.
Figures are computed from collected data and may differ slightly.
In 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
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