Financial Engineering Laboratory is a research group in the department of Industrial and Systems Engineering at KAIST.
Welcome to KAIST Financial Engineering Lab.
Latest Publications
- Insu Choi*, Woosung Koh*, Gimin Kang, Yuntae Jang, & Woo Chang Kim (2024). Encoding Temporal Statistical-space Priors via Augmented Representation under Data Scarcity. 3rd International Workshop on Spatio-Temporal Reasoning and Learning (STRL 2024) at International Joint Conference on Artificial Intelligence (IJCAI) 2024. CEUR-WS.org.
- Minsu Kim*, Joohwan Ko*, Taeyoung Yun*, Dinghuai Zhang, Ling Pan, Woo Chang Kim, Jinkyoo Park, Emmanuel Bengio, Yoshua Bengio (2024), “Learning to Scale Logits for Temperature-Conditional GFlowNet”, International Conference on Machine Learning (ICML)
- Joohwan Ko*, Kyurae Kim*, Woo Chang Kim, Jacob R. Gardner (2024), “Provably Scalable Black-Box Variational Inference with Structured Variational Families”, International Conference on Machine Learning (ICML)
- Insu Choi and Woo Chang Kim (2024). Practical Forecasting of Risk Boundaries for Industrial Metals and Critical Minerals via Statistical Machine Learning Techniques. International Review of Financial Analysis.
- Guhyuk Chung, Yongjae Lee+, and Woo Chang Kim+ (2024). Neural Marked Hawkes Process for Limit Order Book Modeling. Paper presented at the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD).
- Woosung Koh*, Insu Choi*, Yuntae Jang*, Gimin Kang, and Woo Chang Kim (2024). Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series. Paper presented at the AI4TS: AI for Time Series Analysis: Theory, Algorithms, and Application Workshop at AAAI 2024.
- Insu Choi*, Woosung Koh*, Bonwoo Koo*, and Woo Chang Kim (2024). Network-based Exploratory Data Analysis and Explainable Clustering for Financial Customer Profiling. Engineering Applications of Artificial Intelligence.
- Insu Choi and Woo Chang Kim (2024). Enhancing Exchange-Traded Fund Price Predictions: Insights from Information-Theoretic Networks and Node Embeddings. Entropy, 26(1), 70.
- Insu Choi, Jihye Kim, and Woo Chang Kim (2024). An Explainable Prediction for Dietary-Related Diseases via Language Models. Nutrients, 16(5), 686.
- Sanghyeon Bae, Yongjae Lee, Woo Chang Kim, Jang Ho Kim, and Frank J. Fabozzi (2023). Goal-based investing with goal postponement: Multistage stochastic mixed-integer programming approach. Annals of Operations Research. [Tentatively accepted]
- Insu Choi and Woo Chang Kim (2023). Enhancing Financial Literacy in South Korea: Integrating AI and Data Visualization to Understand Financial Instruments’ Interdependencies. Societal Impacts.
- Insu Choi and Woo Chang Kim (2023). Estimating Historical Downside Risks of Global Financial Market Indices via Inflation Rate-Adjusted Dependence Graphs. Research in International Business and Finance.
- Sanghyeon Bae, Yongjae Lee, and Woo Chang Kim (2023). Optimal Portfolio Choice of Couples with Tax-deferred Accounts and Survival-contingent Products. Quantitative Finance.
- Yongjae Lee, John R. J. Thompson, Jang Ho Kim, Woo Chang Kim, and Francesco A. Fabozzi (2023). An Overview of Machine Learning for Asset Management. Journal of Portfolio Management.
- Jang Ho Kim, Woo Chang Kim, Yongjae Lee, Bong-Geun Choi, and Frank J. Fabozzi (2023). Robustness in Portfolio Optimization. Journal of Portfolio Management.
- Jinkyu Lee, Do-Gyun Kwon, Yongjae Lee, Jang Ho Kim, and Woo Chang Kim (2023). Large-scale Financial Planning via a Partially Observable Stochastic Dual Dynamic Programming Framework. Quantitative Finance.
- Dongyeol Lee and Woo Chang Kim (2023). Optimal Intertemporal Liquidation of Institutional Investors with Cash Requirements and Viable Loans. European Journal of Finance.
- Chanyeong Kim, Jong Woong Park, Hyunglip Bae, and Woo Chang Kim (2023). Transformer-based Stagewise Decomposition for Large-Scale Multistage Stochastic Optimization. Presented at the International Conference on Machine Learning (ICML) (Oral Presentation).
- Hyunglip Bae, Jinkyu Lee, Woo Chang Kim, and Yongjae Lee (2023). Deep Value Function Networks for Large-Scale Multistage Stochastic Programming Problems. Presented at the 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023).
- Joohwan Ko, Michael Poli, Stefano Massarolli, and Woo Chang Kim (2023). Multilevel Approach to Efficient Gradient Calculation in Stochastic Systems. Presented at the ICLR 2023 Workshop on Physics for Machine Learning.
- Hyeongwoo Kong, Wonje Yun, and Woo Chang Kim (2023). Tracking customer risk aversion. Finance Research Letters.
- Insu Choi, Myounggu Lee, Hyejin Kim, and Woo Chang Kim (2023). Elucidating Directed Statistical Dependencies: Investigating Global Financial Market Indices' Influence on Korean Short Selling Activities. Pacific-Basin Finance Journal.
- Jang Ho Kim, Yongjae Lee, Woo Chang Kim, and Frank J. Fabozzi (2022), "Goal-Based Investing Based on Multi-Stage Robust Portfolio Optimization", Annals of Operations Research.
- Insu Choi, Jihye Kim, and Woo Chang Kim (2022), "Dietary Pattern Extraction Using Natural Language Processing Techniques", Frontiers in Nutrition, 281.
- Insu Choi and Woo Chang Kim (2022), “Analyzing and Utilizing Thematic Stocks based on Text Mining Techniques and Information Flow-Based Networks – An Example of the Republic of Korea’s Mask-Themed Stocks”, Industrial Engineering and Management Systems, 21(2), 244-266.
- Munki Chung, Yongjae Lee, Jang Ho Kim, Woo Chang Kim and Frank J. Fabozzi (2022), “The effects of errors in means, variances, and correlations on the mean-variance framework”, Quantitative Finance.
- Jinkyu Lee, Sanghyeon Bae, Woo Chang Kim, and Yongjae Lee (2022), “Value Function Gradient Learning for Large-Scale Multistage Stochastic Programming Problems”, European Journal of Operational Research.
- Insu Choi and Woo Chang Kim (2022), "Predicting Fluctuations Based on Nonlinear Network Using Return Data of Global Financial Marketg Indices", KEA 70th Aniversary International Conference.
- Guhyuk Chung, Munki Chung, Yongjae Lee, and Woo Chang Kim (2022), “Market Making under Order Stacking Framework: A Deep Reinforcement Learning Approach”, ICAIF '22: 3rd ACM International Conference on AI in Finance.
- Insu Choi, Wonje Yun, and Woo Chang Kim (2022), "Improving Data Efficiency for Analyzing Global Exchange Rate Fluctuations Based on Nonlinear Causal Network-Based Clustering", Annals of Operations Research.
- Hyeongwoo Kong, Wonje Yun, Weonyoung Joo, Ju-Hyun Kim, Kyoung-Kuk Kim, Il-Chul Moon, and Woo Chang Kim (2022), "Constructing a personalized recommender system for life insurance products with machine-learning techniques", Intelligent Systems in Accounting, Finance and Management.
