Yoontae Hwang

yoontae.hwang@eng.ox.ac.uk / Github / Google Scholar / LinkedIn

I received my Ph.D. in Industrial Engineering from Ulsan National Institute of Science and Technology (UNIST, Korea) on August 16th, 2024. My dissertation, titled "Financial Representation Learning," was completed under the supervision of Prof. Yongjae Lee. During my doctoral studies, I published 6 papers in peer-reviewed journals and AI conferences.

Recently, I was awarded the Sejong Science Fellowship by the National Research Foundation of Korea. Since September 2024, I have been working as a (Sejong Science Fellowship) Postdoctoral Researcher hosted by Prof. Stefan Zohren at the University of Oxford where I've been focused on time-series analysis based on deep learning.

I am actively seeking academic positions starting from September 2025, with a focus on my research interests in Deep Learning, Time-series analysis, Portfolio optimization and Asset management. In my future role, I hope to continue exploring the intersection of AI and financial markets, with a particular emphasis on developing more robust predictive models.

Research Questions

How can we develop advanced algorithms that make household asset management more accessible and efficient, especially for working households that have limited capacity to grasp complex financial theory? Mainstream economic theory emphasizes financial education for asset management, yet many working households struggle to acquire and apply complex knowledge. My research question explores how individualized models—accounting for household financial status, market conditions, and personal objectives—can break down barriers, enhance decision-making, and ultimately foster a more inclusive for household asset management.

[Keywords of Interest] (AI in Finance) From a deep learning perspective, the major topics (Asset Management, Time-Series Analysis, Finance) and their subtopics outlined below are highly relevant to the research question on making household asset management. The subtopics currently under more intensive study (work in progress) in this study are marked with an asterisk (*). Please note that these keywords do not represent the full scope of my research interests.
  • Asset management
    • Decision Focused Learning (Portfolio optimization*, Order execution, Statistical arbitrage)
    • Goal-based investing* (Multi-stage portfolio optimization, Stochastic optimization)
    • Other topics (Trading strategy*, Factor investing)
  • Time-sereis analysis
    • Representation Learning (Foundation models for finance*, Regime switching model)
    • Forecasting (Irregular multivariate time series forecasting*, Nowcasting, Volatility forecasting)
    • Classification (Lead-lag detection, Stop-loss adjusted labels)
    • Learning strategy (Loss shaping constraints, Temporal domain generalization)
  • Finance
    • Hosuehold Financial Health
    • Investor Modeling (Clustering, Order flow, Synthetic data)
    • Other topics (Asset pricing, Sports betting, Market making)

Publications

J: Journal / C: Conference / *: equal contribution

[C3] Geodesic Flow Kernels for Semi-Supervised Learning on Mixed-Variable Tabular Dataset
Yoontae Hwang, Yongjae Lee
AAAI Conference on Artificial Intelligence (AAAI), 2025, Acceptance rate 24%
[paper] [code] [seminar@UNIST] [bibtex]
[C2] CAFO: Feature-Centric Explanation on Time Series Classification
Jaeho Kim, Seok-ju Hanhn, Yoontae Hwang, Junghye Lee, Seulki Lee
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024, Acceptance rate 20%
Best Poster Award, @UNIST AI Tech Workshop 2024
[paper] [code] [bibtex]
[C1] SimStock : Representation Model for Stock Similarities
Yoontae Hwang, Junhyeong Lee, Daham Kim, Seunghwan Noh, Joohwan Hong Yongjae Lee
ACM International Conference on AI in Finance (ICAIF), 2023, Acceptance rate 21%, Oral presentation
[paper] [code] [seminar@SKKU] [bibtex]
[J5] Heterogeneous Trading Behaviors of Individual Investors
Yoontae Hwang, Junpyo Park, Jang Ho Kim, Yongjae Lee, Frank J Fabozzi
Finance Research Letters (FRL), 2023, Acceptance rate 28%,
[paper] [bibtex]
[J4] Identifying household finance heterogeneity via deep clustering
Yoontae Hwang, Yongjae Lee, Frank J Fabozzi
Annals of Operations Research (ANOR), 2023, Acceptance rate 23%,
[paper] [bibtex]
[J3] Household Financial Health: A Machine Learning Approach for Data-Driven Diagnosis and Prescription
Kyeongbin Kim*, Yoontae Hwang*, Dongcheol Lim, Suhyeon Kim, Junghye Lee, Yongjae Lee
Quantitative Finance (QF), 2023, Acceptance rate 23%,
Commendation Award, @Commissioner of Statistics Korea 2020
[paper] [bibtex]
[J2] Stop-loss adjusted labels for machine learning-based trading of risky assets
Yoontae Hwang, Junpyo Park, Dong-Young Lim, Yongjae Lee
Finance Research Letters (FRL), 2023, Acceptance rate 28%,
[paper] [code] [bibtex]
[J1] A Study on the Estimation of Apartment Price Index: Focused on the Machine Learning Algorithm
Yoontae Hwang
Journal of Money & Finance (KMFA), 2019, Acceptance rate 45.71%, Domestic journal (South Korea)
[paper] [bibtex]

Working Paper

S: Sumitted / W: Work in progress / *: equal contribution

[S] Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization
Yoontae Hwang, Yaxuan Kong, Stefan Zohren, Yongjae Lee
Finance Journal, 2025.01 (intended), Acceptance rate 13%,
[paper] [code]
[S] Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management
Yoontae Hwang, Stefan Zohren, Yongjae Lee
Finance Journal, 2024.12, Acceptance rate 23%,
Best Paper Award @the Korean Academic Society of Business Administration 2024
[paper] [code] [bibtex]
[S] Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement
Yaxuan Kong, Yiyuan Yang, Yoontae Hwang, Wenjie Du, Stefan Zohren, Zhangyang Wang, Ming Jin, Qingsong Wen
AI Confernece, 2025.02, Acceptance rate 23%,
[paper] [code]
[W] Neural Networks and Deep Learning for Asset Management
Yoontae Hwang, Junhyeong Lee, Stefan Zohren, Jang Ho Kim, Yongjae Lee, Woo Chang Kim, Frank J Fabozzi
Survey paper

Work Experience

University of Oxford
Oxford, United Kingdom, 2024. 09 - Present
Postdoctoral Researcher (Sejong Science Fellowship)
Shinhan Investment Corp
Seoul, South Korea, 2020. 12 - 2021. 05
Visiting Student
Shinhan Bank
Seoul, South Korea, 2018. 12 - 2019. 03
Visiting Student
Republic of Korea Army
Yeoncheon, South Korea, 2015. 03 - 2016. 12
Sergeant (Reconnaissance Unit)

Education

Ulsan National Institute of Science and Technology (UNIST)
Ulsan, South Korea, Mar. 2020 to Aug. 2024
Ph.D in Industrial Engineering
Advisor: Yongjae Lee
Sangmyung University
Seoul, South Korea, Mar. 2014 to Feb. 2020
B.S. in Economics & Mathematics (Double Major)

Awards & Fundings

A: Awards / F: Fundings

  • [A1] Best Paper Award, 2024, Korean Academic Society of Business Administration
  • [F2] Sejong Science Fellowship (Oversea Track), 2024, National Research Foundation of Korea (NRF)
    • 70,000,000 KRW
  • [F1] Ph.D. Fellowship, 2022, National Research Foundation of Korea (NRF)
    • 40,000,000 KRW

Teaching

  • Time Series Analysis and Signal Processing, 2024 Winter (PhD Course), University of Oxford
  • AI for Finance, Spring 2022 (UNIST & KAIST), Spring 2023 (UNIST)
    • Lecture on Dimension reduction methods (SVD, Autoencoder)
    • AI practice sessions : Portfolio optimization

Services

R: Reviewer / P: Program Committee

  • Conferences: [P] ICAIF 2024 [link]
  • Conference Workshops: [P] AAAI 2025 [link], ICLR 2025 [link][pdf]
  • Journals: [R] Finance Research Letters, [R] TMLR

Random stuff

  • Latest research trends in long term time-series forecasting, 2022.09.23, Talk, [Slide]
  • UNIST-POSTECH-KAIST Data Science Competition 2022, Tutorials, [Page]

Recommended Books and Guides

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