Research

Financial Artificial Intelligence (AI)

How can we design an AI that automatically trades stocks? How can we detect financial frauds? Financial AI aims to develop models, algorithms, and systems for financial applications (e.g., actuarial and insurance, consumer banking, and investment banking), transforming subjective decision-making to data-driven decision-making. Despite the “efficient market hypothesis” of classic economics, we have seen many success stories of investments, and we develop state-of-the-art AI methods to exploit the tiny gap between conventional theory and what actually happens. We focus on the following researches.

  • Time series prediction: we develop methods for predicting future values of time series.
  • Asset value prediction and algorithm trading: we design AI that learns high-quality trading strategy.
  • Consumer analytics: we design methods for effective personalization, and customer understanding.
  • Fraud detection and prediction: we develop methods for detecting and predicting suspicious financial transactions and activities.

Publication

  • Yong-chan Park, Jun-Gi Jang, and U Kang, "Fast and Accurate Partial Fourier Transform for Time Series Data", The 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2021
  • Jaemin Yoo, Yejun Soun, Yong-chan Park, and U Kang, "Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts", The 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2021 [BIBTEX]
  • Jaemin Yoo and U Kang, "Attention-Based Autoregression for Accurate and Efficient Multivariate Time Series Forecasting", SIAM International Conference on Data Mining (SDM) 2021 [BIBTEX]
  • Jun-gi Jang, Dongjin Choi, Jinhong Jung, and U Kang, "Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range", ACM International Conference on Information and Knowledge Management (CIKM) 2018, Lingotto, Turin, Italy. [BIBTEX] [HOMEPAGE] [PDF]
  • Yongsub Lim and U Kang, "Time-weighted Counting for Recently Frequent Pattern Mining in Data Streams", Knowledge and Information Systems (KAIS). doi:10.1007/s10115-017-1045-1 [BIBTEX] [PDF]
  • Yongsub Lim, Jihoon Choi, and U Kang, "Fast, Accurate, and Space-efficient Tracking of Time-weighted Frequent Items from Data Streams", 23rd ACM International Conference on Information and Knowledge Management (CIKM) 2014,Shaghai, China [BIBTEX] [PDF]