Weichen WANG
Prof. Weichen WANG
Assistant Professor

3917 1617

KK 1319

Academic & Professional Qualification
  • PhD in Operations Research and Financial Engineering, Princeton University, 2016
  • BS in Mathematics and Physics, Tsinghua University, 2011

Dr. Weichen Wang joined HKU in 2021 as an Assistant Professor. He obtained his PhD in Operations Research and Financial Engineering from Princeton University in 2016. After graduation, he joined Two Sigma Investments as a quantitative researcher where he worked on applying machine learning for equity market forecasting. Dr. Wang also served as a Visiting Lecturer at Princeton University for Spring 2020. Before his PhD, he received his bachelor’s degree in Mathematics and Physics from Tsinghua University in 2011.

Dr. Wang’s research areas include big data analysis, econometrics, statistics and machine learning, and he is particularly interested in the factor structure of the financial market and real-world applications of machine learning and deep learning. His works have been published in top journals including Annals of Statistics, Journal of Machine Learning Research, Journal of Econometrics etc.

  • Business Statistics
  • Research Methodologies in Business Analytics
  • Quantitative Trading / AI in Finance
Research Interest
  • Big data analysis
  • Machine learning
  • Econometrics and asset pricing
  • Factor model and low-rank structure
  • Semi-parametric and robust statistics
Selected Publications
  • Fan, J., Lou, Z., Wang, W., & Yu, M. (2024). Ranking Inferences Based on the Top Choice of Multiway Comparisons. Journal of the American Statistical Association, 0(0), 1-14.
  • Wang, W., An, R., & Zhu, Z. (2024). Volatility Prediction Comparison via Robust Volatility Proxies: An Empirical Deviation Perspective. Journal of Econometrics, 239(2), 105633.
  • Wang, W., Han, J., Yang Z., & Wang Z. (2021). Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time. International Conference on Machine Learning (ICML), 10772-10782.
  • Fan, J., Wang, W., & Zhu, Z. (2021). A Shrinkage Principle for Heavy-Tailed Data: High-Dimensional Robust Low-Rank Matrix Recovery. Annals of Statistics, 49(3), 1239-1266.
  • Fan, J., Wang, W., & Zhong, Y. (2018). An l∞ Eigenvector Perturbation Bound and Its Application to Robust Covariance Estimation. Journal of Machine Learning Research, 18(207), 1–42.
  • Fan, J., Liu, H., & Wang, W. (2018). Large Covariance Estimation through Elliptical Factor Models. Annals of Statistics, 46(4), 1383–1414.
  • Wang, W., & Fan, J. (2017).Asymptotics of Empirical Eigen-Structure for High Dimensional Spiked Covariance. Annals of Statistics, 45(3), 1342–1374.
  • Fan, J., Liao, Y., & Wang, W. (2016). Projected Principal Component Analysis in Factor Models. Annals of Statistics, 44(1), 219–254.
Recent Publications
悉心栽培數據人才– 王煒辰博士