Weichen Wang

Biography

I joined HKU in 2021 as an Assistant Professor in the area of Innovation and Information Management of HKU Business School. I am also one of the members of the Statistics Group.

I obtained my PhD in Operations Research and Financial Engineering from Princeton University in 2016, supervised by Prof Jianqing Fan. After graduation, I joined Two Sigma Investments as a quantitative researcher and worked on applying machine learning for equity market forecasting. I also served as a visiting lecturer at Princeton University for Spring 2020. Before PhD, I received my bachelor’s degree in Mathematics and Physics from Tsinghua University in 2011.

My research combines statistics and machine learning methodology with applications in econometrics and financial science. I am particularly interested in the factor structure of the financial market and real-world applications of machine learning.

Econometric Factor Analysis

• Fan, J., Liao, Y. and Wang, W. (2016). Projected Principal Component Analysis in Factor Models. Annals of Statistics, 44(1), 219-254.
• Fan, J., Liu, H. and Wang, W. (2018). Large Covariance Estimation through Elliptical Factor Models. Annals of Statistics, 46(4), 1383–1414.
• Fan, J., Wang, W. and Zhong, Y. (2019). Robust Covariance Estimation for Approximate Factor Models. Journal of Econometrics, 208(1), 5–22.



High-dimensional Statistical Inference

• Fan, J., Rigollet, P. and Wang, W. (2015). Estimation of Functionals of Sparse Covariance Matrices. Annals of Statistics, 43(6), 2706–2737.
• Wang, W. and Fan, J. (2017). Asymptotics of Empirical Eigen-Structure for High Dimensional Spiked Covariance. Annals of Statistics, 45(3), 1342–1374.
• Fan, J., Liu, H., Wang, W. and Zhu, Z. (2018). Heterogeneity Adjustment with Applications to Graphical Model Inference. Electronic Journal of Statistics, 12(2), 3908–3952.
• Fan, J., Lou, Z., Wang, W. and Yu, M. (2024). Ranking Inferences Based on the Top Choice of Multiway Comparisons. Journal of the American Statistical Association, 0(0), 1–14.



Robust Methodologies

• Fan, J., Wang, W. and Zhu, Z. (2021). A Shrinkage Principle for Heavy-Tailed Data: High-Dimensional Robust Low-Rank Matrix Recovery. Annals of Statistics, 49(3), 1239-1266.
• Wang, W., An, R. and Zhu, Z. (2024). Volatility Prediction Comparison via Robust Volatility Proxies: An Empirical Deviation Perspective. Journal of Econometrics, 239(2), 105633.



Machine Learning

• Fan, J., Wang, W. and Zhong, Y. (2018). An l-infinity Eigenvector Perturbation Bound and Its Application to Robust Covariance Estimation. Journal of Machine Learning Research, 18(207), 1–42.
• Wang, W., Han, J., Yang Z. and 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.
• Chen, X., Liao, Y. and Wang, W. (2024). Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves. Journal of Econometrics (Under Review).
• Fan, J., Lou, Z., Wang, W. and Yu, M. (2024). Spectral Ranking Inferences based on General Multiway Comparisons. Operations Research (Under Review).



Others

• Wang, W. and Zhang, X. (2011). Network-based Group Variable Selection for Detecting Expression Quantitative Trait Loci (eQTL). BMC Bioinformatics, 12, 269.
• Wang, W., Qin, Z., Feng, Z., Wang, X. and Zhang, X. (2013). Identifying Differentially Spliced Genes from Two Groups of RNA-seq Samples. Gene, 518(1), 164–170.
• Min, K., Cheng, Z., Liu, J., Fang, Y., Wang, W., Yang, Y., Geldsetzer, P., Barnighausen, T., Yang, J., Liu, D., Chen, S. and Wang, C. (2023). Early-stage Predictors of Deterioration among 3145 Non-severe SARS-CoV-2-infected People Community-isolated in Wuhan, China: A Combination of Machine Learning Algorithms and Competing Risk Survival Analyses. Journal of Evidence-Based Medicine, 16(2), 166-177.

Working Papers

• Cao, Y., Meng, X. and Wang, W. (2024+). Estimation of Out-of-Sample Sharpe Ratio for High Dimensional Portfolio Optimization. Working Paper.
• Deng Y., Gao, J. and Wang W. (2024+). On Reference Policy Regulated Multi-period Mean-variance Portfolio Optimization. Working Paper.

Teaching

• MSBA7032: Quantitative Trading / AI in Finance (Spring 2024 at HKU)
• IIMT6017: Research Methodologies in Business Analytics (Spring 2022, Fall 2023 at HKU)
• MSBA7002: Business Statistics (Fall 2021, 2022, 2023 at HKU)
• ORF504: Financial Econometrics (Spring 2020 at Princeton University)

Research Group

PhD Students

• Yutao Deng

Research Assistants

• Ziyan Cai (now PhD in IIM Business Analytics at HKU)
• Leda Wang (now PhD in Statistics at Yale University)
• Xizhuo Wang (now Master of Science in Computational Finance at CMU)