Address
7-278, Lau Ming Wai Academic Building, City University of Hong Kong
Phone
+852 34424753
Fax
+852 34420189
Email
Personal Web
http://www.jingyuhe.com
Qualifications
PhD - Econometrics and Statistics (The University of Chicago Booth School of Business)
MBA - Business Administration (The University of Chicago Booth School of Business)
MS - Statistics (The University of Chicago)
BS - Statistics (University of Science and Technology of China)
Biography
Dr. He's research interests include machine learning in finance, empirical asset pricing, and Bayesian statistics. His research work has appeared in leading statistics, finance and econometrics journals.
Awards
| Award Title | Institution |
|---|---|
| Best Paper Award | 2024 China Fintech Research Conference |
| 2022 INQUIRE Europe Research Grant Award | INQUIRE Europe |
Research Grants
PI: "Are asset pricing models sparse?", HKRGC - General Research Fund, (2025-2027), Jingyu He, Doron Avramov
PI: "Regression Tree for Portfolio Optimization and Imbalanced Data", General Research Fund - HKRGC, (2023-2025), Jingyu He, Xin He, Guanhao Feng
PI: "What Stocks are Predictable by Machine Learning? Find Heterogenity of Stocks by Firm Characteristics", Strategic Research Grant - City University of Hong Kong, (2023-2025), Jingyu He
"Financial Systemic Risk Measures based on Monte Carlo Simulation: Theory and Methods", NSFC/RGC Joint Research Scheme - NSFC/RGC, (2022-2025), Jeff Hong, Guangwu Liu, Zhi Chen, Jingyu He
PI: "XBART: A Novel Tree-Based Machine Learning Framework for Regression, Classification and Treatment Effect Estimation", Early Career Scheme - HKRGC, (2022-2023), Jingyu He
PI: "Elliptical Slice Sampler for Hierarchical Models in Marketing", Start-Up Grant - City University of Hong Kong, (2021-2023), Jingyu He
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Publications
Journal Publications and Reviews
Feng, Guanhao; He, Jingyu; Polson, Nick G.; Xu, Jianeng / Deep Learning in Characteristics-Sorted Factor Models. November 2024; In: Journal of Financial and Quantitative Analysis. Vol. 59, No. 7, pp. 3001-3036
Wang, Meijia; He, Jingyu; Hahn, P. Richard / Local Gaussian process extrapolation for BART models with applications to causal inference. 2024; In: Journal of Computational and Graphical Statistics. Vol. 33, No. 2, pp. 724-735
Paul, Erina; He, Jingyu; Mallick, Himel / Accelerated Bayesian Reciprocal LASSO. November 2023; In: Communications in Statistics: Simulation and Computation.
He, Jingyu; Hahn, P. Richard / Stochastic tree ensembles for regularized nonlinear regression. March 2023; In: Journal of the American Statistical Association. Vol. 118, No. 541, pp. 551–570
Feng, Guanhao; He, Jingyu / Factor investing: A Bayesian hierarchical approach. September 2022; In: Journal of Econometrics. Vol. 230, No. 1, pp. 183-200
Hahn, P. Richard; He, Jingyu; Lopes, Hedibert F. / Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors. 2019; In: Journal of Computational and Graphical Statistics. Vol. 28, No. 1, pp. 142-154
Hahn, P. Richard; He, Jingyu; Lopes, Hedibert / Bayesian Factor Model Shrinkage for Linear IV Regression With Many Instruments. April 2018; In: Journal of Business and Economic Statistics. Vol. 36, No. 2, pp. 278-287
Hahn, P. Richard; Carvalho, Carlos M.; Puelz, David; He, Jingyu / Regularization and Confounding in Linear Regression for Treatment Effect Estimation. 2018; In: Bayesian Analysis. Vol. 13, No. 1, pp. 163-182
Working Papers
Feng, Guanhao; He, Jingyu; Li, Junye; Sarno, Lucio; Zhang, Qianshu / Currency Return Dynamics: What Is the Role of U.S. Macroeconomic Regimes?. July 2024;
Cong, William Lin; Feng, Guanhao; He, Jingyu; Wang, Yuanzhi / Mosaics of Predictability. February 2024;
Cong, Lin William; Feng, Guanhao; He, Jingyu; Li, Junye / Uncommon Factors for Bayesian Asset Clusters. September 2022;
Cong, Lin William; Feng, Guanhao; He, Jingyu; He, Xin / Growing the Efficient Frontier on Panel Trees. October 2021;
Chapters, Conference Papers, Creative and Literary Works
Krantsevich, Nikolay; He, Jingyu; Hahn, P. Richard / Stochastic Tree Ensembles for Estimating Heterogeneous Effects. April 2023; Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) 2023. Vol. 206, pp. 6120-6131
He, Jingyu; Yalov, Saar; Hahn, P. Richard / XBART: Accelerated Bayesian Additive Regression Trees. April 2019; The 22nd International Conference on Artificial Intelligence and Statistics. pp. 1130-1138
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