Research

Research Areas

Machine Learning, Tree Ensembles, Bayesian Statistics, Empirical Asset Pricing.


Publication

  1. Nikolay Krantsevich, Jingyu He and P. Richard Hahn (2023). Stochastic Tree Ensembles for Estimating Heterogeneous Effects. The 26th International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.

  2. Guanhao Feng and Jingyu He (2022). Factor Investing: A Bayesian Hierarchical Approach. Journal of Econometrics. 230.1 (2022): 183-200.

  3. Jingyu He and P. Richard Hahn (2021). Stochastic tree ensembles for regularized nonlinear regression. Accepted at Journal of the American Statistical Association. Supplementary material.

  4. Jingyu He, Saar Yalov and P. Richard Hahn. XBART: Accelerated Bayesian Additive Regression Trees. The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS). 2019. slides, poster, Appendix, package: XBART.

  5. P. Richard Hahn, Jingyu He and Hedibert Lopes. Efficient sampling for Gaussian linear regression with arbitrary priors. Journal of Computational and Graphical Statistics. 28.1 (2019): 142-154. R package : bayeslm, Demo Script.

  6. P. Richard Hahn, Carlos M. Carvalho, Jingyu He and David Puelz. Regularization and confounding in linear regression for treatment effect estimation. Bayesian Analysis. 13.1 (2018): 163-182.

  7. P. Richard Hahn, Jingyu He and Hedibert Lopes. Bayesian factor model shrinkage for linear IV regression with many instruments. Journal of Business and Economic Statistics. 36.2 (2018): 278-287.


Working Paper

  1. Lin William Cong, Guanhao Feng, Jingyu He and Junye Li (2022). Uncommon Factors for Bayesian Asset Clusters. SSRN.

  2. Lin William Cong, Guanhao Feng, Jingyu He and Xin He (2022). Asset Pricing with Panel Trees Under Global Split Criteria. SSRN.
    3. 2022 INQUIRE Europe Research Award.

  3. Meijia Wang, Jingyu He and P. Richard Hahn (2022). Local Gaussian process extrapolation for BART models with applications to causal inference. Revise and resubmit, Journal of Computational and Graphical Statistics.

  4. Guanhao Feng, Jingyu He, Nicholas Polson and Jianeng Xu (2022). Deep Learning in Characteristics-Sorted Factor Models. Revise and resubmit, Journal of Financial and Quantitative Analysis.

  5. Jingyu He, Nicholas Polson and Jianeng Xu (2021). Data Augmentation with Polya Inverse Gamma. Revise and resubmit, Journal of Computational and Graphical Statistics.

  6. Guanhao Feng, Jingyu He and Nicholas Polson (2019). Deep Learning for Predicting Asset Returns.


Research Grants

  1. PI, “Regression Tree for Portfolio Optimization and Imbalanced Data.” Hong Kong Research Grants Council, General Research Fund, 01/2023-12/2025.

  2. INQUIRE Europe Research Grant, 2022.

  3. PI, “XBART, a novel tree-based machine learning framework for regression, classification and treatment effect estimation.” Hong Kong Research Grants Council, Early Career Scheme, 01/2022-12/2023.

  4. Co-I, Financial Systemic Risk Measures based on Monte Carlo Simulation: Theory and Methods. National Natural Science Foundation of China & Hong Kong Research Grants Council, NSFC/RGC Joint Research Scheme, 01/2022-12/2025.

  5. PI, Elliptical Slice Sampler for Hierarchical Models in Marketing, City University of Hong Kong, Start-up Grant, 10/2021-10/2023.