Research

Starry Night and the Astronauts, Alma Thomas (1972)


Research Areas

Machine Learning, Empirical Asset Pricing, Bayesian Statistics and Econometrics.

Publication

Finance

  1. Siyu Bie, Guanhao Feng, Naixin Guo and Jingyu He (2025). Can news predict firm bankruptcy? Forthcoming, Journal of Financial Markets.

  2. Lin William Cong, Guanhao Feng, Jingyu He and Xin He (2025). Growing the Efficient Frontier on Panel Trees. Journal of Financial Economics, 167 (2025): 104024.

  3. Guanhao Feng, Jingyu He, Nicholas Polson and Jianeng Xu (2024). Deep Learning in Characteristics-Sorted Factor Models. Journal of Financial and Quantitative Analysis, 59.7 (2024): 3001-3036.

Econometrics and Statistics

  1. Meijia Wang, Jingyu He and P. Richard Hahn (2024). Local Gaussian process extrapolation for BART models with applications to causal inference. Journal of Computational and Graphical Statistics, 3.2 (2024): 724-735.

  2. Jingyu He and P. Richard Hahn (2023). Stochastic tree ensembles for regularized nonlinear regression. Journal of the American Statistical Association, 118.541 (2023): 551-570. Supplementary material.

  3. Erina Paul, Jingyu He and Himel Mallick (2025). Accelerated Bayesian Reciprocal LASSO. Communications in Statistics - Simulation and Computation, 54(4), 1175–1191.

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

  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.

Machine Learning Conference Proceedings

  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. 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.

Working Paper

  1. Siddhartha Chib, Guanhao Feng, Jingyu He and Qianshu Zhang (2025). Beta-pricing Models with Traded and Nontraded Factors: A Unified Framework for Estimation and Comparison.

  2. Doron Avramov, Guanhao Feng, Jingyu He and Shuhua Xiao (2025). Schrödinger`s Sparsity in the Cross Section of Stock Returns.

  3. Siyu Bie, Jingyu He, Sehwa Kim and Yao Lu (2025). Loan Portfolio Exposure to Macroeconomic Conditions: A Dynamic Tree Model.

  4. Jingyu He and Junye Li (2024). Heteroskedastic SDF and Learning about Time-Varying Factor Risk Premia.

  5. Siyu Bie, Francis X. Diebold, Jingyu He and Junye Li (2024). Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching.

  6. Lin William Cong, Guanhao Feng, Jingyu He and Yuanzhi Wang (2024). Mosaics of Predictability.

  7. Guanhao Feng, Jingyu He, Junye Li, Lucio Sarno and Qianshu Zhang (2024). Currency Return Dynamics: What Is the Role of U.S. Macroeconomic Regimes?

  8. Lin William Cong, Guanhao Feng, Jingyu He and Junye Li (2023). Uncommon Factors and Asset Heterogeneity in the Cross Section and Time Series.

  9. Jingyu He, Nicholas Polson and Jianeng Xu (2021). Data Augmentation with Polya Inverse Gamma.

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

Research Grants

  1. PI, "Illusion of stock return predictability: find the heterogeneity.", National Natural Science Foundation of China, Young Scientists Fund, 01/2025-12/2027.

  2. PI, "Are asset pricing models sparse?", Hong Kong Research Grants Council, General Research Fund, 01/2025-12/2027.

  3. PI, "What Stocks are Predictable by Machine Learning?", City Univeristy of Hong Kong, Strategic Research Grant, 09/2023-08/2025.

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

  5. INQUIRE Europe Research Grant, 2022.

  6. 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.

  7. 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.

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