Basic Information:
Dr. Jun Zhang is an Assistant Researcher at the School of Statistics and Data Science, Southeast University. He obtained his Ph.D. from Southwest University of Finance and Economics in June 2024. With extensive international research experience, he served as a Visiting Scholar and Research Assistant at City University of Hong Kong (2022-2023) and later worked as a Research Assistant and Postdoctoral Fellow at Hong Kong Laboratory for AI-Powered Financial Technologies Limited (2023-2025), where he participated in multiple FinTech research projects. An accomplished academic, he has also achieved remarkable results in various academic competitions during his student years. He welcomes inquiries from students interested in academic research, FinTech applications, or academic competitions.For further information, please feel free to contact Dr. Zhang via email.
Research area:
Interdisciplinary research on statistical theory and financial econometrics, high-dimensional factor models, investment portfolios, and large-scale financial factor evaluation and screening.
Selected Papers:
1.Jun Zhang, Wei Lan, Xinyan Fan, and Wen Chen. Maximum Conditional Alpha Test For Conditional Multi-Factor Models.Statistica Sinica, 2023, DOI: 10.5705/ss.202022.0137.
2.Jun Zhang, Wei Lan, and Nengsheng Fang. A Network-Adjusted Multiple Testing Procedure with Application to Mutual Fund Selection.Quarterly Journal of Economics and Management (In Chinese), 2023, 2(4): 119-142.
3. Yongqiang Wu,Jun Zhang*, and Wei Lan. Structured covariance matrix estimation under volatility constraint.Finance Research Letters, 2025, DOI: https://doi.org/10.1016/j.frl.2025.108047.
Working Papers:
1. Guanhao Feng, Wei Lan, Hansheng Wang, andJun Zhang. Anomaly or Risk Factor? A Stepwise Evaluation. Minor Revision atManagement Science, 2024.
2.Jun Zhang, Dan Pu, Wei Lan, and Guanhao Feng. Testing Alphas in Linear Factor Models: A Portfolio Approach. Under review, 2025.
3.Jun Zhang, Wei Lan, Long Feng, and Guanhao Feng. Testing and Comparing Asset Pricing Factor Models: An Out-of-Sample Perspective. Under review, 2025.
