Zhoufan Zhu

My name is Zhoufan Zhu (朱周帆 in Chinese). I am now an assistant professor at WISE & Department of Finance, Xiamen University. My recent research lies at the intersection of artificial intelligence and finance. I am dedicated to designing advanced computational and machine learning algorithms for financial data analysis, primarily focusing on stock market data, to improve trading strategies and portfolio selection in the context of modern big data. My specific areas of focus include:

1) Developing multimodal learning algorithms (e.g., multimodal foundation models) to integrate multi-scale financial data for asset pricing and portfolio selection;

2) Building real-world learning systems to learn generalizable, trustworthy, and fair representations from imperfect financial data;

3) Creating causality-driven learning algorithms to address conformal prediction problems in complex financial data, improving their interpretability and safety for risk management.

Before joining Xiamen University, I earned my Ph.D. in Mathematical Statistics from the School of Statistics and Management at Shanghai University of Finance and Economics in 2024, under the supervision of Prof. Liwen Zhang. Between July 2022 and January 2024, I served as a Research Assistant in the Department of Statistics and Actuarial Science at The University of Hong Kong, where I had the honor of working under the guidance of Prof. Ke Zhu.

Selected Publications

JOEF

Big Portfolio Selection by Graph-based Conditional Moments Method.

Zhoufan Zhu, Ningning Zhang, and Ke Zhu

Journal of Empirical Finance, 2024

Bib HTML Code

@article{Zhu2024Graph,
  title = {Big Portfolio Selection by Graph-based Conditional Moments Method.},
  author = {Zhu, Zhoufan and Zhang, Ningning and Zhu, Ke},
  journal = {Journal of Empirical Finance},
  volume = {78},
  number = {},
  pages = {101533},
  year = {2024},
}

JBES

Asset Pricing via the Conditional Quantile Variational Autoencoder.

Xunling Yang^†, Zhoufan Zhu^†, Dong Li, and Ke Zhu

Journal of Business & Economic Statistics, 2024

Bib HTML Code

@article{Yang2024Asset,
  title = {Asset Pricing via the Conditional Quantile Variational Autoencoder.},
  author = {Yang, Xunling and Zhu, Zhoufan and Li, Dong and Zhu, Ke},
  journal = {Journal of Business \& Economic Statistics},
  volume = {42},
  number = {2},
  pages = {681-694},
  year = {2024},
  publisher = {Taylor & Francis},
}

ICML

Variance Control for Distributional Reinforcement Learning

Qi Kuang^†, Zhoufan Zhu^†, Liwen Zhang, and Fan Zhou

In Proceedings of the 40th International Conference on Machine Learning, 2023

Bib HTML

@inproceedings{Kuang2023Variance,
  author = {Kuang, Qi and Zhu, Zhoufan and Zhang, Liwen and Zhou, Fan},
  title = {Variance Control for Distributional Reinforcement Learning},
  year = {2023},
  publisher = {JMLR.org},
  articleno = {736},
  numpages = {22},
  booktitle = {Proceedings of the 40th International Conference on Machine Learning},
}

IJCAI

Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning

Fan Zhou, Zhoufan Zhu, Qi Kuang, and Liwen Zhang

In Proceedings of the 30th International Joint Conference on Artificial Intelligence, 2021

Bib HTML

@inproceedings{Zhou2021Non,
  author = {Zhou, Fan and Zhu, Zhoufan and Kuang, Qi and Zhang, Liwen},
  booktitle = {Proceedings of the 30th International Joint Conference on Artificial Intelligence},
  pages = {3455-3461},
  title = {Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning},
  year = {2021},
}