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Ruixun Zhang 张瑞勋

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Bio

Ruixun Zhang is an assistant professor and Boya Young Fellow in the Department of Financial Mathematics, School of Mathematical Sciences at Peking University (PKU), with affiliations at the PKU Center for Statistical Science, the PKU National Engineering Laboratory for Big Data Analysis and Applications, the PKU Laboratory for Mathematical Economics and Quantitative Finance, and the MIT Laboratory for Financial Engineering. Ruixun received a Ph.D.  in Applied Mathematics from MIT in 2015 under the supervision of Andrew W. Lo, and bachelor's degrees in Mathematics and Applied Mathematics, and Economics (double degree) from Peking University in 2011.

Ruixun’s research interests include machine learning, sustainable investing, market microstructure, and evolutionary models of financial behavior. His research has appeared in journals such as Proceedings of the National Academy of Sciences, Operations Research, and Management Science. His work has been recognized by the S&P Global Academic ESG Research Award (2022), the International Centre for Pension Management (ICPM) Research Award (2023), the CFRI&CIRF-China Finance Review International Research Excellence Award (2023), the Best Paper Prize for Young Scholars in the Annual Conference of the Operations Research Society of China (2023), and the Commodity and Energy Markets Association (CEMA) Questrom-CEMA Best Paper Prize (2024). He serves on the editorial board of Digital Finance and International Journal of Financial Engineering. Ruixun has extensive experience working with industry partners including Google, Goldman Sachs, and several quantitative trading firms.

Google Scholar profile and CV.

Recent news

  • 2025: Our working paper “Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure” is available on SSRN and arxiv.

    • This work presents the first theoretical integration of factor structure with diffusion models, offering a principled approach for high-dimensional financial simulation with limited data.

    • We derive rigorous statistical guarantees, establishing nonasymptotic error bounds for generated distribution.

    • Empirical analysis demonstrates the economic significance of our framework in constructing mean-variance optimal portfolios and factor portfolios.

  • 2025: Our paper “On Consistency of Signatures Using Lasso” has been published at Operations Research.

    • We study the statistical consistency of signature using Lasso regression, both theoretically and numerically. We demonstrate that signature can be applied to learn nonlinear functions and option prices with high accuracy, and the performance depends on properties of the underlying process and the choice of the signature.

  • 2025. Our paper “The checkerboard copula and dependence concepts” has been published at SIAM Journal on Financial Mathematics.

    • We study the problem of choosing the copula when the marginal distributions of a random vector are not all continuous. We show that the “checkerboard copula” has the largest Shannon entropy, which means that it carries the least information among all possible copulas for a given random vector.

  • 2025. Our paper “Convergence Rate in Nonlinear Two-Time-Scale Stochastic Approximation with State (Time)-Dependence” has been accepted by AAAI 2025.

    • This is the outcome of an undergraduate research project (本研) by Zixi Chen, together with my PhD student Yumin Xu.

  • 2024: Our new book The Adaptive Markets Hypothesis: An Evolutionary Approach to Understanding Financial System Dynamics (joint with Andrew W. Lo) has been published! [Oxford University Press][Amazon]

  • 2024: Our working paper “Periodic Trading Activities in Financial Markets: Mean-field Liquidation Game with Major-Minor Players” is available on SSRN

    • We introduce a new mean-field liquidation game involving major and minor traders to rationalize observed periodic trading activities in the market. The model predicts that, in equilibrium, minor traders exhibit front-running behaviors in both the periodic and trend components of their strategies, reducing the major trader's profit. Such strategic interactions diminish the strength of periodicity in both overall trading volume and asset prices.

  • 2024: I am honored to receive the 北京大学黄廷方/信和青年杰出学者（NG Teng Fong/ Sino Scholarship for Outstanding Youth）

  • 2024: Our paper “Optimal Impact Portfolios with General Dependence and Marginals” is forthcoming at Operations Research.

    • We characterize the distribution of induced order statistics for general dependence and general marginals of any bivariate random variables, which is used to construct optimal impact portfolios.

    • 2023: Congratulations to Chaoyi Zhao (PhD student) who has won Second place in the Best Paper Prize for Young Scholars at the Annual Conference of the Operations Research Society of China (Financial Engineering and Risk Management Branch).

  • 2024: My paper “Toward Interpretable Machine Learning: Evaluating Models for Heterogeneous Predictions” is forthcoming at Annals of Operations Research.

  • 2024: Our paper “Quantifying the Returns of ESG Investing: An Empirical Analysis with Six ESG Metrics” is forthcoming at The Journal of Portfolio Management. SSRN.

    • We quantify the financial performance of ESG portfolios in the U.S., Europe, and Japan, based on data from six major ESG rating agencies. We propose several statistical and voting-based methods to aggregate individual ESG ratings. Overall, we find that there exists a significant signal in ESG rating scores that can be used for portfolio construction despite their noisy nature.

  • 2023. Our paper “A Hawkes Process Analysis of High-Frequency Price Endogeneity and Market Efficiency” is forthcoming at the European Journal of Finance.

    • This project is completed by a group of undergraduate students I supervise (本研).

  • 2023: Our paper “Interpretable Image-Based Deep Learning for Price Trend Prediction in ETF Markets” has been accepted by the European Journal of Finance.

    • We propose an attention-based convolutional neural network for price trend prediction that takes arbitrary images constructed from financial time series data as input.

    • The model achieves good out-of-sample performance and learns meaningful technical patterns that are interpretable by humans.

  • 2023: Our paper “Explainable Machine Learning Models of Consumer Credit Risk” is forthcoming at the Journal of Financial Data Science.

    • We create machine learning (ML) models to forecast home equity credit risk for individuals using a real-world dataset and demonstrate methods to explain the output of these ML models to make them more accessible to the end-user.

  • 2022: Our working paper “Estimating Market Liquidity from Daily Data: Marrying Microstructure Models and Machine Learning” is now on SSRN.

    • We apply (interpretable) machine learning to estimate market liquidity by combining human-engineered liquidity proxies based on microstructure models and widely available low-frequency data. Combining human-engineered proxies and the raw low-frequency data achieves cross-sectional correlations of over 0.95 and time-series correlations of over 0.70 between model estimates and the ground-truth average spread.

  • 2022: Our research won the S&P Global Academic ESG Research Award. Paper “Measuring and Optimizing the Risk and Reward of Green Portfolios” is available at The Journal of Impact and ESG Investing.

    • How do you measure the financial reward (or cost) of investing towards carbon neutrality?

    • We study the performance of green portfolios in both the US and Chinese markets, constructed using a broad range of climate-related environmental metrics.

  • 2022: Our working paper “Spectral Volume Models: High-Frequency Periodicities in Intraday Trading Activities” is now on SSRN.

    • We develop a spectral model for high-frequency intraday trading volumes, and find very strong and consistent periodicities at a few round-second / round-minute frequencies, across a large panel of stocks in both US and China. We study why they happen and how they are useful for volume prediction.

  • 2022: Our working paper “High-Frequency Liquidity in the Chinese Stock Market: Measurements, Patterns, and Determinants” is now on SSRN.

    • We study a range of high-frequency liquidity measures in the Chinese stock market using limit order book data.

  • I co-organize the regular Seminar series in Financial Mathematics at Peking University.

Contact

Office: 智华楼 472
School of Mathematical Sciences
Peking University
5 Yiheyuan Road
Beijing, China 100871

Email: zhangruixun AT pku DOT edu DOT cn



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