Ruixun Zhang 张瑞勋


Hi, I am an Assistant Professor in the Department of Financial Mathematics, School of Mathematical Sciences at Peking University (PKU). I am also affiliated with 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.

Prior to joining PKU, I obtained my Ph.D.  in Applied Mathematics from MIT in 2015, under the supervision of Andrew W. Lo. I received my bachelor's degrees in Mathematics and Applied Mathematics, and Economics (double degree) from Peking University in 2011. I also worked at Google and Goldman Sachs in the past.

My research interests include sustainable investing, market microstructure, machine learning applications in finance, and evolutionary foundations of economic behavior and financial markets. My research has been recognized by the S&P Global Academic ESG Research Award (2022), the International Centre for Pension Management (ICPM) Research Award, Honourable Mention (2023), the CFRI&CIRF-China Finance Review International Research Excellence Award (2023), and the Best Paper Prize for Young Scholars in the Annual Conference of the Operations Research Society of China (Financial Engineering and Risk Management Branch, 2023).

My Google Scholar profile and CV.

Recent news


Our new book The Adaptive Markets Hypothesis: An Evolutionary Approach to Understanding Financial System Dynamics (joint with Andrew W. Lo) has been published!

  • I am looking for PostDocs on a rolling basis. We offer competitive benefits and a first-class platform. 北京大学博士后项目的更多信息请参见这里.

  • 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 working paper “On Consistency of Signatures Using Lasso” is now on arxiv.

    • We revisits the consistency issue of Lasso regression for the signature transform, both theoretically and numerically.

  • 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.


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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