Ruixun Zhang 张瑞勋
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Affiliation:
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Bio
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 , the , the PKU Laboratory for Mathematical Economics and Quantitative Finance, and the .
Prior to joining PKU, I obtained my Ph.D. in in 2015, under the supervision of . I received my bachelor's degrees in Mathematics and Applied Mathematics, and Economics (double degree) from Peking University in 2011. I also worked at and 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 , the , the , 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), and the Commodity and Energy Markets Association (CEMA) Questrom-CEMA Best Paper Prize (2024).
My and CV.
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Recent news
2025: Our working paper “Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure” is available on
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 .
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 .
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 .
This is the outcome of an undergraduate research project (本研) by Zixi Chen, together with my PhD student Yumin Xu.
2024: Our paper “Performance Attribution for Portfolio Constraints” has been published at .
It recently won the . It has previously won the .
We propose a new performance attribution framework that quantifies the information content in portfolio constraints. Contrary to conventional wisdom, constraints may improve portfolio performance under certain scenarios.
2024: Our new book The Adaptive Markets Hypothesis: An Evolutionary Approach to Understanding Financial System Dynamics (joint with Andrew W. Lo) has been published! [][]
2024: Our working paper “Periodic Trading Activities in Financial Markets: Mean-field Liquidation Game with Major-Minor Players” is available on
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 .
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 .
2024: Our paper “Quantifying the Returns of ESG Investing: An Empirical Analysis with Six ESG Metrics” is forthcoming at The Journal of Portfolio Management. .
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 “Quantifying the Impact of Impact Investing” is forthcoming at . It recently won the !.
We propose a quantitative framework for assessing the financial impact of any form of impact investing. The framework can be applied to understand biotech venture philanthropy, semiconductor R&D consortium, divesting from ‘‘sin’’ stocks, investing in ESG, and even ‘‘meme’’ stock rallies such as GameStop in 2021.
AFA 2022 talk:
has reported our work.
2023: Our paper “Social Contagion and the Evolutionary Survival of Diverse Investment Styles” has been published at the .
We model the contagion of investment ideas in a multi-period setting, and show that a greater diversity in investment styles are able to survive compared to what traditional theory predicts.
A blog published on
2023. Our paper “A Hawkes Process Analysis of High-Frequency Price Endogeneity and Market Efficiency” is forthcoming at the .
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 .
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 .
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 .
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 . Paper “Measuring and Optimizing the Risk and Reward of Green Portfolios” is available at .
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 .
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 .
We study a range of high-frequency liquidity measures in the Chinese stock market using limit order book data.
2022: is published in the inaugural issue of Collective Intelligence.
How do bias, polarization, and other challenges to collective intelligence happen? We propose ways to prevent such failures by nudging the “madness of mobs” towards the “wisdom of crowds” through shifts in the environment.
2021: is now published in iScience.
Why are we Bayesians and only have finite memory? Evolution provides an answer.
2021: is now published in Plos One.
Are we “rational” in financial decision making? We conduct an experiment with real monetary payoffs to show that people engage in probability matching, also known as the “matching law” or Herrnstein’s Law.
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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