Ruixun Zhang 张瑞勋
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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
2023: Our working paper “Performance Attribution for Portfolio Constraints” won the CFRI&CIRF-China Finance Review International Research Excellence Award (Asset Management)! The paper is available on SSRN.
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.
2023: Our paper “Quantifying the Impact of Impact Investing” is forthcoming at Management Science. It recently won the International Centre for Pension Management (ICPM) Research Award, Honourable Mention! The paper is available on SSRN.
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: recording on Youtube
Institutional Investors has reported our work.
2023: Our working paper “Optimal Impact Portfolios with General Dependence and Marginals” is now on SSRN.
We develop the impact portfolio construction framework first proposed in Lo and Zhang (2021) to allow for general dependence between impact and returns, as well as general marginals of the return distributions.
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).
2023: Our paper “Social Contagion and the Evolutionary Survival of Diverse Investment Styles” has been published at the Journal of Economic Dynamics and Control.
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 Harvard Law School Forum on Corporate Governance
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 working paper “Quantifying the Returns of ESG Investing: An Empirical Analysis with Six ESG Metrics” is now on 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, the latter based on the theory of social choice. 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. SSRN.
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.
2022: The Wisdom of Crowds vs. the Madness of Mobs: An Evolutionary Model of Bias, Polarization, and Other Challenges to Collective Intelligence 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: The evolutionary origin of Bayesian heuristics and finite memory is now published in iScience.
Why are we Bayesians and only have finite memory? Evolution provides an answer.
2021: To maximize or randomize? An experimental study of probability matching in financial decision making 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: 理科一号楼 1426E
School of Mathematical Sciences
Peking University
5 Yiheyuan Road
Beijing, China 100871
Email: zhangruixun AT pku DOT edu DOT cn
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