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祝武

金融系    金融学助理教授

研究员(兼)

Google Website: https://sites.google.com/view/zhuwu/research?authuser=0

电话: (86) (10) 62792443

办公室:李华楼B328

邮箱:zhuwu@sem.tsinghua.edu.cn

开放时间:Office hour 13:30-15:30

教育经历

2021,  Ph.D in Economics,  The University of Pennsylvania

2021, Master in Statistics,  The University of Pennsylvania

2016, Master in Economics, Peking University

2009, Bachelor in Materials Physics, The University of Science and Technology, Beijing 



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工作经历

2023年-           清华大学数智审计研究中心研究员

2021年-至今  清华大学经管学院 助理教授


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讲授课程

Teaching: 

Advanced Empirical Asset Pricing (Ph.D. 2023-2024 Fall) 

Methodology and Applications of Financial Big Data (Graduate students, 2021-2024 Fall)

Deep Learning and Its Applications in Finance (2022-2025 Spring, Undergraduate) 

Machine Learning and Its Applications in Finance (2022-2025 Fall, Undergraduate)

Machine Learning (Graduate students, 2025 Fall) 

Advanced Corporate Finance (Ph.D. 2021 Fall, 2022 Fall)

Machine Learning for Central Bankers (EMBA for Indonesia Central Bank)



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研究领域

AI (Artificial Intelligence),  Corporate Finance, Asset Pricing, Big Data, Network Economics,  Macroeconomics,  Corporate Innovation, and Chinese Economy.


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学术成果

Papers:

  1. "Hierarchical Vintage Sparse PCA. Discussion on the paper by Rohe and Zeng",  with Jeff Cai, Dan Yang, Linda Zhao, Journal of the Royal Statistical Society B: Statistical Methodology, 2023

  2. State Ownership in China: An Equity Network Perspective, "The Arc of the Chinese Economy",  with Jeff Cai, Xian Gu, Linda Zhao. edited by, Hanming Fang and Marshall W. Mayer (Forthcoming, Cambridge University Press, 2025)

  3. Textual Factors: A Scable, Interpretable, and Data-driven Approach to Analyzing Unstructured Information. with Lin William Cong, Tengyuan Liang, Xiao Zhang (ForthcomingManagement Science, 2025)

    Develop a theoretical framework and implementable algorithm for topic modelling to analyze unstructured information (text or audio) in a large scale way, and significantly enhance the economic interpretation.

    Keywords: LLMs, Textual Factors, Topic Models, Representation Learning.

  4. Network Regression and Supervised Centrality Estimation, with Jeff Cai, Ran Chen, Haipeng Shen,  Dan Yang, and Linda Zhao (Conditionally Accepted, Journal of the American Statistical Association (JASA), 2025)

    Empirical networks are full of noise or measurement error which significantly bias  estimation of network effect, we develop a semi-supervised framework to  jointly debias the network effect estimation and denoise the observed network, and provide an asymptotic distribution for our estimation.

    Keywords: Supervised Learning, Network Regression, Two-Stage Estimation, Measurement Errors in Networks.

  5. The Network Effects of Agency Conflicts, with Rakesh Vohra and Yiqing Xing, (Reject&Resubmit, American Economic Review)

    We develop a general framework incorporating endogenous action in the equity-holding network and various type of within-firm friction (Limited Ability, Default Cost, Interst Costs, and Moral Harzard) . We show that firm-level agency conflicts not only counter the role of network structure in the propagation of shocks but they can have a significant impact on system-wide behavior that differs from those predicted based on network structure alone. This implies that corporate governance can play an pivotal role in macro fluctuations. 

    Keywords: Network Effect, Macro Fluctuations, Agency Conflicts, Corporate Governance.

  6. Ownership Network and Firm Growth - What Do Forty Million Companies Tell Us About the Chinese Economy?  with Frankin Allen, Jeff Cai, Xian Gu, QJ Jun Qian, and Linda Zhao (Best Paper Award in CFRC2021), (Revise&Resubmit, Management Science)

    Using the business registration data which covers a univeral firms registered in China. We document that ownership network play a pivotal role in promoting firm  growth and develop a general equilibrium with ownership network, endogenous policy learning, and investment decision to quantify the findings.

    Keywords: Equity-holding (Ownership) Networks, Learning in Networks, Firm Growth.

  7. Tiered Intermediation in Equity-holding Networks, with Robert Townsend and Yu Shi (Finalists of Best Ph.D. Paper in MFA 2020,  CICF, AEA, CICM, CFRC, NAES, MFA, MIT, IMF, UPENN, PHBS-IER Special Issue Conference (2024), Revise&Resubmit, Management Science)

    Using Chinese business registry data, we construct a comprehensive firm-to-firm equity-holding network and demonstrate its role as a tiered financial intermediary. The network exclusively channels credit supply shocks from corporate shareholders to their subsidiaries, with no significant propagation between subsidiaries or in the reverse direction, and have a signicant impact on the bank credit reallocation. we develop a general equilibrium model incorporating an equity network, heterogeneous investment opportunities, and financial constraints, showing that tiered intermediation arises as an equilibrium outcome.

    Keywords: Tiered Intermediation, Equity-holding Networks, Investment, Equity Financing

  8. Optimal Assortment and Pricing via Generalized MNL Models with Novel Poisson Arrivals (with Ran Chen, Jeff Cai, Qitao Huang, Martin Wainwright, Linda Zhao, 2024 Econometric Society Interdisciplinary Frontiers (ESIF) conference on Economics and AI+ML (Cornell), INFORMS Annual 2024(Invited Submission)

    We develop an asymptotic optimal algorithm to jointly learning the assortment (customer preference) and time-varying customer arrival (market size), we provide a theoretical upper and lower bound for our optimal algorithm.

    Keywords: Reinforcement Learning, Contextual Bandits, Joint Learning on Assortment and Pricing, On-line learning

  9. ChatGPT and DeepSeek: Can They Predict  the Stock Market and  Macroeconomy (with Jian Chen, Guohao Tang, and Guofu Zhou, Submitted)

    We systemically examine the predicability of ChatGPT and Deepseek on the stock market and its linkage to the macroeconomy

    Keywords: ChatGPT, DeepSeek, Stock Market Predictability, Macro Forecast, Investor Attention, Limited Information Processing 

  10. Centralization or Decentralization - The Evolution of State Ownership in China, with Franklin Allen, Jeff Cai, Xian Gu, QJ Jun Qian, Linda Zhao (Best Paper Award in CICF2021, Submitted)

  11. Link Complexity and Cross Predictability, with Guofu Zhou, Finalist of Best Paper in Financial Management Association 2020 (US)

  12. Networks and Business Cycles, with Yucheng Yang

  13. Automation-Induced Innovation Shift (with Lin William Cong, Yao Lu, and Hanqing Shi,  AsianFA 2024, AEA 2025, CICF 2025, ). 

  14. Innovation Networks and M&A, (with Yuwei Cui, and Yao Lu)

  15. The Carbon Risk Premium Revisited: The Role of Production Networks. (with Shubo Kou, Kai Li, Minghao Li, CICF 2025, SED 2025, MRS 2025)

  16. Heterogeneous LLM Adoption for Research Writing: Initial Dynamics and Implications. (with Lin William Cong, Submitted)

  17. Global Minds and Local Gains: A Talent Tale of US and China (with Hanming Fang, Xian Gu, Hanyi Yan,)

Selected projects in progress: 

    1. A Tale of Two Networks: Investments Like China

    2. Novelty Premium and LLMs (come out soon, prelimary draft is available) 

    3. Competitive Narratives (with Shangjin Wei)

Book Chapters:

    1. State Ownership in China: An Equity Network Perspective, "The Arc of the Chinese Economy", the University of Pennsylvania, 2023, with Jeff Cai, Xian Gu, Linda Zhao. Cambridge University Press,  edited by, Hanming Fang and Marshall W. Mayer (to appear)

Media Coverages:

    1. "Tiered Intermediation in Business Groups", with Robert Townsend and Yu Shi, (VoxChina, 2020)

    2. "Centralization or Decentralization - the Evolution of State Ownership in China", with Franklin Allen, Jeff Cai, Xian Gu, Jun Qian (QJ), Linda Zhao (VoxChina, 2021)

    3. "Centralization or Decentralization - the Evolution of State Ownership in China", with Franklin Allen, Jeff Cai, Xian Gu, Jun Qian, Linda Zhao (Stanford Chinese Economy Briefs, China's Economy and Institutions, 2024 )









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业界经历

2024 清华大学经管学院先进工作者

2023 清华大学经管学院先进工作者

2022  清华大学经管学院学生工作优秀奖(一等奖)

2021 清华大学经管学院学生工作优秀奖(二等奖)

2021 XiYue Best Paper Award in CICF (China International Conference in Finance)

2021 Best Paper Award in CFRC (China Finance Research Conference)

2020 Finalist of Best Ph.D. Paper Award in Middlewest Financial Association (MFA)

2020 Finalist of Best Paper in Investment (Financial Management Association, US)

2020 Wharton Mack Institute for Innovation Research (Machine Learning, Networks, and Asset Pricing, 2020)

2018, 2019 Wharton Global Initiatives Research Grant (2018, 2019)


Editorial Services:

Management Science   Associate Editor(Guest)     2024-

ACM Conf. on AI in Finance, Committee, 2024-




















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所获荣誉

To students who are interested in my research,


I hope the following provides insights into my research interests and agenda.


Primarily, I aim to delve both theoretically and empirically into how the connections among firms, financial, physical, and technological, influence corporate actions, portfolio management, business cycles, and systemic risk. I employ microdata to substantiate the macro narratives. 


Besides, I am also super interested in exploring the application and theory of Machine Learning, Deep Learning, and Reinforcement Learning in economics, finance, and business decisions which presently constitutes my research focus. I have initiated multiple projects in this regime and welcome students with robust backgrounds in Finance, Math, Computer Science, or Statistics. My collaboration spans several disciplines from Finance, Economics to Mathematics, Statistics, and Computer Science across various institutions. 

My expertise also lies in harnessing big data and enormous datasets to unveil micro channels that bolster a vibrant macro picture.


My research to date falls into three domains:


1. The first delves into the tangible and intangible linkages between firms, examining their implications on corporate finance, governance, monetary policy, and the broader economy, an area in which I have a special interest in both theory and empirical work.


2. The second explores the employment of statistical learning, deep learning, and reinforcement learning techniques in corporate finance, portfolio or asset management, enriched by regular interdisciplinary discussions with my co-authors from fields like finance, statistics, and computer science across various institutions.


3. The third investigates the interplay between non-structural data (like text, video, and graph) with deep learning and asset management.


My students have been placed in the very top institutions like UPenn Wharton, Princeton, MIT, DE SHAW, Morgan Stanley, Jane Street, Ubiquant Investment, Optival etc., 


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