2026年             国际货币基金组织(IMF, Visiting Economist (兼职))

2025年-           清华大学经管 (深圳）计算金融研究中心副主任

2023年-           清华大学数智审计研究中心研究员

2021年-           清华大学经管学院 助理教授

《高级实证资产定价》（博士生课程，2023-至今, 秋季学期）

《公司金融与资本市场前沿》（博士生课程，2026-至今, 春季学期）

《金融大数据的方法与应用》（研究生课程，2021-2024学年，秋季学期）

《机器学习的金融前沿》(研究生课程, 2025学年-至今，秋季学期 ）

《深度学习及其金融应用》（本科课程，2022学年-至今，春季学期）

《机器学习及其金融应用》（本科课程，2022-2025学年秋季）

《高级公司金融》（博士课程，2021年秋季、2022年秋季）

《中央银行家的机器学习》（印度尼西亚中央银行高级管理人员工商管理硕士课程，2024年）

人工智能，资产定价，公司金融，大数据，网络经济学，宏观经济学，企业创新，中国经济

Papers:

1. Network Regression and Supervised Centrality Estimation, with Jeff Junhui Cai (U Notre Dame), Ran Chen (U Washington), Haipeng Shen (HKU),  Dan Yang (HKU), and Linda Zhao (Wharton UPenn) (Journal of the American Statistical Association, 2026)

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

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

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

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

4. "Hierarchical Vintage Sparse PCA. Discussion on the paper by Rohe and Zeng",  with Jeff Junhui Cai (U Notre Dame), Dan Yang (HKU), Linda Zhao (Wharton UPenn), Journal of the Royal Statistical Society. Series B: Statistical Methodology, 84, 2023

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

   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 (Imperial College), Jeff Junhui Cai (U Notre Dame), Xian Gu (Durham), QJ Jun Qian (Fudan FISF), and Linda Zhao (Wharton UPenn) (Revise&Resubmit, Management Science)

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

   - 2021 CFRC Best Paper Award (China Financial Research Conference)

7. Tiered Intermediation in Equity-holding Networks, with Robert Townsend  (MIT) and Yu Shi (IMF) (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)

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

   - 2020 Finalist of Best Paper Award in MFA (Midwest Financial Association Annual Meeting, US)

8. Generative AI for Analysts (with Jian Xue (Tsinghua),  Qian Zhang (Tsinghua), Revise & Resubmit)

   - Won the Best Paper Award at Conference on Machine Learning and Applications in Finance, SIEM, 2025

9. Optimal Assortment and Pricing via Generalized MNL Models with Novel Poisson Arrivals (with Ran Chen (U Washington), Jeff Junhui Cai (U Notre Dame), Qitao Huang (Tsinghua), Martin Wainwright (MIT), Linda Zhao (Wharton UPenn), 2024 Econometric Society Interdisciplinary Frontiers (ESIF) conference on Economics and AI+ML (Cornell), INFORMS Annual 2024) (Under Review)

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

10. ChatGPT and DeepSeek: Can They Predict  the Stock Market and  Macroeconomy (with Jian Chen (Xiamen U), Guohao Tang (Hunan U), and Guofu Zhou (U Washington), Submitted)

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

12. Centralization or Decentralization - The Evolution of State Ownership in China, with Franklin Allen (Imperial College), Jeff Junhui Cai (U Notre Dame), Xian Gu (Durham), QJ Jun Qian (Fudan FISF), Linda Zhao (Wharton UPenn) (Submitted)

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

13. Link Complexity and Cross Predictability, with Guofu Zhou (U Washington) )

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

14. Networks and Business Cycles, with Yucheng Yang (U Zurich)

15. Automation-Induced Innovation Shift (with Lin William Cong (Cornell), Yao Lu (Tsinghua), and Hanqing Shi (Tsinghua), Submitted). 

    - Won the CICF Best Paper Award (China International Conference in Finance, 2025)

    - Won the Global Association of Risk Professionals Research Excellence Award (MRS, 2025)

    - Won the Best Paper Award (第四届全国金融研究院长论坛)

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

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

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

    (Conference on Frontiers in Machine Learning and Economics: Methods and Applications (Chicago Booth 2025))

19. Global Minds and Local Gains: An AI Talent Tale of US and China (with Hanming Fang (UPenn), Xian Gu (Durham), Hanyin Yan (Tsinghua))

    The paper ''Global Minds and Local Gains: An AI Talent Tale of US and China'' was splitted into two papers. (with Hanming Fang (UPenn), Xian Gu (Durham), Hanyin Yan (Tsinghua))

    (Conference on Frontiers in Machine Learning and Economics: Methods and Applications (Chicago Booth 2025))

    Keywords: LLMs, AI, Talent Migration, Spillover

    - Won the Best Paper Award (CAFRE, 2025)

20. Interpretable Factors of Firm Characteristics (with Yuxiao Jiao (CUFE), Guofu Zhou (U Washington), Yingzi Zhu (Tsinghua), Submitted) 

21. ``AI-as-Cofounder'': Generative AI for Entrepreneurship (with Jeff Junhui Cai (U Notre Dame), Xian Gu (Durham U), Liugang Sheng (CUHK), Mengjia Xia (UPenn), Linda Zhao (Wharton UPenn), Submitted, Asian Bureau of Finance and Economic Research (ABFER), 2026, Adam Smith WorkShop (London Business School,2026))

22. AI Inventions: US and China (with Hanming Fang (UPenn), Xian Gu (Durham), Hanyin Yan (Tsinghua U), 2026)

23. Sequential Innovation: Zero-to-One and One-to-N (with Fei Li and Mofei Zhao, come out soon, 2026)

Selected projects in progress: 

1.     Novelty Premium and LLMs ( prelimary draft is available)

2.     Learning and Talent Reallocation (with Guodong Chen, Fawang Tian, Yiming Qian)
   

3.     Competitive Narratives (with Junhui Jeff Cai (U Notre Dame), Jiacui Li (U Utah),  Shangjin Wei (Columbia U))

4.     Textual Regression: Doubly-Supervised Learning (with Junhui Jeff Cai (U Notre Dame), Zikai Lin (Tsinghua), Jun Liu (Tsinghua/Harvard), Xinyue Yang (Tsinghua))

5.     Debiasing Training Leakage in LLMs (with  Junhui Jeff Cai, Hongkai Jiang, Come out soon)
   

           - We propose a statistical framework to debias the looking-ahead bias prevalent in the pre-trained LLMs 

6.      Interpretable Arbitrage in Asset Pricing 

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 )

Fundings:

1.  AI, Science, and Technology of 100 Years (PI), 小林实中国经济研究基金 (Kobayashi China Economic Research Fund, 2026)

2. Generative AI and its Implications on Information Disclosure (Co-PI), Chinese National Science Foundation (Grant ID 72442014),2025-2028.

3. LLMs and Competitive Narratives (PI), Chinese State Council, Macro Department of Development Research Center, 2024-2025.

4. AI in Behavior Finance and Platform (PI), Harvest Fund, 2024-2028.

5. News, LLMs, and Macro Forecasts in the Era of GPTs (PI), Chinese State Council, Macro Department of Development Research Center, 2023.

6. State-Owned Economy and State-Owned Enterprises (Leading member, Chinese State Council, China State owned Assets Supervision and Administration Commission),2023.

7. Innovations of Non-Public Firms - Micro and Macro Perspectives (Leading Member), National NSFC (grant ID 72273071), 2023.

8. Tsinghua University Initiative Scientific Research Program (PI), 2022.

9. Tsinghua University School of Economics and Management Research Grant (PI), 2022

10. Tsinghua University, Oversea Initiative Fund (PI),2022.

无

1. 2025 The Best Paper Award, 20th Annual Conference on Asia-Pacific Financial Markets, 2025

2. 2025 The Best Paper Award (第四届全国金融研究院长论坛)

3. 2025 The Best Paper Award at Conference on Machine Learning and Applications in Finance, SIEM

4. 2025 The Best Paper Award (CAFRE)

5. 2025 Global Association of Risk Professionals Research Excellence Award (MRS, 2025)

6. 2025 The CICF Best Paper Award (China International Conference in Finance)

7. 2024 清华大学经管学院先进工作者

8. 2023 清华大学经管学院先进工作者

9. 2022 清华大学经管学院学生工作优秀奖(一等奖)

10. 2021 清华大学经管学院学生工作优秀奖(二等奖)

11. 2021 The XiYue Best Paper Award in CICF (China International Conference in Finance)

12. 2021 The Best Paper Award in CFRC (China Finance Research Conference)

13. 2020 The Finalist of Best Ph.D. Paper Award in Middlewest Financial Association (MFA)

14. 2020 The Finalist of Best Paper in Investment (Financial Management Association, US)

15. 2020 Wharton Mack Institute for Innovation Research (Machine Learning, Networks, and Asset Pricing, 2020)

16. 2019 Wharton Global Initiatives Research Grant (2018, 2019)

17. 2018 Wharton Global Initiatives Research Grant
    

Academic Services:

Management Science   Associate Editor(Guest)     2024-now

Program Committee, 2024 ACM International Conference on AI in Finance

Committee, SFS (Society for Financial Studies) Cavalcade Asia-Pacific, 2025

To students who are interested in my research,

I am actively hiring RAs, and I hope the following provides insights into my research interests and agenda.

Primarily, I am  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, Economics, Math, Computer Science, or Statistics, I am especially paying attention to  students who are persistent and enthusiastic about research or challenges . 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 (structured or unstructured) to unveil micro channels that bolster a vibrant macro picture.

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

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, CMU, DE SHAW, Morgan Stanley, Jane Street, Ubiquant Investment, Optiver, etc.,