About

Jinfei Sheng is an Assistant Professor of Finance at the Paul Merage School of Business, University of California, Irvine, where he has been a faculty member since July 2018. He received his Ph.D. in Finance from the Sauder School of Business at the University of British Columbia, Canada. Professor Sheng's research interests span Empirical Asset Pricing, Investments, AI & FinTech, and Behavioral Finance, with a focus on big data, textual analysis, and machine learning. A central theme of his work is understanding the role of information in financial markets. He studies a wide range of information sources, including macroeconomic news, earnings announcements, news articles, online reviews, cryptocurrency whitepapers, and mutual fund prospectuses, and explores their implications for asset prices, investor behavior, and market efficiency. His research also extends to labor finance and financial intermediation. Professor Sheng's work has been published in leading finance journals, and he has presented at premier academic conferences and been invited to speak at top financial institutions. His research has received multiple awards and has been featured in media outlets. In addition, he serves as an Associate Editor for the Annual Review of FinTech and as a reviewer for major finance journals and conferences. At UC Irvine, he developed a new FinTech course for students, serves as the founding faculty advisor for student organizations, and teaches Managerial Finance for MBA students. He has been recognized with excellence in teaching awards at both UCI and UBC.

Research topics

• Computer Science
• Economics
• Financial economics
• Political Science
• Monetary economics
• Macroeconomics
• Business
• Geography

Selected publications

• Do Mutual Funds Walk the Talk? Evidence from Fund Risk Disclosure

  Review of Financial Studies · 2026-05-15

  article1st authorCorresponding

  Abstract We examine the informativeness of fund risk disclosure by combining fund returns and textual data. We develop a novel measure, INF, to capture the explanatory power of disclosed risks for fund returns. Average INF is 55% after controlling for market risk and remains 26–29% after excluding risks related to fund name or strategy. We explain variations in INF through disclosure costs and benefits: (1) Funds with less informative disclosures face SEC comment letters and reduced flow, (2) informative disclosures attract institutional flows, and (3) performance concerns may disincentivize disclosure. Overall, funds face trade-offs between transparency and maintaining their competitive advantage. (JEL G12)

  PublisherDOI

• Replication Code and Pseudo Data for "Generative AI and Asset Management"

  Harvard Dataverse · 2026-02-11

  datasetOpen access

  Replication Code and Pseudo Data for "Generative AI and Asset Management" by Jinfei Sheng, Zheng Sun, Baozhong Yang, and Alan L. Zhang. The Review of Financial Studies, forthcoming.

  PublisherDOI

• Generative AI and Asset Management

  Review of Financial Studies · 2026-05-11

  article1st authorCorresponding

  Abstract Using a novel measure of investment companies’ reliance on generative artificial intelligence (GenAI), we document a sharp increase in GenAI usage by hedge funds after ChatGPT’s 2022 launch. A difference-in-differences test shows that hedge funds adopting GenAI earn 2-4% higher annualized abnormal returns than nonadopters, while non-hedge funds do not benefit. The outperformance originates from funds’ AI talent and ChatGPT’s strength in analyzing firm-specific information. We conduct a new survey of fund managers’ GenAI usage to provide direct validation of our measure and offer additional new insights on how managers adopt GenAI tools in their practice. (JEL C81, G11, G14, G23)

  PublisherDOI

• Trading in Twilight: Sleep, Mental Alertness, and Stock Market Trading

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

• Trading in Twilight: Sleep, Mental Alertness, and Stock Market Trading

  National Bureau of Economic Research · 2025-02-01

  reportOpen access
  PublisherDOI

• Trading in Twilight: Sleep, Mental Alertness, and Stock Market Trading

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

• Asset Pricing in the Information Age: Employee Expectations and Stock Returns

  The Review of Asset Pricing Studies · 2025-01-31 · 10 citations

  article1st authorCorresponding

  Abstract Firms with more positive employee expectations tend to earn higher future returns, delivering annualized abnormal returns ranging from 8% to 11%. Employees’ forward-looking expectations are a stronger return predictor than employee satisfaction, which is backward-looking. Employee expectations can predict returns because they reflect information about firms’ fundamentals that has not yet been reflected in traditional data sources, such as earnings reports. Hedge funds actively trade on this information, consistent with a decay in forecasting power over longer holding horizons. Overall, this paper highlights the importance of labor in asset pricing, specifically from the perspective of employee expectations. (JEL G12, G14)

  PublisherDOI

• Geopolitical Risk and Stock Returns

  SSRN Electronic Journal · 2025-01-01 · 2 citations

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Fund Disclosure Views and Informational Efficiency

  SSRN Electronic Journal · 2024-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Partisan Return Gap: The Polarized Stock Market in the Time of a Pandemic

  Management Science · 2023 · 28 citations

  1st authorCorresponding
  • Political Science
  • Economics
  • Financial economics

  Using two proxies for investors’ political affiliation, we document sharp differences in stock returns between firms likely dominated by Democratic investors (blue stocks) and those dominated by Republican investors (red stocks) during the COVID pandemic. Red stocks have 20 basis points higher risk-adjusted returns than blue stocks on COVID news days (Partisan Return Gap). Lockdown policies, COVID cases, industry and firm fundamentals only explain at most 40% of the return gap. Polarized political beliefs about COVID, revealed through people’s social distancing behavior, contribute to about 40% of the return gap beyond the fundamental channel. Our paper provides partisanship as a novel aspect in understanding abnormal stock returns during the pandemic. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The data and e-companion are available at https://doi.org/10.1287/mnsc.2023.4913 .

  PublisherDOI

Frequent coauthors

• Yun Ke

  7 shared

• David Hirshleifer

  University of Southern California

  5 shared

• Jenny Li Zhang

  4 shared

• Kin Lo

  4 shared

• Adlai J. Fisher

  University of British Columbia

  4 shared

• Charles Martineau

  University of Toronto

  4 shared

• Zheng Sun

  Xinyang College of Agriculture and Forestry

  2 shared

• Mikhail Simutin

  2 shared

Education

• PhD

  University of British Columbia

  2018

Awards & honors

• CICF XiYue Best Paper Award
• Berkeley RDI AI & Decentralization Innovation Award