About

Chloe L. Xie is the Zenon Zannetos (1955) Career Development Assistant Professor in Accounting and an Assistant Professor of Accounting at the MIT Sloan School of Management. Her research focuses on capital market imperfections, such as limits to arbitrage, deviations from von Neumann-Morgenstern preferences, and criminal behavior, and how these frictions shape the information environment. Her work also considers how these frictions influence disclosure decisions, asset pricing, investor decision-making, and non-financial market outcomes. Prior to her academic career, she worked as a consultant advising financial institutions on valuation, mergers and acquisitions, and corporate strategy. Chloe Xie holds a PhD in business administration from Stanford University's Graduate School of Business.

Research topics

• Computer Science
• Business
• Computer Security
• Accounting
• Economics
• Finance
• Actuarial science
• Internet privacy
• Monetary economics

Selected publications

• Informed Trade of Earnings Announcements

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Human + AI in Accounting: Early Evidence from the Field

  Journal of Accounting Research · 2025-01-01 · 10 citations

  articleOpen accessSenior author

  ABSTRACT This paper provides early evidence on the integration and impact of generative artificial intelligence (GenAI) in accounting at the accountant and task levels. Using survey data from 277 professional accountants, we document substantial heterogeneity in adoption patterns, perceived benefits, and concerns about GenAI. Using proprietary field data from an AI‐enabled accounting platform serving 79 small‐ and medium‐sized enterprises, we analyze over 200,000 transaction‐level records. We document that GenAI adoption is associated with significant productivity gains and systematic reallocation of effort away from routine data entry toward business communication and quality assurance tasks. GenAI use is also associated with improvements to financial reporting quality, evidenced by more granular ledgers and faster month‐end closing. Examining human–AI interaction, we find that accountants selectively intervene when AI confidence scores are low, consistent with complementarity between professional expertise and AI. A framed field experiment further shows that while AI assistance improves classification accuracy on average, reliance on non‐consensus AI recommendations can increase the risk of error. Overall, our findings highlight both the promise and the risks of GenAI in accounting and suggest that, in practice, AI is most effective as a tool that augments—rather than replaces—professional judgment.

  PublisherDOI

• Human + AI in Accounting: Early Evidence from the Field

  AEA Randomized Controlled Trials · 2024-10-31 · 1 citations

  dataset1st authorCorresponding
  PublisherDOI

• Discretionary Announcement Timing and Stock Returns 

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Generative AI in Financial Reporting: Early Evidence from the Field

  AEA Randomized Controlled Trials · 2024-10-31

  dataset1st authorCorresponding
  PublisherDOI

• Generative AI in Financial Reporting: Early Evidence from the Field

  AEA Randomized Controlled Trials · 2024-10-31

  dataset1st authorCorresponding
  PublisherDOI

• American Disclosure Options

  National Bureau of Economic Research · 2023-12-01

  reportOpen accessSenior author

  We study strategic disclosure timing by correlated firms in the presence of risk-averse investors.Firms delay disclosures in the hope that positively correlated firms will announce especially good news and lift their own price.Risk premia rise before disclosures, drop when disclosures occur, and then rise again.Conditional risk premia can be much larger than unconditional risk premia.Disclosures are always good news, but disclosures that are only moderately good news induce clustering of disclosures by other positively correlated firms.We present evidence of strategic behavior in earnings announcement timing as predicted by the model.

  PublisherDOI

• American Disclosure Options

  SSRN Electronic Journal · 2023-01-01

  articleOpen accessSenior author
  PublisherDOI

• American Disclosure Options

  SSRN Electronic Journal · 2023-01-01

  articleOpen accessSenior author
  PublisherDOI

• Obfuscation in mutual funds

  Journal of Accounting and Economics · 2021 · 79 citations

  • Computer Science
  • Business
  • Economics
  PublisherOA PDFDOI

Frequent coauthors

• Kerry Back

  9 shared

• Seyed Mohammad Kazempour

  5 shared

• Christina Zhu

  5 shared

• Bruce Carlin

  5 shared

• Yang Song

  University of Washington

  5 shared

• Ed deHaan

  Stanford University

  5 shared

• Jungho Choi

  2 shared

Labs

• MIT Sloan School of ManagementPI