Sean Cao Directory Page

Sean Cao

Sean Cao

Director and Co-founder of the AI Initiative for Capital Market Research

Associate Professor

Ph.D., University of Illinois at Urbana-Champaign

Contact

4332G Van Munching Hall

The Smith AI Initiative

Dr. Cao is the Director and Founding faculty of the AI Initiative for Capital Market Research and holds the position of associate professor (with tenure) at the Robert H. Smith School of Business, University of Maryland. Additionally, he is an affiliated professor at Harvard Business School (D^3 Institute). Dr. Cao's research work has gained prominence in respected media outlets such as the Financial Times, CNBC, Bloomberg, The Guardian, Quartz, and IR Magazine. His research papers, some co-authored with PhD students, have received several best paper awards, including the Fama-DFA Prize from Journal of Financial Economics for From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses and the Michael J. Brennan Award from Review of Financial Studies for How to Talk When Machines are Listening: Corporate Disclosure in the Age of AI. He has published in leading journals across finance, accounting, and computer science, including Journal of Accounting Research, Journal of Financial Economics, Journal of Financial and Quantitative Analysis, Review of Financial Studies, The Accounting Review, Contemporary Accounting Research, Management Science, and IEEE Computer. Dr. Cao also contributes as a guest associate editor at Management Science and co-chaired conferences with the Review of Financial Studies(dual submission) on Fintech and Machine Learning.

Dr. Cao is deeply committed to helping business communities through his research. In addition to his role in founding and leading the AI initiative at the University of Maryland, he champions AI's impact in finance and accounting by delivering over 200 invited research talks at universities and national regulatory and policy-advisory agencies, including the Central Bank of Japan, the Central Bank of Thailand, and the U.S. Securities and Exchange Commission. He promotes accessible learning on AI, fintech, and accounting through a free AI textbook for accounting/finance, short-term PhD seminars. and a tutorial blog site (YouTube: Sean Cao_Fintech or Bilibili ID: Seancao). Explore more at his website.

Research Fellowships, Awards and Keynote Talks

News

New Textbook Gives Businesses a Roadmap for Using AI
Smith School professor Sean Cao, director of the AI Initiative for Capital Market Research, has authored a free textbook on AI for…
Read News Story : New Textbook Gives Businesses a Roadmap for Using AI
All in on AI

How Smith Is Preparing Leaders for the Future of Work

Read News Story : All in on AI
Smith Secures $150K for AI Initiative for Capital Market Research
GRF CPAs & Advisors has awarded $150,000 to the University of Maryland’s Robert H. Smith School of Business to seed an AI Initiative…
Read News Story : Smith Secures $150K for AI Initiative for Capital Market Research

Research

Blockchain Technology Could Bring Benefits to the Auditing Industry
Read the article : Blockchain Technology Could Bring Benefits to the Auditing Industry
Why Man + Machine Adds Up to Better Stock Picks
Read the article : Why Man + Machine Adds Up to Better Stock Picks
AI-Powered Pricing: Does It Make the Buying Experience More Fair and Equitable?
Read the article : AI-Powered Pricing: Does It Make the Buying Experience More Fair and Equitable?

Academic Publications

Distributed Ledgers and Secure Multi-Party Computation for Financial Reporting and Auditing
August 2024

To understand the disruption and implications of distributed ledger technologies for financial reporting and auditing, we analyze firm misreporting, auditor monitoring and competition, and regulatory policy in a unified model. A federated blockchain for financial reporting and auditing can improve verification efficiency not only for transactions in private databases but also for cross-chain verifications through privacy-preserving computation protocols. Despite the potential benefit of blockchains, private incentives for firms and first-mover advantages for auditors can create inefficient under-adoption or partial adoption that favors larger auditors. Although a regulator can help coordinate the adoption of technology, endogenous choice of transaction partners by firms can still lead to adoption failure. Our model also provides an initial framework for further studies of the costs and implications of the use of distributed ledgers and secure multiparty computation in financial reporting, including the positive spillover to discretionary auditing and who should bear the cost of adoption.

Author: Sean Cao, Associate Professor (with tenure), Robert H. Smith School of Business, University of Maryland, United States of America

Site Visits and Corporate Investment Efficiency
April 2024

Site visits allow visitors to physically inspect productive resources and interact with on-site employees and executives face to face. We posit that, by allowing visitors to acquire investment-related information and monitor the management team, site visits offer disciplinary benefits for corporate investments. Using mandatory disclosures of site visits in China, we find that corporate investments become more responsive to growth opportunities as the intensity of site visits increases, consistent with the notion that site visits yield disciplinary benefits. We also find that the positive association between site visits and investment efficiency is more pronounced when visitors can glean more investment-related information and when they have stronger incentives and greater power to monitor managers. This positive association is also stronger among firms with more severe agency problems and higher asset tangibility. The overall evidence supports the notion that site visits serve as a unique venue for institutional investors and financial analysts to acquire valuable information and serve a monitoring function, which generates disciplinary benefits for corporate investments.

Author: Sean Cao, Associate Professor (with tenure), Robert H. Smith School of Business, University of Maryland, United States of America

Applied AI for finance and accounting: Alternative data and opportunities
February 2024

Big data and artificial intelligence (AI) have transformed the finance industry by altering the way data and information are generated, processed, and incorporated into decision-making processes. Data and information have emerged as a new class of assets, facilitating efficient contracting and risk-sharing among corporate stakeholders. Researchers have also increasingly embraced machine learning and AI analytics tools, which enable them to exploit empirical evidence to an extent that far surpasses traditional methodologies. In this review article, prepared for a special issue on Artificial Intelligence (AI) and Finance in the Pacific-Basin Finance Journal, we aim to provide a summary of the evolving landscape of AI applications in finance and accounting research and project future avenues of exploration. Given the burgeoning mass of literature in this field, it would be unproductive to attempt an exhaustive catalogue of these studies. Instead, our goal is to offer a structured framework for categorizing current research and guiding future studies. We stress the importance of blending financial domain expertise with state-of-the-art data analytics skills. This fusion is essential for researchers and professionals to harness the opportunities offered by data and analytical tools to better comprehend and influence our financial system.

Author: Sean Cao, Associate Professor (with tenure), Robert H. Smith School of Business, University of Maryland, United States of America

How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI
The Review of Financial Studies, March 2023

Growing AI readership (proxied for by machine downloads and ownership by AI-equipped investors) motivates firms to prepare filings friendlier to machine processing and to mitigate linguistic tones that are unfavorably perceived by algorithms. Loughran and McDonald (2011) and BERT available since 2018 serve as event studies supporting attribution of the decrease in the measured negative sentiment to increased machine readership. This relationship is stronger among firms with higher benefits to (e.g., external financing needs) or lower cost (e.g., litigation risk) of sentiment management. This is the first study exploring the feedback effect on corporate disclosure in response to technology.

Sean Cao, Associate Professor (with tenure), Robert H. Smith School of Business, University of Maryland, United States of America

“From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses,” forthcoming in Journal of Financial Economics*

An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. Nevertheless, humans win ‘‘Man vs. Machine’’ when institutional knowledge is crucial, e.g., involving intangible assets and financial distress. AI wins when information is transparent but voluminous. Humans provide significant incremental value in ‘‘Man + Machine’’, which also substantially reduces extreme errors. Analysts catch up with machines after ‘‘alternative data’’ become available if their employers build AI capabilities. Documented synergies between humans and machines inform how humans can leverage their advantage for better adaptation to the growing AI prowess.

*American Association of Individual Investors (AAII) Best paper award winner, 2022 Midwest Finance Association Best Paper Award Winner, 2022 Global AI Finance Conference Best Paper Award Winner, 2022 CFRC Conference, PBC School of Finance, Tsinghua University Best Paper Award Winner, 2022 Annual Conference in Digital Economics, ACDE Best Paper Award Winner in Asset Pricing, 2022 SFS Cavalcade Asia-Pacific Conference

Sean Cao (Robert H. Smith School of Business, University of Maryland)