Sean Cao Directory Page
Sean Cao
Director and Co-founder of the AI Initiative for Capital Market Research
Associate Professor
Ph.D., University of Illinois at Urbana-Champaign
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 primarily revolves around the applications and implications of AI for capital markets, encompassing both accounting and finance studies. His overarching aim is to establish a meaningful connection between academic AI research and its impact on industry applications and regulatory considerations, its influence on industry applications and regulatory considerations, ultimately benefiting both business faculty and students.
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. Over the years, he has delivered more than 100 invited research talks at national regulatory and policy-advisory agencies, such as Central Bank of Japan, Central Bank of Thailand, U.S. Securities and Exchange Commission as well as major research universities, including Columbia, Harvard, MIT Sloan, University of Cambridge, and University of Michigan. His research papers, some co-authored with PhD students, have received several best paper awards at prestigious conferences and have been published in leading journals in 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 serves as a track chair for Big Data, Fintech, and Blockchain at the American Accounting Association.
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 has been honored with an award from the Deloitte Initiative for AI and Learning (DIAL). In the Deloitte program, he plays a pivotal role in developing AI solutions for social inclusion and climate change. His research has been presented and applied in leading professional firms such as Alliance Bernstein (AB), Balyasny Asset Management (BAM), State Street Boston Headquarters, Wolfe Investment Research, Accenture, Grant Thornton, and NIV Asset.
In 2020 and 2022, Dr. Cao co-chaired conferences with the Review of Financial Studies (dual submission) on Fintech and Machine Learning. These conferences attracted an average of 150 high-quality submissions annually in the field of AI and Fintech, totaling approximately 500 working papers over three years, contributed by preeminent scholars in finance and accounting. For more information, please visit https://sites.google.com/view/seancao/home.
Dr. Cao's passion for research extends to his teaching. He is dedicated to providing students with the latest learning materials, emphasizing a robust data analytics component to prepare them for successful careers. His teaching abilities have been recognized, with him being ranked in the top 10 percent for outstanding teaching university-wide at the University of Illinois at Urbana-Champaign and well-received in the classroom at the University of Maryland. In his PhD seminar at the University of Maryland, he blends classical capital market research with emerging technologies. Dr. Cao has been invited externally by major research universities to teach short-term doctoral seminars on AI and Fintech for both finance and accounting departments. Furthermore, he also writes a AI textbook for accounting/finance and runs a tutorial blog site (YouTube: Sean Cao_Fintech or Bilibili ID: Seancao_) that designed to assist scholars outside of computer science in seamlessly integrating machine learning into finance and accounting research.
Research Fellowships, Awards and Keynote Talks
- Award recipient of Deloitte Initiative for AI and Learning for developing trustworthy AI for social equity and climate change.
- PanAgora Asset Management’s Dr. Richard A. Crowell Memorial Prize (with coauthors), Visual Information and AI Divide: Evidence from Corporate Executive Presentations.
- For those intrigued by the application of AI in extracting trading signals from corporate production pictures, we invite you to explore this research. Additionally, our paper delves into documenting the potential AI divide among various investor groups.
- American Association of Individual Investors (AAII) Best paper award winner (with coauthors), From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses, 2022 Midwest Finance Association
- Best Paper Award Winner (with coauthors), From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses, 2022 Global AI Finance Conference
- Best Paper Award Winner (with coauthors), From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses, 2022 CFRC Conference, PBC School of Finance, Tsinghua University
- Best Paper Award Winner (with coauthors), From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses, 2022 Annual Conference in Digital Economics, ACDE
- Best Paper Award Winner in Asset Pricing (with coauthors), From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses, 2022 SFS Cavalcade Asia-Pacific Conference
- Winner of the AMTD FinTech Centre Prize (with coauthors), How to Talk When Machines are Listening: Corporate Disclosure in the Age of AI, 2022 Asian Finance Association Conference
- Best Paper Award Winner (with coauthors), How to Talk When Machines are Listening: Corporate Disclosure in the Age of AI, 2021 CAPANA Research Conference
- Keynote Speaker for Pacific-Basin Finance Journal (PBFJ) special issue conference on “Artificial Intelligence and Machine Learning in Corporate Finance”, December 2023.
- Keynote Speaker for “Machine Learning, AI and FinTech in Capital Market” at Stock Exchange of Thailand, Bangkok, Thailand, July 2023
- Panel Speaker at “2023 AAAI Conference on Artificial Intelligence”
- Panel Speaker at the Academy of Management conference for “Textual and Voice Analysis for CEO Traits and Emotion: Challenges and Future Research Directions”, August 2022
- Keynote and Panel Speaker for “Latest NLP models in capital market research” at Technical University of Munich (Technische Universität München), Germany, Nov 18 - 19, 2021
- Panel Speaker at “Toronto Rotman CPA Conference” (with 2,000 FinTech professionals and industry participants), Canada
- See video broadcasted on Feb 17, 2021
News
Research
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)