Sean Cao Directory Page

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 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

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

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)