Forging the Future of Work
Learning from Earnings Calls: Graph-Based Conversational Modeling for Financial Prediction
Information Systems Research
Earnings conference calls are valuable venues for business communication. Empirical research has shown that the content of earnings calls contains predictive signals about future market risks, which has motivated a line of computational studies that utilize earnings transcripts for financial forecasting tasks. However, earnings call transcripts are typically very long, and the predictive signals within them are often sparsely distributed across different sections of the transcript. As a result, existing computational methods often fail to capture the essential information within the transcript that is relevant to market risks. In this work, we design a novel method to model earnings transcripts as a conversational graph where graph nodes represent discussed topics and graph edges indicate the similarity between topics. By doing so, we aim to explicitly model what is discussed (i.e., topical content), how it is discussed (e.g., cross-referencing or newly introduced topics), and in what manner it is discussed (e.g., sentiment and other linguistic features) within the transcript. We then leverage a graph neural network to learn transcript-level representations for financial risk forecasting. Experimental results show that the proposed method significantly reduces risk forecasting errors, demonstrating its capability of modeling earnings call transcripts. Moreover, this predictive power holds even after considering the firm’s fundamentals, and the performance gain over baseline models continues to grow as transcript length increases. The interpretability analysis shows that the proposed method identifies cross-referencing and newly introduced topics as influential for risk prediction. Moreover, the model tends to associate transcripts with a higher number of new topics in the Q&A section, more cross-referencing discussions, and more positive sentiment with lower predicted financial risks. This work contributes methodologically by proposing a novel predictive approach specifically tailored to the domain-specific challenge of transcript-based risk forecasting. We also discuss key design insights and implications.
Yi Yang (HKUST), Yixuan Tang (HKUST), Yangyang Fan (HK PolyU), and Kunpeng Zhang (UMD)
Analytics for Finance and Accounting: Data Structures and Applied AI
Analytics for Finance and Accounting: Data Structures and Applied AI bridges the gap between technical data science education and domain-specific applications in accounting and finance. Designed for students and instructors seeking practical exposure to AI-driven financial analytics, the book prioritizes understanding real-world business data—structured and unstructured—before introducing machine learning techniques. It empowers learners to apply AI tools, such as GPT and pre-trained language models, to analyze corporate disclosures, earnings calls, ESG reports, and other financial documents. Minimizing programming prerequisites, the book integrates video tutorials and applied projects to support hands-on learning. It serves as both a foundational text for graduate-level data analytics courses and a modular supplement for traditional finance and accounting curricula. By combining domain expertise with modern computational tools, this book equips the next generation of financial professionals with the skills to thrive in a data-intensive economy.
Sean Cao, Associate Professor, 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
Journal of Financial Economics, July 2024
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.
Sean Cao, Robert H. Smith School of Business, University of Maryland
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
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.
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.
Sean Cao, Associate Professor (with tenure), Robert H. Smith School of Business, University of Maryland, United States of America