Key Areas of Research
Building credible commitments via board ties: Evidence from the supply chain
November 2025
Using a novel dataset that provides a comprehensive coverage of U.S. firms' industrial supply chain relationships, we find that firms with innovation specific to a buyer are more likely to share a common director with that buyer. This association is stronger when the buyer has a larger number of alternative suppliers. We further find that when a supplier–buyer pair shares a common director, the supplier's R&D investment is more sensitive to the investment opportunities of its buyer. Moreover, such pairs tend to have longer supply chain relationships. Taken together, our findings demonstrate that board ties serve as a credible commitment mechanism to support exchange along the supply chain and safeguard suppliers' buyer-specific investments.
Rebecca Hann, University of Maryland-College Park; Musa Subasi, University of Maryland-College Park; Yue Zheng, Hong Kong University of Science and Technology
Biodiversity Entrepreneurship
Review of Finance
We study an emerging class of start-up organizations focused on biodiversity conservation and the challenges they face in financing these ventures. Using a novel machine learning method, we identify 630 biodiversity-linked start-ups in PitchBook and compare their financing dynamics to other ventures. Biodiversity start-ups raise less capital but attract a broader coalition of investors, including not only venture capitalists (“value investors”) but also mission-aligned impact funds and public institutions (“values investors”). Values investors provide incremental capital rather than substituting value investors, but funding gaps persist. We show biodiversity-linked start-ups use social media activity to help connect with value investors. Our findings can inform policy and practice for mobilizing private capital toward biodiversity preservation, emphasizing hybrid financing models and strategic communication.
Sean Cao, Robert H. Smith School of Business, University of Maryland
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
EPA Scrutiny and Voluntary Environmental Disclosures
Review of Accounting Studies
Market participants have called on the SEC to address the lack of disclosures about firms’ environmental impacts, investments, and exposures. However, the frictions that obstruct the flow of environmental information are not well understood. I shed light on these frictions by examining whether scrutiny by the Environmental Protection Agency (EPA) restricts the firm’s voluntary environmental disclosures in earnings conference calls. Consistent with the notion that EPA scrutiny gives rise to disclosure frictions, I find a negative relation between EPA scrutiny and the environmental disclosures of scrutinized firms. This negative relation is concentrated among firms without environmental expert directors, suggesting that environmental governance mitigates the chilling effect of EPA scrutiny. In terms of disclosure quality, I show that environmental disclosures include fewer quantitative details under EPA scrutiny. Collectively, these findings provide insights into the frictions that restrict the flow of environmental information to market participants, an important issue given the SEC’s efforts to improve current disclosure practices.
Mark Zakota, Assistant Professor, Robert H. Smith School of Business, University of Maryland
CFO Narcissism and the Power of Persuasion Over Analysts: A Mixed-Methods Approach
Review of Accounting Studies
We study the role of CFO narcissism in the intent and ability to positively influence sell-side analysts’ perceptions of the firm. Consistent with narcissists casting favorable impressions on others, we find CFO narcissism is associated with overly optimistic analyst valuations. We then study public persuasion attempts by analyzing conference call transcripts and private persuasion attempts through a laboratory study. In the conference call setting, we provide evidence that narcissistic CFOs use more persuasive language and are more inclined to call on bearish analysts, both of which we link to higher price targets. In the lab study, we simulate a one-on-one conversation and find that narcissists are especially more likely to use coercive methods to induce higher valuations (e.g., threatening to remove private lines of communication). Collectively, we provide evidence that narcissistic CFOs exercise persuasion tactics to favorably influence analysts’ perceptions of firm value.
Chad Ham and Mark Piorkowski - Indiana University; Nick Seybert - University of Maryland; Sean Wang - Southern Methodist University
AI-powered Analysts
We explore how brokerage firms’ investments in artificial intelligence (AI) affect their analysts’ information production. We find that analysts employed at brokerage firms with greater AI integration issue more accurate earnings forecasts. Cross-sectional analyses reveal that AI’s benefits are more pronounced for analysts with less firm-specific experience and when the firm’s disclosures are more readable. Further tests indicate that a key mechanism driving the improvement in forecast accuracy is that AI adoption helps mitigate the adverse effects of analyst decision fatigue and optimism bias. Finally, we find that forecast revisions made by AI-powered analysts are more informative to capital markets. Overall, our evidence points to the advantageous impact of AI on information production capabilities of financial analysts.
Michael Kimbrough, Musa Subasi, Liu Yang
Do Credit Rating Agencies Learn from the Options Market?
Management Science, November 2024
Do credit rating agencies (CRAs) learn from the options market? We examine this question by exploring the relation between options trading activity and credit rating accuracy. We find that as options trading volume increases, credit ratings become more responsive to expected credit risk and exhibit greater ability to predict future defaults. We also find that CRAs rely more on the options market as a source of ratings-related information when firm default risk is higher, options trading is more informative, manager-provided information is of lower quality, and firm uncertainty is higher. Our results are robust to a number of sensitivity tests, including alternative measures of options trading and credit rating accuracy. We reach similar inferences using various approaches to address endogeneity issues, including difference-in-difference analyses and an instrumental variables approach. Overall, our findings are consistent with the view that CRAs incorporate unique information from the options market into their rating decisions which, in turn, improves credit rating accuracy.
Musa Subasi, University of Maryland-College Park
Paul Brockman, Lehigh University
Jeff Wang, San Diego State University
Eliza Zhang, University of Washington-Tacoma
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
Seductive Language for Narcissists in Job Postings
Management Science
Prior research indicates that narcissistic executives engage in earnings management and other negative organizational behaviors, and many studies ponder why firms hire such individuals, especially into corporate accounting positions. Utilizing a selection of terms from real-world job postings that we characterize as either describing a “Rule-Bender” or “Rule-Follower" candidate, we first conduct several validation studies which reveal that these terms vary predictably across types of job postings, that people generally agree with our categorization of these terms, and that Rule-Benders are viewed as possessing worse managerial skills but a higher proclivity for unethical behavior. We then demonstrate that narcissistic job seekers are more attracted to job postings that describe the ideal candidate using Rule-Bender terms for both general positions (Experiment 1) and senior accounting positions (Experiment 2). Finally, we examine firm characteristics that might lead professional recruiters to incorporate Rule-Bender language into Chief Accounting Officer job postings and find that Rule-Bender terms are preferred for higher-growth, higher-innovation firms (Experiment 3), and when more aggressive reporting would benefit the firm (Experiment 4). Our results suggest that recruiters’ language choices can attract Rule-Bending narcissists to firms, perhaps even in unintended circumstances.
Jonathan Gay (University of Mississippi), Scott Jackson (University of South Carolina), Nick Seybert (University of Maryland)