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Corporate Disclosure

Paper Session

Monday, Jan. 5, 2026 10:15 AM - 12:15 PM (EST)

Loews Philadelphia Hotel, Commonwealth Hall A2
Hosted By: American Finance Association
  • Eitan Goldman, Indiana University

Corporate Communications with Government Executive Officials: Evidence from the STOCK Act

Amanda Heitz
,
Tulane University
Youan Wang
,
Xiamen University
Yu Zhige
,
Xiamen University

Abstract

This paper examines how insider trading restrictions on government officials affect corporate disclosure. The Stop Trading on Congressional Knowledge (STOCK) Act, enacted in 2012, prohibited over 28,000 executive branch officials from profiting from non-public information, altering their incentives to share regulatory and policy insights with firms. While the Act aimed to enhance transparency and curb unethical behavior, we find that it inadvertently weakened firms’ access to valuable government-sourced information, leading to changes in corporate disclosure decisions. Using a difference-in-differences design, we find that firms with significant government contracts reduced management forecast frequency by 8.62% post-STOCK Act. This effect is strongest among firms highly dependent on government sales, politically engaged firms, and those in competitive industries, suggesting that restricted access to regulatory insights increased uncertainty about future government-related business conditions. Textual analysis of earnings calls reveals that affected firms discuss government contracting less while emphasizing political risk more frequently, reflecting heightened concerns about regulatory unpredictability. Analyst forecast errors increase, while dispersion declines, indicating greater difficulty in assessing firm performance despite reaching more homogeneous estimates. We further document a decline in price informativeness and an increase in the implied cost of capital, highlighting broader capital market consequences. Our findings suggest that by changing the incentives of executive branch officials, the STOCK Act reduced firms’ ability to obtain informal regulatory insights, weakening their voluntary disclosure practices and reducing the overall flow of information in capital markets. This study contributes to the literature on regulation, political connections, and corporate disclosure by demonstrating how policies targeting individual actors can have unintended firm-level consequences, ultimately affecting market efficiency and firm behavior.

How Do Multiple Regulators Regulate? Evidence from Fairness Opinion Providers’ Conflict of Interest Disclosures

Philip Berger
,
University of Chicago
Rachel Geoffroy
,
Ohio State University
Claudia Imperatore
,
Bocconi University
Lisa Liu
,
Columbia University

Abstract

Producing and disseminating financial reporting disclosures often involves multiple parties operating under multiple regulators. Conflict of interest disclosures by fairness opinion providers in mergers and acquisitions are an important example. The Securities and Exchange Commission (SEC) oversees the companies responsible for disseminating in their SEC filings any conflicts of interest of their fairness opinion providers. The Financial Industry Regulatory Authority (FINRA) regulates the fairness opinion providers who supply conflict of interest information to their client companies. We use the fairness opinion setting to assess the effectiveness of each regulator’s enforcement efforts and to examine whether they improve or degrade enforcement when they enforce jointly. We find each regulator is effective when acting independently. However, when FINRA begins to regulate the disclosure in transactions previously enforced by only the SEC, regulatory effectiveness diminishes. The result suggests the SEC delegates some of the enforcement tasks to FINRA when FINRA is present even though FINRA does not directly enforce the SEC filers. Our cross-sectional tests show that the reduction in enforcement effectiveness under joint regulators is smaller when the SEC faces fewer resource constraints or joint oversight is more needed. Our findings provide implications for the design of disclosure regulations.

Voluntary Disclosure, Misinformation, and AI Information Processing: Theory and Evidence

Jeremy Bertomeu
,
Washington University in St. Louis
Yupeng Lin
,
National University of Singapore
Yibin Liu
,
National University of Singapore
Zhenghui Ni
,
National University of Singapore

Abstract

This paper investigates the impact of generative AI on firms’ voluntary disclosure choices. Our theoretical model highlights a trade-off between AI’s improved ability to process disclosed information and its potential for misinformation, modeled as a random “hallucination” unrelated to the firms’ fundamentals. We predict that increased AI processing leads to more strategic non-disclosure due to two related economic forces. First, hallucinations provide additional camouflage after strategic non-disclosure. Second, because users consider the risk of misinformation, they discount observed marginal disclosures, further reducing the benefit of disclosure. To test our predictions, we leverage OpenAI’s launch of ChatGPT in November 2022 as a shock to AI processing. Consistent with the theory, firms with more AI processing reduce their voluntary disclosures. Further, the introduction of ChatGPT reduces information processing failures, which manifests in increased information processing speed. Combining the crowding-out effect on information supply and the positive impact on information processing speed, we do not find evidence of a net increase in information quality.

Discussant(s)
Mayur Choudhary
,
London Business School
Joseph Kalmenovitz
,
University of Rochester
Jan Schneemeier
,
Michigan State University
JEL Classifications
  • G3 - Corporate Finance and Governance