ORCID

0009-0003-9242-9436

Keywords

Marketing-Finance Interface, Artificial Intelligence Disclosure, Corporate Risk Communication, Firm Value, AI-Washing, AI Governance, Signaling Theory

Subject Categories

Accounting | Finance and Financial Management | Strategic Management Policy

Abstract

Firms across industries are racing to signal their artificial intelligence ambitions to investors, yet the financial consequences of how they communicate those ambitions remain poorly understood. Drawing on signaling theory and data from S&P 500 firms, this dissertation addresses that gap through two complementary empirical studies.

The first essay analyzes financial market reactions to over 200 AI announcements by non-technology companies, finding a generally negative stock market reaction to these announcements. Surprisingly, firms with stronger technical capabilities face more severe negative reactions, possibly because investors expect strategic discretion from technically proficient companies or view these announcements as resource diversions from core operations. Announcement specificity serves as a moderating factor: vague AI statements from high-capability non-tech firms trigger less negative market responses than highly specific ones. These findings reveal that investor reactions depend not just on what is communicated about AI, but on who is communicating it and with what level of precision.

The second essay shifts from the event level to the firm level, examining how the volume, tone, and thematic composition of AI risk disclosures in mandatory 10-K filings relate to firm value. Employing text analysis and machine learning techniques on the risk factor sections of S&P 500 annual filings, the study finds that firms engaging more extensively with AI-related risks in their disclosures are associated with significantly higher firm value, suggesting that investors interpret AI risk engagement not as a warning sign but as a signal of managerial awareness and strategic preparedness. Importantly, not all risk narratives carry equal weight, as firms emphasizing regulatory and financial dimensions of AI risk are rewarded with meaningfully higher valuations, while the overall tone of disclosures and broader categories such as technological and reputational risk yield no significant market effects. These findings highlight that disclosure specificity, rather than mere volume or sentiment, is what investors ultimately reward.

Together, these essays advance our understanding of how firms navigate the tension between innovation signaling and strategic transparency under growing regulatory and investor scrutiny of AI-related communications.

Completion Date

2026

Semester

Summer

Committee Chair

Vadakkepatt, Gautham

Degree

Doctor of Philosophy (Ph.D.)

College

College of Business

Department

Marketing

Format

PDF

Document Type

Dissertation

Language

English

Release Date

8-15-2028

Download

Available for download on Tuesday, August 15, 2028

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