This website uses cookies.

By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device to enhance site navigation and analyze site performance and traffic. For more information on our use of cookies, please see our Privacy Policy.

  1. Home
  2. ASSA Annual Meeting
  3. 2027
  4. Preliminary Program
  5. AI, Technology Disruptions and Corporate Finance
« Back to Results

AI, Technology Disruptions and Corporate Finance

Paper Session

Monday, Jan. 4, 2027 10:15 AM - 12:15 PM (EST)

Westin DC Downtown
Hosted By: Association of Financial Economists
  • Chair: Lemma W. Senbet, University of Maryland

New Technology Sectoral Disruptions

Tolga Caskurlu
,
University of Amsterdam
Gerard Hoberg
,
University of Southern California
Gordon M. Phillips
,
Dartmouth College

Abstract

We construct a novel measure of technology sectoral disruptions (TSDs) using a dynamic text-based spatial model of patents based on the extent to which innovation is suddenly highly correlated across multiple industries. We identify multiple TSDs occurring over a 70-year period of time. Abnormal stock returns and insider trading indicate that TSDs are largely unexpected and generate positive and long-lasting value gains. Impacted small firms initially increase equity issuance, reduce equity payouts, and increase both R&D and asset growth and experience increased valuations consistent with Schumpeter’s 1912 theory of creative destruction and Arrow’s 1962 theory of innovation by smaller firms. Large firms, in contrast, on average reduce R&D and capital expenditures and experience declining valuations and decreased sales growth.

CFOs Meet LLMs

John R. Graham
,
Duke University
Campbell R. Harvey
,
Duke University
Manish Jha
,
Georgia State University

Abstract

Business sentiment is a closely watched economic signal, but measuring it is slow and costly: surveys reach only a few hundred firms, arrive periodically, and take time to compile. We show that large language models hold the potential to address these shortcomings. We prompt an LLM to role-play as the CFO of a specific company at a specific date and focus on the economic-optimism question on the Duke–Federal Reserve CFO Survey over 2002–2025. We find that the LLM reproduces individual human responses: the predicted optimism score significantly forecasts the CFO’s actual answer, surviving firm and year-quarter fixed effects and a control for the most recent prior response. Predictive accuracy increases with the amount of information supplied, as both respondent history and firm characteristics improve fit, and the relationship persists under quarterly aggregation. With appropriate conditioning, LLMs may be able to serve as credible digital twins of executives, offering scalable, high-frequency expectations data for financial research and policy.

Generative AI Adoption, Labor Restructuring, and Corporate Capital Structure

Iftekhar Hasan
,
Fordham University
Yunying Huang
,
Fordham University
David Matsa
,
Northwestern University
Buhui Qiu
,
University of Sydney

Abstract

We study the connection between Generative AI and capital structure. We use 350
million LinkedIn job postings from Revelio Labs to construct a firm-level measure of
Generative AI adoption based on hiring for “integrator” roles that build or deploy large-language-
model systems. Using a staggered difference-in-differences approach, we find
that firms adopting Generative AI exhibit reduced leverage and improved debt-service
capacity. Results are consistent and robust across alternative leverage measures and
modern staggered-adoption estimators, and withstand identification checks including
entropy balancing, stacked-cohort matching, parallel-trends tests, synthetic DiD, and
two instrumental variable strategies based on the evolution of LLM capabilities and
geographic proximity to AI research universities. One mechanism reveals that Generative
AI adoption shifts firms toward intangible, less collateralizable capital and raises
technology risk, consistent with lower debt capacity. Second, adoption increases mass
layoffs, especially among firms with high ex ante generative-AI exposure, and deleveraging
is stronger in low-union industries, consistent with elevated labor frictions on
the displaced professional workforce and reduced reliance on debt as a labor-discipline
device.

Discussant(s)
Josh Lerner
,
Harvard Business School
Anne Hansen
,
Federal Reserve Bank of Richmond
Mariassunta Giannetti
,
Stockholm School of Economics
JEL Classifications
  • O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights
  • G3 - Corporate Finance and Governance