The Impact of Large-Scale Artificial Intelligence Announcements on Equity Risk: A Case Study of Accenture

Silu Yang

Co-Presenters: Individual Presentation

College: College of Business and Public Management

Major: BS.FINANCE

Faculty Research Mentor: Yu, Huaibing  

Abstract:

The impoundment of firm-specific information into market prices remains a cornerstone of financial economics, yet the persistent gap between theoretical instantaneous adjustment and real-world frictions demands deeper investigation. This study is motivated by the increasing complexity of the technology ecosystem, in which "leader" firms, giants with superior liquidity and analyst coverage, serve as primary informational catalysts. Understanding the intra-industry information transfer (IIIT) phenomenon is meaningful not only for academic theory but for practical market efficiency, as it reveals how these leaders’ disclosures prompt a sector-wide revaluation of non-announcing peers. To quantify this critical spillover, we analyze 400 event observations (2021–2026) across Software, Hardware, Semiconductors, and IT Services. By employing event study methodology and OLS regression to track Cumulative Abnormal Returns (CAR) of 24 major competitors, we aim to bridge the gap between disclosure and price discovery. Our objective is to define the transmission elasticity of "earnings surprises" while identifying the temporal lags that characterize modern information friction, offering vital insights for investors navigating interconnected tech markets.