Priced in? Verifying and Testing Stock-Market Reactions to CHIPS Act Award Announcements

Authors

  • Alexander Li Tang Department of Finance, George Mason University, Fairfax, VA
  • Lei Gao Department of Finance, George Mason University, Fairfax, VA

DOI:

https://doi.org/10.13021/jssr2026.5643

Abstract

The 2022 CHIPS and Science Act allocated tens of billions of dollars in semiconductor manufacturing subsidies, disbursed through a series of company-specific award announcements in 2024. Whether these announcements produced abnormal stock price reactions among awardee firms, and whether reaction size scaled with award size, was tested using a market-model event study. Announcement dates, funding amounts, and corporate ticker matches for each of 17 CHIPS Act awards were cross-checked against primary Department of Commerce and NIST press releases, replacing an initial secondary-source dataset, with a timeline of confounding company news, such as earnings releases and M&A activity, under construction for each event window. Across 15 publicly traded awardees, no statistically significant average abnormal stock return was found around the first, preliminary-terms announcement: the mean cumulative abnormal return over a three-day window was +2.6% (t = 0.99), with only 8 of 15 firms showing a positive reaction. This null result held across multiple abnormal-return models and nonparametric test statistics, and was supported by randomization inference. An apparent relationship between award size and reaction magnitude was found to be driven almost entirely by a single distressed micro-cap firm, Wolfspeed, whose $750 million award equaled 52% of its market capitalization; excluding this outlier collapsed the relationship. A power analysis showed the sample could reliably detect reactions above 7.5%, meaning a modest windfall effect could not be statistically ruled out. Preliminary findings suggest CHIPS Act awards were largely anticipated by markets and priced in ahead of announcement, though ongoing date and entity verification, along with confound analysis, may still refine these results.

Published

2026-09-24

Issue

Section

Costello College of Business: Department of Finance