Abstract

We document a robust cross-sectional return premium associated with corporate disclosure of artificial intelligence in 10-K filings. Using a quarterly panel of S&P 1500 constituents from 2021Q1 through 2025Q3, we construct a firm-level AI-exposure measure from textual analysis of the Management Discussion and Analysis section: counts of forty-seven AI-related keywords (general references such as “artificial intelligence” and “machine learning” plus specific technology references such as “large language model” and “transformer architecture”) aggregated and normalized by section length. Sorting firms into value-weighted quintile portfolios on this measure and rebalancing quarterly, the long–short portfolio long the top quintile and short the bottom earns 4.81 percent per year (t = 2.91 under Newey–West with three lags), corresponding to an annualized Sharpe ratio of 0.51. The premium survives the Fama–French five-factor model augmented with the Carhart momentum factor: the six-factor alpha is 3.12 percent per year (t = 2.43). The premium is concentrated in the eleven quarters following the November 2022 public release of large language models; in the pre-ChatGPT sub-sample (2021Q1–2022Q3) the long–short return is statistically indistinguishable from zero (R̂ = 0.94%, t = 0.61), and in the post-ChatGPT sub-sample (2022Q4–2025Q3) it averages 7.62 percent per year (t = 3.49). The premium loads positively on the high-minus-low book-to-market factor and negatively on the size factor in conventional decompositions; a residual unexplained component remains across all six factor specifications we test, including a seven-factor model that adds an intangible-capital factor following Eisfeldt, Kim, and Papanikolaou (2022). We propose three non-exclusive interpretations—a risk-based account, a mispricing-and-gradual-learning account, and a characteristics account in which AI exposure proxies for unmeasured intangible capital—and identify the diagnostic margins that empirically separate them. We are explicit that the design is descriptive of a cross-sectional pattern, not causally identifying a specific risk or sentiment channel. We close by drawing implications for portfolio construction, intangible-capital measurement in growth accounting, and the methodological discipline of textual-disclosure research design.