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\section{Related Literature and Hypotheses}
\label{sec:literature}
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Our study sits at the intersection of five strands of research: textual analysis in finance, the
pricing of corporate disclosure, technology and innovation in asset pricing, investor attention and
narrative economics, and the nascent literature on the economics of artificial intelligence. Each
contributes a building block---a measurement tool, an object of study, a candidate risk factor, a
behavioral mechanism, and a comparison case---and our contribution is to combine them: we measure a
technology theme from firms' own words, price it in the cross-section, and show that its pricing turns
on precisely when a behavioral mechanism predicts it should. We take the strands in turn and close
with the hypotheses they jointly motivate.

\subsection{Textual analysis and natural language processing in finance}
A now-large literature extracts economic signals from financial text. The foundational work of
\citet{tetlock2007} showed that the tone of a \textit{Wall Street Journal} column forecasts
aggregate returns, and \citet{tetlock2008} extended this to firm-level news. \citet{lm2011}
demonstrated that finance-specific sentiment dictionaries materially outperform general-purpose word
lists in 10-K analysis, launching a wave of dictionary-based studies and the widely used
Loughran--McDonald word lists; \citet{lm2016} survey this field. A second generation moved from
counting words to modeling them: \citet{gentzkow2019} review text-as-data methods in economics,
while \citet{kelly2019textregression} and \citet{bybee2024narrative} use machine-learning
representations of news to construct interpretable factors and a ``state of the economy'' from text.
Our paper belongs to the most recent, embedding-based generation of this work. The limitations of
keyword counting are acute for a theme like AI: a naive count cannot tell ``we do not currently use
artificial intelligence'' apart from ``we are an artificial-intelligence leader,'' it misses the
paraphrase of a firm that describes machine learning without ever writing ``AI,'' and it treats
boilerplate and substance identically. We instead measure the \emph{semantic similarity} of
disclosure to anchor concepts using a sentence transformer \citep{reimers2019sbert}, which maps
passages into a vector space where meaning, not surface form, governs proximity. This follows a
growing practice of using contextual embeddings to quantify disclosure content
\citep{cong2022textfactor}, and is closest in spirit to similarity-to-concept designs that score
documents against curated reference passages.

\subsection{The pricing of corporate disclosure}
A parallel literature asks whether the \emph{content} and \emph{changes} of mandatory disclosure
predict returns. \citet{cohen2020lazy} show that firms that change the language of their 10-K
relative to the prior year (``lazy prices'' when they do not) experience predictable future returns
and operating outcomes. \citet{hoberg2010product} and \citet{hobergphillips2016} use 10-K product
descriptions to build text-based industry classifications and measures of product-market fluidity
that carry asset-pricing content. \citet{lopezlira2023risk} extracts risk exposures from 10-K
risk-factor sections. The general lesson is that the qualitative portions of filings contain
value-relevant information that markets process incompletely and with delay. We contribute a
focused, concept-level measure---AI engagement---and show that its pricing is strongly
state-dependent, a dimension the disclosure-pricing literature has largely left implicit.

\subsection{Technology, innovation, and the cross-section of returns}
Asset pricing has long sought to connect technological change to risk and return.
\citet{hirshleifer2013innovative} show that innovative efficiency predicts returns;
\citet{kogan2017technological} build market-based measures of innovation from patents and link them
to growth and reallocation; \citet{koganpapanikolaou2014} show that investment-specific technology
shocks shape the cross-section. A more recent literature studies AI exposure directly:
\citet{babina2024aiinvestment} measure firm-level AI investment from job-posting and resume data and
document faster growth among AI-adopting firms; \citet{eisfeldt2023generative} construct a
firm-level measure of \emph{generative}-AI exposure from the task content of occupations and show
that a generative-AI factor was priced precisely in the weeks around ChatGPT's release.
\citet{acemoglu2020wrong} and \citet{webb2020} study the labor-market exposure of firms to AI and
automation. Our contribution relative to this work is to measure AI exposure from the firm's
\emph{own disclosure}---a self-reported, narrative signal available for the entire index at annual
frequency---and to characterize the full time profile of its pricing across a decade and a half, not
only the event window. Where \citet{eisfeldt2023generative} use occupation-task exposure, we use the
language firms choose, providing a complementary and highly granular measure.

\subsection{Investor attention, salience, and narrative economics}
Why might value-relevant disclosure be priced only after a catalyst? The behavioral literature
offers a clear mechanism: limited investor attention. \citet{barberodean2008} show that retail
investors are drawn to attention-grabbing stocks; \citet{da2011} construct a direct attention proxy
from Google search and link it to prices; \citet{dellavigna2009} document under-reaction to
information released when attention is low; and \citet{hirshleifer2009driven} show that earnings news
is underweighted when investors are distracted. \citet{shiller2017narrative} argues that contagious
narratives drive major asset-price movements, and \citet{bakerwurgler2006} show that sentiment
differentially affects hard-to-value, speculative stocks. Our results provide a sharp illustration:
the information about firms' AI engagement existed in filings for years but was repriced abruptly
when a salient consumer event---ChatGPT---focused investor attention on AI. The asymmetry we document
(AI rewarded, ESG penalized after 2022) is itself consistent with a shift in the prevailing market
narrative.

\subsection{ESG and climate disclosure as a comparison case}
Finally, our placebo design connects to the literature on the pricing of ESG and climate disclosure.
\citet{pastor2021sustainable} model how sustainability preferences depress the expected returns of
green assets in equilibrium, and \citet{pastor2022dissecting} show that green stocks' strong
\emph{realized} returns through 2021 reflected unexpected increases in climate concern rather than
high expected returns. \citet{bolton2021risk} document a carbon premium. Consistent with the
post-2021 reversal in this literature, we find that high-ESG and high-climate disclosure earned
\emph{negative} spreads after 2022, exactly when AI disclosure began to earn large positive
ones---underscoring that the post-2022 re-rating reflects a thematic rotation toward AI rather than a
generic disclosure effect.

\subsection{Hypotheses}
\label{subsec:hypotheses}
Our analysis is organized around four hypotheses.
\begin{description}[leftmargin=2.4em,style=nextline]
\item[H1 (Unconditional pricing).] If AI exposure carries a stable risk premium or persistent
mispricing, then sorting on AI-disclosure intensity yields a positive and significant risk-adjusted
return spread over the full 2010--2026 sample.
\item[H2 (Regime dependence).] If instead AI exposure is repriced only once AI becomes salient, the
return spread is small before and large after the late-2022 generative-AI catalyst, with a
detectable structural break.
\item[H3 (AI-specificity).] If the post-2022 spread reflects a genuine AI repricing rather than a
mechanical or market-wide artifact, it appears for AI-related semantic factors but not for unrelated
factors (e.g.\ ESG, climate, macro risk).
\item[H4 (Cross-sectional, not within-firm).] If the market repriced \emph{which} firms are
AI-exposed rather than rewarding incremental wording, the effect is concentrated in the
cross-section and attenuates under firm and sector fixed effects.
\end{description}
\noindent The data, as we show, decisively favor H2--H4 over H1.