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  pdftitle  = {Does the Language of Artificial Intelligence Move Markets? A Semantic Analysis of S&P 500 SEC Filings and the Cross-Section of Stock Returns, 2010-2026},
  pdfauthor = {Manel Kammoun, Djerrycharli Tandjambianda, Simon-Pierre Boucher}
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\newcommand{\WPnumber}{17}
\newcommand{\WPtitle}{Does the Language of Artificial Intelligence Move Markets?}
\newcommand{\WPsubtitle}{A Semantic Analysis of S\&P~500 SEC Filings and the Cross-Section of Stock Returns, 2010--2026}
\newcommand{\WPdate}{June 2026}
\newcommand{\WPversion}{1.0}
\newcommand{\WPabstract}{%
\begingroup\singlespacing % long abstract: single spacing so that Keywords and JEL stay on page 2
We construct a \emph{semantic} measure of artificial-intelligence (AI) engagement for every
S\&P~500 firm by embedding the text of its annual SEC filings (Form~10-K) with a sentence
transformer and scoring each passage by cosine similarity to professionally curated AI
``anchor'' concepts---internal AI adoption, AI infrastructure exposure, and AI product-disruption
risk. Linking these scores to monthly equity prices from Yahoo Finance and to the Fama--French
factor data, we assemble a panel of \num{7869} firm-years (500 firms, fiscal years 2010--2026)
and ask whether the intensity of AI language predicts the cross-section of subsequent returns.
Our central result is \textbf{conditional and economically large}. Over the full sample the
relation between AI-disclosure intensity and future returns is positive but modest and only
marginally significant; a risk-adjusted long--short portfolio earns an insignificant
$\sim$2\%/year. After the public launch of ChatGPT (30 November 2022), however, the relation
\emph{more than sextuples}: the spread between the top and bottom quintile of AI disclosure rises
from $+2.4$ percentage points per year before 2022 to $+14.6$ points per year afterward. The
break survives section-weighting choices, alternative signal definitions and horizons, dimension
reduction, and---crucially---the exclusion of the ``Magnificent~7'' mega-caps. A battery of
identification tests sharpens the interpretation: (i) a \emph{placebo} across five non-AI
semantic factors shows the post-2022 jump is specific to AI-related language (ESG and climate
disclosure, by contrast, earn \emph{negative} post-2022 spreads); (ii) a market-model
\emph{event study} finds that firms with higher pre-event AI disclosure earned $+10.1\%$ higher
cumulative abnormal returns over the year after ChatGPT \tstat{1.97}; and (iii) the effect is
absorbed by firm fixed effects and roughly halved by sector fixed effects, indicating a
\emph{cross-sectional, regime-dependent re-rating}---the market repriced \emph{which firms and
sectors} are AI-exposed once AI became salient, rather than rewarding year-to-year changes in any
single firm's wording. The findings provide direct, text-based evidence for an attention- and
narrative-driven repricing of AI exposure, and illustrate the value of embedding-based disclosure
measurement for asset pricing.

\par\endgroup
}
\newcommand{\WPkeywords}{textual analysis, sentence embeddings, artificial intelligence, disclosure, cross-section of returns, investor attention, event study, generative AI}
\newcommand{\WPjel}{G12, G14, G17, M41, O33}

% --- Authors ---
\newcommand{\WPauthor}{Manel Kammoun \quad Djerrycharli Tandjambianda \quad Simon-Pierre Boucher}
\newcommand{\WPaffiliation}{%
  Département des sciences administratives\\
  Université du Québec en Outaouais%
}
\newcommand{\WPemail}{%
  \href{mailto:manel.kammoun@uqo.ca}{manel.kammoun@uqo.ca} \,\textperiodcentered\,
  \href{mailto:djerrycharli.tandjambianda@uqo.ca}{djerrycharli.tandjambianda@uqo.ca} \,\textperiodcentered\,
  \href{mailto:simon-pierre.boucher@uqo.ca}{simon-pierre.boucher@uqo.ca}%
}
\newcommand{\WPaddress}{%
  Gatineau -- Pavillon Alexandre-Taché\\
  283, boulevard Alexandre-Taché\\
  Gatineau, Québec, Canada J9A 1L8%
}

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\begin{document}

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\input{sections/literature}
\input{sections/data}
\input{sections/methodology}
\input{sections/results}
\input{sections/robustness}
\input{sections/discussion}
\input{sections/conclusion}

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