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% METADATA — Edit these fields for each working paper
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\newcommand{\WPnumber}{2026-03}
\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}{%
  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 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 7{,}869 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
  \emph{conditional and economically large}: over the full sample the relation is positive but
  modest and only marginally significant, yet after the launch of ChatGPT (November~2022) it 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 afterward, and the long--short Sharpe
  ratio rises from 0.15 to 1.33. The break survives section-weighting choices, alternative signal
  definitions and horizons, dimension reduction, and the exclusion of the ``Magnificent~7''
  mega-caps. Three identification tests sharpen the interpretation: (i)~a placebo across five
  non-AI semantic factors shows the post-2022 jump is specific to AI (ESG and climate disclosure
  earn \emph{negative} post-2022 spreads); (ii)~a market-model event study finds that firms with
  higher pre-event AI disclosure earned $+10.1\%$ higher cumulative abnormal returns over the year
  after ChatGPT ($t=1.97$); and (iii)~the effect is absorbed by firm fixed effects and roughly
  halved by sector fixed effects. We interpret the evidence as an attention- and narrative-driven
  \emph{re-rating}: the market repriced which firms and sectors are AI-exposed once AI became
  salient, rather than rewarding year-to-year changes in wording.%
}
\newcommand{\WPkeywords}{textual analysis, sentence embeddings, artificial intelligence, disclosure, cross-section of returns, investor attention, event study}
\newcommand{\WPjel}{G12, G14, G17, M41, O33}

% --- Authors ---
\newcommand{\WPauthor}{%
  \begin{tabular}{c}
    {\large Manel Kammoun}\\[-0.05cm]
    {\small\href{mailto:manel.kammoun@uqo.ca}{manel.kammoun@uqo.ca}}\\[0.35cm]
    {\large Djerrycharli Tandjambianda}\\[-0.05cm]
    {\small\href{mailto:djerrycharli.tandjambianda@uqo.ca}{djerrycharli.tandjambianda@uqo.ca}}\\[0.35cm]
    {\large Simon-Pierre Boucher}\\[-0.05cm]
    {\small\href{mailto:simon-pierre.boucher@uqo.ca}{simon-pierre.boucher@uqo.ca}}
  \end{tabular}%
}
\newcommand{\WPaffiliation}{%
  Département des sciences administratives\\
  Université du Québec en Outaouais%
}
\newcommand{\WPemail}{}
\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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