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1% ============================================================================2% UQO Working Paper Template — Economics / Finance3% ============================================================================4\documentclass[12pt,letterpaper]{article}56% --- Encoding & Language ---7\usepackage[utf8]{inputenc}8\usepackage[T1]{fontenc}9\usepackage[english]{babel}1011% --- Page Layout ---12\usepackage[13 letterpaper,14 top=1in,15 bottom=1in,16 left=1in,17 right=1in18]{geometry}1920% --- Typography ---21\usepackage{mathptmx} % Times-like font (text + math)22\usepackage{microtype} % Improved typographic refinements23\usepackage{setspace}24\onehalfspacing % 1.5 line spacing2526% --- Math ---27\usepackage{amsmath,amssymb,amsthm}2829% --- Tables ---30\usepackage{booktabs}31\usepackage{tabularx}32\usepackage{multirow}33\usepackage{threeparttable}34\usepackage{siunitx}35\sisetup{36 input-symbols = {()},37 table-format = -1.3,38 parse-numbers = false39}4041% --- Figures ---42\usepackage{graphicx}43\usepackage{subcaption}44\usepackage{float}4546% --- Colors & Links ---47\usepackage[dvipsnames]{xcolor}48\usepackage[49 colorlinks = true,50 linkcolor = NavyBlue,51 citecolor = NavyBlue,52 urlcolor = NavyBlue,53 breaklinks = true54]{hyperref}55\hypersetup{56 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},57 pdfauthor = {Manel Kammoun, Djerrycharli Tandjambianda, Simon-Pierre Boucher}58}5960% --- Bibliography ---61\usepackage[authoryear,round,semicolon]{natbib}62\bibliographystyle{aer}6364% --- Captions ---65\usepackage[66 font = small,67 labelfont = bf,68 labelsep = period,69 justification = justified,70 singlelinecheck = false71]{caption}7273% --- Headers & Footers ---74\usepackage{fancyhdr}75\pagestyle{fancy}76\fancyhf{}77\renewcommand{\headrulewidth}{0pt}78\fancyfoot[C]{\thepage}7980% --- Section Formatting ---81\usepackage{titlesec}82\titleformat{\section}{\large\bfseries}{\thesection.}{0.5em}{}83\titleformat{\subsection}{\normalsize\bfseries}{\thesubsection.}{0.5em}{}84\titleformat{\subsubsection}{\normalsize\itshape}{\thesubsubsection.}{0.5em}{}8586% --- Appendix Support ---87\usepackage[toc,page]{appendix}8889% --- Paper-specific ---90\sisetup{group-minimum-digits=4, group-separator={,}}91\newcommand{\tstat}[1]{{\footnotesize(\textit{t}=#1)}}92\newcommand{\tablelegend}[1]{\par\vspace{0.25em}\begin{minipage}{\linewidth}\footnotesize\textit{Notes:} #1\end{minipage}}93\graphicspath{{figures/}}9495% --- Misc ---96\usepackage{enumitem}97\setlist{nosep,leftmargin=*}9899% ============================================================================100% METADATA101% ============================================================================102\newcommand{\WPnumber}{17}103\newcommand{\WPtitle}{Does the Language of Artificial Intelligence Move Markets?}104\newcommand{\WPsubtitle}{A Semantic Analysis of S\&P~500 SEC Filings and the Cross-Section of Stock Returns, 2010--2026}105\newcommand{\WPdate}{June 2026}106\newcommand{\WPversion}{1.0}107\newcommand{\WPabstract}{%108\begingroup\singlespacing % long abstract: single spacing so that Keywords and JEL stay on page 2109We construct a \emph{semantic} measure of artificial-intelligence (AI) engagement for every110S\&P~500 firm by embedding the text of its annual SEC filings (Form~10-K) with a sentence111transformer and scoring each passage by cosine similarity to professionally curated AI112``anchor'' concepts---internal AI adoption, AI infrastructure exposure, and AI product-disruption113risk. Linking these scores to monthly equity prices from Yahoo Finance and to the Fama--French114factor data, we assemble a panel of \num{7869} firm-years (500 firms, fiscal years 2010--2026)115and ask whether the intensity of AI language predicts the cross-section of subsequent returns.116Our central result is \textbf{conditional and economically large}. Over the full sample the117relation between AI-disclosure intensity and future returns is positive but modest and only118marginally significant; a risk-adjusted long--short portfolio earns an insignificant119$\sim$2\%/year. After the public launch of ChatGPT (30 November 2022), however, the relation120\emph{more than sextuples}: the spread between the top and bottom quintile of AI disclosure rises121from $+2.4$ percentage points per year before 2022 to $+14.6$ points per year afterward. The122break survives section-weighting choices, alternative signal definitions and horizons, dimension123reduction, and---crucially---the exclusion of the ``Magnificent~7'' mega-caps. A battery of124identification tests sharpens the interpretation: (i) a \emph{placebo} across five non-AI125semantic factors shows the post-2022 jump is specific to AI-related language (ESG and climate126disclosure, by contrast, earn \emph{negative} post-2022 spreads); (ii) a market-model127\emph{event study} finds that firms with higher pre-event AI disclosure earned $+10.1\%$ higher128cumulative abnormal returns over the year after ChatGPT \tstat{1.97}; and (iii) the effect is129absorbed by firm fixed effects and roughly halved by sector fixed effects, indicating a130\emph{cross-sectional, regime-dependent re-rating}---the market repriced \emph{which firms and131sectors} are AI-exposed once AI became salient, rather than rewarding year-to-year changes in any132single firm's wording. The findings provide direct, text-based evidence for an attention- and133narrative-driven repricing of AI exposure, and illustrate the value of embedding-based disclosure134measurement for asset pricing.135136\par\endgroup137}138\newcommand{\WPkeywords}{textual analysis, sentence embeddings, artificial intelligence, disclosure, cross-section of returns, investor attention, event study, generative AI}139\newcommand{\WPjel}{G12, G14, G17, M41, O33}140141% --- Authors ---142\newcommand{\WPauthor}{Manel Kammoun \quad Djerrycharli Tandjambianda \quad Simon-Pierre Boucher}143\newcommand{\WPaffiliation}{%144 Département des sciences administratives\\145 Université du Québec en Outaouais%146}147\newcommand{\WPemail}{%148 \href{mailto:manel.kammoun@uqo.ca}{manel.kammoun@uqo.ca} \,\textperiodcentered\,149 \href{mailto:djerrycharli.tandjambianda@uqo.ca}{djerrycharli.tandjambianda@uqo.ca} \,\textperiodcentered\,150 \href{mailto:simon-pierre.boucher@uqo.ca}{simon-pierre.boucher@uqo.ca}%151}152\newcommand{\WPaddress}{%153 Gatineau -- Pavillon Alexandre-Taché\\154 283, boulevard Alexandre-Taché\\155 Gatineau, Québec, Canada J9A 1L8%156}157158% ============================================================================159% DOCUMENT160% ============================================================================161\begin{document}162163% --- Title Page ---164\input{sections/titlepage}165166% --- Main Body ---167\input{sections/introduction}168\input{sections/literature}169\input{sections/data}170\input{sections/methodology}171\input{sections/results}172\input{sections/robustness}173\input{sections/discussion}174\input{sections/conclusion}175176% --- References ---177\newpage178\bibliography{references}179180% --- Appendix ---181\input{appendix/appendix}182183\end{document}184