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\section{Results}
\label{sec:results}
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\subsection{Portfolio sorts: a weak unconditional relation}
Table~\ref{tab:sorts} reports the quintile sorts over the full sample. The relation between AI
disclosure and future returns is positive and broadly monotone but economically modest. The
equal-weighted Q5$-$Q1 spread is $+4.6$ percentage points per year for AI adoption \tstat{1.83},
$+3.9$ for AI infrastructure \tstat{1.58}, $+5.3$ for AI disruption risk \tstat{2.27}, and $+4.9$ for
the composite \tstat{1.88}. Value-weighting attenuates the adoption and composite spreads, indicating
the effect is stronger among smaller index members. The left panel of Figure~\ref{fig:quint} displays
the full-sample pattern: returns rise gently from the lowest to the highest AI quintile. On its own,
this is suggestive but far from decisive evidence---and, as we now show, it averages over two very
different regimes.

\input{tables/tab_sorts}

\subsection{Does AI disclosure earn risk-adjusted alpha unconditionally? (H1)}
Table~\ref{tab:alpha} reports CAPM and FF6 alphas of the long--short portfolios. The AI-opportunity
composite earns a raw spread of about $+2.3\%$ per year, of which the FF6 alpha is $+1.9\%$---but
statistically insignificant \tstat{1.24}. The AI-adoption and AI-infrastructure long--shorts are
similarly insignificant after risk adjustment. The one exception is the AI-disruption-risk spread,
whose FF6 alpha of $+2.8\%$ per year is marginally significant \tstat{2.10}, hinting at a small risk
premium for firms that flag AI as a competitive threat. The factor loadings (not the focus here) show
the AI long--short is roughly market-neutral with modest negative profitability (RMW) and investment
(CMA) tilts, consistent with a growth-oriented long leg. The verdict on H1 is clear:
\textbf{unconditionally, AI disclosure is at best weakly priced.} This is not the end of the story but
the beginning of it.

\input{tables/tab_alpha}

\subsection{Panel and Fama--MacBeth estimates}
Table~\ref{tab:panel} juxtaposes the two-way fixed-effects panel with the Fama--MacBeth estimator. The
contrast is instructive. In the Fama--MacBeth (cross-sectional) regressions, the AI-adoption and
AI-infrastructure premia are positive---$+1.3$ and $+1.6$ percentage points per standard
deviation---with \textit{t}-statistics near 1.6--1.7, indicating a weak but positive cross-sectional
premium on average. In the two-way fixed-effects panel, however, every AI coefficient collapses to
essentially zero. Because firm fixed effects remove all permanent cross-firm differences, this tells
us the signal lives \emph{across} firms, not \emph{within} them: a given firm writing relatively more
about AI from one year to the next does not predict its own subsequent return (anticipating H4).
Throughout, size carries the familiar negative premium ($t\approx-8$) and volatility a positive one,
consistent with the broader cross-sectional literature in this large-cap universe.

\input{tables/tab_panel}

\subsection{The structural break: AI pricing switched on after ChatGPT (H2)}
The full-sample averages mask a sharp regime change that is the paper's central result. Splitting the
sample at the November 2022 ChatGPT launch, the center and right panels of Figure~\ref{fig:quint}
reveal a striking transformation: the quintile pattern is nearly flat before 2022 and steeply
increasing afterward. The high-minus-low composite spread is $+2.4$ percentage points per year
pre-ChatGPT but $+14.6$ points per year post-ChatGPT---a more than sixfold increase.

\begin{figure}[htbp]\centering
\includegraphics[width=\textwidth]{fig1_quintile_returns.png}
\caption{Mean forward twelve-month return by AI-disclosure composite quintile, full sample and by
sub-period. The high-minus-low spread is $+2.4$ percentage points before ChatGPT and $+14.6$ points
after.}\label{fig:quint}
\end{figure}

Table~\ref{tab:break} quantifies the break two ways. In sub-period sorts, the AI-adoption Q5$-$Q1
spread rises from $+2.2\%$ per year \tstat{2.04} pre-2022 to $+14.3\%$ \tstat{2.15} post-2022; the
composite rises from $+2.5\%$ to $+14.6\%$; AI infrastructure from $+1.4\%$ to $+13.6\%$. In the
pooled interaction regressions, the post-2022 incremental slope is positive for all three signals and
statistically significant for AI infrastructure \tstat{2.63}---the facet most directly tied to the
chip, cloud, data-center, and power firms that led the 2023--2025 rally. Figure~\ref{fig:spread} shows
the annual spreads: small and mixed-sign through 2021, uniformly large and positive for every
post-2022 cohort.

\input{tables/tab_break}

\begin{figure}[htbp]\centering
\includegraphics[width=0.9\textwidth]{fig3_annual_spread.png}
\caption{Annual high-minus-low (Q5$-$Q1) forward-return spread on the AI-disclosure composite, by
10-K fiscal year. Post-ChatGPT cohorts (red) are uniformly and substantially positive.}
\label{fig:spread}
\end{figure}

The same regime change is visible in two further lenses. Figure~\ref{fig:cum} plots the cumulative
value of one dollar invested in the equal-weighted, annually rebalanced, self-financing
high-minus-low portfolio: it drifts sideways-to-up for a decade and then inflects upward after the
dashed ChatGPT line. Figure~\ref{fig:fmyear} plots the year-by-year Fama--MacBeth slope on the AI
composite (with controls): it hovers near zero for the entire 2010s and then turns sharply positive,
peaking in the 2024 cohort. The latter figure also delivers an important nuance: the size-controlled
premium is largest in the most recent cohort, consistent with the AI \emph{infrastructure}
super-cycle of 2024--2025 (the build-out of data centers and accelerated compute). We return to this
concentration when discussing limitations.

\begin{figure}[htbp]\centering
\includegraphics[width=0.86\textwidth]{fig2_cumulative_ls.png}
\caption{Cumulative value of \$1 invested in the equal-weighted, annually rebalanced, self-financing
high-minus-low AI-disclosure portfolio (AI opportunity composite and AI disruption-risk).}
\label{fig:cum}
\end{figure}

\begin{figure}[htbp]\centering
\includegraphics[width=0.9\textwidth]{fig6_fm_byyear.png}
\caption{Year-by-year Fama--MacBeth slope on the AI-disclosure composite (forward return per
cross-sectional standard deviation), controlling for size, momentum, and volatility. Bars are $\pm1$
standard error.}\label{fig:fmyear}
\end{figure}

Finally, Figure~\ref{fig:bin} confirms the pattern is not an artifact of a few influential
observations: the binned relation between the AI composite and forward returns is essentially flat in
the pre-ChatGPT sub-period and distinctly upward-sloping afterward, across the entire distribution of
AI disclosure rather than only at the extreme quintiles.

\begin{figure}[htbp]\centering
\includegraphics[width=\textwidth]{fig5_binscatter.png}
\caption{Binned scatter of forward twelve-month return against the AI-disclosure composite, by
sub-period. Each point is a portfolio of firms in a bin of the AI signal; the line is an OLS fit. The
conditional slope steepens markedly after ChatGPT.}\label{fig:bin}
\end{figure}

\subsection{The anatomy of the premium: size and sector}
Where in the cross-section does the post-2022 premium concentrate? Table~\ref{tab:doublesort} and
Figure~\ref{fig:double} report an AI~$\times$~size double sort. Before 2022 the AI gradient is flat
within both size halves. After 2022 it is steep, and dramatically so among smaller firms: small,
high-AI firms earned a mean forward return of 34.6\%, versus 19.5\% for small, low-AI firms---a
15-point gap---while the corresponding gap among large firms is 7 points. The premium is thus
\emph{not} merely a mega-cap phenomenon (a point we reinforce in Section~\ref{sec:robustness}); if
anything it is strongest among the smaller, less-followed names, consistent with an attention-based
mechanism in which information diffuses more slowly into smaller stocks.

\input{tables/tab_doublesort}

\begin{figure}[htbp]\centering
\includegraphics[width=\textwidth]{fig9_double_sort.png}
\caption{AI-disclosure $\times$ firm-size double sort: mean forward twelve-month return by AI tercile
within small and large firms, pre- and post-ChatGPT.}\label{fig:double}
\end{figure}

\subsection{Economic significance}
The statistical break is matched by an economic one. The Sharpe ratio of the equal-weighted
AI-composite long--short portfolio---a direct gauge of risk-adjusted attractiveness---rises from 0.15
in the pre-ChatGPT decade to 1.33 afterward; for the AI-disruption-risk spread it rises from 0.33 to
1.15. A Sharpe ratio above one is exceptional for a single-characteristic, self-financing equity
strategy, and its emergence precisely at the ChatGPT boundary is hard to reconcile with a constant
risk premium. The point estimates in the quintile sorts are equally vivid: a 14-point annual spread
between the top and bottom AI quintiles is roughly three times the unconditional equity risk premium
and an order of magnitude larger than the pre-2022 spread. Even acknowledging that a portion of this
reflects the higher realized volatility of AI-exposed firms (documented in Section~\ref{sec:robustness})
and the broad technology rally, the magnitude of the post-2022 re-rating is among the largest
single-theme cross-sectional effects in the recent asset-pricing record.