\begin{table}[H]\centering
\caption{Panel and Fama--MacBeth Return Regressions}\label{tab:panel}
\small
\begin{tabular}{lcc}\toprule
& (1) Two-way FE & (2) Fama--MacBeth \\\midrule
AI adoption ($z$) & -0.0047 \tstat{-0.24} & -0.0000 \tstat{-0.00} \\
AI infrastructure ($z$) & +0.0134 \tstat{0.61} & +0.0158 \tstat{1.54} \\
AI disruption risk ($z$) & -0.0153 \tstat{-1.00} & +0.0001 \tstat{0.01} \\
Size ($z$) & -0.2242 \tstat{-7.17} & -0.0330 \tstat{-8.21} \\
Momentum ($z$) & -0.0024 \tstat{-0.25} & +0.0081 \tstat{1.08} \\
Volatility ($z$) & +0.0197 \tstat{2.12} & +0.0687 \tstat{3.56} \\
\midrule
Firm FE & Yes & No \\
Year FE & Yes & (by construction) \\
N (obs / years) & 6811 & 15 \\
\bottomrule
\end{tabular}
\tablelegend{This table reports forward-return regressions estimated two ways. Column~(1) is a two-way (firm~$\times$~year) fixed-effects panel with standard errors double-clustered by firm and year; the firm fixed effects absorb all permanent cross-firm differences, so the AI coefficients identify whether \emph{within-firm} changes in AI language predict returns. Column~(2) is the Fama--MacBeth estimator---the average of annual cross-sectional slopes---with Newey--West $t$-statistics on the time series of coefficients. The dependent variable is the winsorized forward 12-month return, and all signals and controls are within-year cross-sectional $z$-scores, so coefficients are expressed per cross-sectional standard deviation. The contrast between the two columns is the object of interest: the cross-sectional (Fama--MacBeth) AI premia are positive while the within-firm (panel) coefficients collapse to zero, locating the signal across rather than within firms.}
\end{table}