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1\begin{table}[H]\centering2\caption{Panel and Fama--MacBeth Return Regressions}\label{tab:panel}3\small4\begin{tabular}{lcc}\toprule5& (1) Two-way FE & (2) Fama--MacBeth \\\midrule6AI adoption ($z$) & -0.0047 \tstat{-0.24} & -0.0000 \tstat{-0.00} \\7AI infrastructure ($z$) & +0.0134 \tstat{0.61} & +0.0158 \tstat{1.54} \\8AI disruption risk ($z$) & -0.0153 \tstat{-1.00} & +0.0001 \tstat{0.01} \\9Size ($z$) & -0.2242 \tstat{-7.17} & -0.0330 \tstat{-8.21} \\10Momentum ($z$) & -0.0024 \tstat{-0.25} & +0.0081 \tstat{1.08} \\11Volatility ($z$) & +0.0197 \tstat{2.12} & +0.0687 \tstat{3.56} \\12\midrule13Firm FE & Yes & No \\14Year FE & Yes & (by construction) \\15N (obs / years) & 6{,}811 & 15 \\16\bottomrule17\end{tabular}18\tablelegend{This table reports forward-return regressions estimated two ways. Column~(1) is a two-way (firm and year) fixed-effects panel, equation~\eqref{eq: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 of equation~\eqref{eq:fm}---the average of fifteen 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.}19\end{table}20