\begin{table}[H]\centering
\caption{Correlations among Signals, Controls, and Returns}\label{tab:corr}
\small
\begin{tabular}{lrrrrrr}\toprule
& (1) & (2) & (3) & (4) & (5) & (6) \\\midrule
(1) AI adoption & 1.00 & 0.80 & 0.86 & 0.07 & 0.01 & 0.04 \\
(2) AI infrastructure & 0.80 & 1.00 & 0.74 & 0.11 & 0.02 & 0.04 \\
(3) AI disruption risk & 0.86 & 0.74 & 1.00 & 0.07 & 0.02 & 0.04 \\
(4) Size & 0.07 & 0.11 & 0.07 & 1.00 & 0.08 & -0.10 \\
(5) Momentum & 0.01 & 0.02 & 0.02 & 0.08 & 1.00 & 0.00 \\
(6) Forward return & 0.04 & 0.04 & 0.04 & -0.10 & 0.00 & 1.00 \\
\bottomrule
\end{tabular}
\tablelegend{This table reports Pearson correlation coefficients among the three within-year standardized AI signals, the size and momentum controls, and the forward 12-month return, computed over the estimation panel. Columns (1)--(6) correspond to the row variables in the same order. The three AI signals are mutually highly correlated (pairwise $\rho$ between 0.74 and 0.86), consistent with their being three lenses on a single latent ``AI salience'' construct, whereas each AI signal's unconditional correlation with the forward return is small ($\approx 0.04$), foreshadowing that the action lies in the time variation of the relation rather than its unconditional level.}
\end{table}