\begin{table}[H]\centering
\caption{Difference-in-Differences around ChatGPT}\label{tab:did}
\small
\begin{tabular}{lcc}\toprule
& Treated$\times$Post & Exposure$\times$Post \\
& (binary) & (continuous, per SD) \\\midrule
Coefficient & 0.0170 & 0.0269 \\
\textit{t}-statistic & (0.78) & (1.51) \\
N (firm-years) & 6790 & 6790 \\
Firm \& Year FE & Yes & Yes \\\bottomrule
\end{tabular}
\tablelegend{This table reports difference-in-differences estimates of the effect of pre-determined AI exposure on returns around ChatGPT. The dependent variable is the winsorized forward 12-month return, and all specifications include firm and year fixed effects with standard errors double-clustered by firm and year. Treatment is fixed at each firm's pre-event (2019--2021) average AI exposure, so it cannot respond to the event: the first column uses a binary treated indicator (above-median pre-event exposure) interacted with the post-2022 indicator, and the second uses continuous pre-event exposure (per standard deviation) interacted with Post. Both coefficients are positive but weaker than the cross-sectional estimates, the pattern expected once firm fixed effects absorb the persistent component of AI exposure and only within-firm variation remains to identify the effect.}
\end{table}