\begin{table}[H]\centering
\caption{ChatGPT Event Study}\label{tab:event}
\small
\begin{tabular}{lrrrrrr}\toprule
Horizon & High & Mid & Low & High$-$Low & \textit{t} & \textit{p} \\\midrule
+60 trading days & +1.89 & +1.79 & -1.04 & +2.93 & 1.77 & 0.077 \\
+6 months & -2.72 & -5.17 & -7.25 & +4.53 & 1.60 & 0.110 \\
+1 year & -6.38 & -11.80 & -16.46 & +10.08 & 1.97 & 0.050 \\
+18 months & -8.46 & -16.03 & -23.05 & +14.59 & 1.89 & 0.060 \\
\midrule
\multicolumn{7}{l}{\footnotesize Cross-sectional regression, CAR(+1y) on pre-event AI exposure (per SD):}\\
\multicolumn{7}{l}{\footnotesize\quad No sector FE: $+3.13$\% \tstat{1.39}, $n=483$, $R^2=0.160$ \quad
With sector FE: $+2.09$\% \tstat{0.99}}\\
\bottomrule
\end{tabular}
\tablelegend{This table reports market-model cumulative abnormal returns (CAR, in percent) around the launch of ChatGPT on 30~November 2022. For each stock the market model is estimated over the January--October 2022 window, and abnormal returns are cumulated over horizons of 60 trading days, six months, one year, and 18 months. Firms are grouped into terciles of \emph{pre-event} AI disclosure measured from the filings of calendar year 2021---all dated before the event---and the table reports the mean CAR for the High, Mid, and Low terciles, the High$-$Low difference, and a Welch $t$-test (with $p$-value) of that difference that treats firms as independent; Table~\ref{tab:eventcda} reports tests that allow for the cross-sectional correlation induced by the common event date. The bottom rows report a cross-sectional regression of one-year CAR on pre-event AI exposure (per standard deviation), with and without sector fixed effects. Because abnormal returns are purged of each firm's market beta, the spread is not a difference in beta exposure; it may still reflect exposure to sector or style factors omitted from the market model.}
\end{table}