spb/wp17_uqo

1\begin{table}[H]\centering2\caption{Difference-in-Differences around ChatGPT}\label{tab:did}3\small4\begin{tabular}{lcc}\toprule5& Treated$\times$Post & Exposure$\times$Post \\6& (binary) & (continuous, per SD) \\\midrule7Coefficient & 0.0170 & 0.0269 \\8\textit{t}-statistic & (0.78) & (1.51) \\9N (firm-years) & 6{,}790 & 6{,}790 \\10Firm \& Year FE & Yes & Yes \\\bottomrule11\end{tabular}12\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 average AI exposure in the filings of 2019--2021, 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 imprecise; the binary estimate is indistinguishable from zero and the continuous estimate is significant only at the 15\% level. The post-period contains three formation years and the year clusters are few, so the double-clustered standard errors should be read with caution.}13\end{table}14