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1\begin{table}[H]\centering2\caption{Robustness of the AI Return Spread}\label{tab:robust}3\small4\begin{tabular}{lcc}\toprule5& Pre (2010--21) & Post (2022--24) \\\midrule6\multicolumn{3}{l}{\textit{Panel A. Alternative signal definition (composite)}}\\7Section-weighted (base) & +2.5 \tstat{1.58} & +14.6 \tstat{2.07} \\8Simple mean & +2.3 \tstat{1.50} & +13.4 \tstat{2.23} \\9Top-5\% tail & +1.5 \tstat{1.04} & +14.9 \tstat{2.47} \\10\midrule\multicolumn{3}{l}{\textit{Panel B. Alternative holding horizon}}\\116-month forward return & -0.1 \tstat{-0.10} & +1.1 \\1224-month forward return & +9.3 \tstat{2.86} & +23.0 \\13\midrule\multicolumn{3}{l}{\textit{Panel C. Construction / universe}}\\14Sector-neutral composite & +2.3 \tstat{3.35} & +3.8 \tstat{0.84} \\15First principal component & +2.6 \tstat{2.25} & +13.4 \tstat{1.97} \\16Excl.\ Magnificent-7 & +1.7 \tstat{1.07} & +14.1 \tstat{1.92} \\17Excl.\ top-10 by market cap & --- & +14.5 \tstat{1.93} \\18\bottomrule19\end{tabular}20\tablelegend{This table assesses the robustness of the AI composite Q5$-$Q1 forward-return spread (in percent per year, with Newey--West $t$-statistics) across alternative research-design choices, reported separately for the pre-period (2010--2021) and post-period (2022--2024). Panel~A varies the signal definition (section-weighted mean, simple mean, top-5\% tail); Panel~B varies the holding horizon (6- and 24-month forward returns); and Panel~C varies the construction and universe (sector-neutralization, replacing the three signals with their first principal component, and excluding the Magnificent~7 or the ten largest firms by market capitalization). The post-ChatGPT spread is stable across signal definitions, grows with the horizon, survives the exclusion of mega-cap winners, and shrinks markedly only under sector-neutralization---locating much of the premium in a between-sector channel.}21\end{table}22