\section{Additional Tables and Figures}
\label{sec:app_additional}

This appendix collects supplementary tables and figures that support the main results but are not essential for following the core argument.

\subsection{Descriptive Figures}

\begin{figure}[H]
\centering
\includegraphics[width=0.85\textwidth]{figures/figD1_return_density.pdf}
\caption{Distribution of 1-minute log returns on FOMC days. Winsorized at 0.5\% tails for display. The heavy tails and leptokurtic shape are consistent with the Jarque--Bera test rejections reported in Table~\ref{tab:desc_1min_ret}.}
\label{fig:D1}
\end{figure}

\begin{figure}[H]
\centering
\includegraphics[width=0.85\textwidth]{figures/figD5_acf_returns.pdf}
\caption{Autocorrelation of 1-minute returns. Grey band shows 95\% confidence interval. Negative first-order autocorrelation reflects bid--ask bounce effects typical of high-frequency data.}
\label{fig:D5}
\end{figure}

\begin{figure}[H]
\centering
\includegraphics[width=0.85\textwidth]{figures/figD8b_log_rv_distribution.pdf}
\caption{Distribution of $\log(\mathrm{RV})$. Near-Gaussian shape validates the use of $\log(\mathrm{RV})$ as the dependent variable in the panel regressions.}
\label{fig:D8b}
\end{figure}

\subsection{Ensemble Model Diagnostics}

\begin{figure}[H]
\centering
\includegraphics[width=0.6\textwidth]{figures/pca_axis_quality.png}
\caption{PCA axis assignment quality. Bar height shows $|\text{Corr}|$ between PC projection and keyword differential. All axes exceed the 0.04 minimum separation threshold.}
\label{fig:pca_quality}
\end{figure}

\begin{figure}[H]
\centering
\begin{subfigure}[b]{0.48\textwidth}
\includegraphics[width=\textwidth]{figures/novelty_comparison.png}
\caption{Novelty: MiniLM vs BERT}
\end{subfigure}
\hfill
\begin{subfigure}[b]{0.48\textwidth}
\includegraphics[width=\textwidth]{figures/scatter_comparison.png}
\caption{Score scatter plots}
\end{subfigure}
\caption{Model comparison: MiniLM (384d) vs.\ BERT (768d). Novelty correlation $r = 0.72$; policy stance tone $r = 0.56$. The moderate inter-model correlation supports the use of an ensemble approach.}
\label{fig:model_comparison}
\end{figure}

\begin{figure}[H]
\centering
\begin{subfigure}[b]{0.48\textwidth}
\includegraphics[width=\textwidth]{figures/tone_comparison.png}
\caption{Tone comparison across models}
\end{subfigure}
\hfill
\begin{subfigure}[b]{0.48\textwidth}
\includegraphics[width=\textwidth]{figures/confidence_distribution.png}
\caption{Ensemble confidence distribution}
\end{subfigure}
\caption{Tone model comparison and confidence diagnostics. The right panel shows that ensemble confidence is concentrated above 0.7, indicating strong inter-model agreement for most statements.}
\label{fig:tone_conf}
\end{figure}

\subsection{Additional Panel Regression Tables}

Tables~\ref{tab:panel_rv_stance}--\ref{tab:panel_return_novelty} report panel minute-level regressions for the remaining dependent variables (realized volatility in levels, realized beta, and returns), separately for stance and novelty.

\input{tables/tableP_panel_rv_stance.tex}
\input{tables/tableP_panel_rv_novelty.tex}
\input{tables/tableP_panel_beta_stance.tex}
\input{tables/tableP_panel_beta_novelty.tex}
\input{tables/tableP_panel_return_stance.tex}
\input{tables/tableP_panel_return_novelty.tex}

\subsection{Additional Event-Level Regression Tables}

Tables~\ref{tab:rolling_rv_ratio_30min} and~\ref{tab:rolling_log_rv_ratio_30min} report the event-level RV-ratio specifications, which corroborate the pre/post difference results in the main text.

\input{tables/tableR_rv_ratio_30min.tex}
\input{tables/tableR_log_rv_ratio_30min.tex}

\subsection{Additional IRF and Drift Figures}

\begin{figure}[H]
\centering
\includegraphics[width=0.85\textwidth]{figures/figR9_drift_by_novelty.pdf}
\caption{Cumulative return drift by novelty tercile. Unlike stance-based drift (Figure~\ref{fig:R8}), novelty terciles show less directional separation, consistent with novelty affecting volatility rather than returns.}
\label{fig:R9}
\end{figure}

\begin{figure}[H]
\centering
\includegraphics[width=0.85\textwidth]{figures/figR10_irf_ret_h_novelty.pdf}
\caption{Impulse response: cumulative return to novelty. Effects are generally smaller and less significant than stance effects (Figure~\ref{fig:R10_stance}), supporting the hypothesis that novelty operates through volatility rather than returns.}
\label{fig:R10_novelty}
\end{figure}

\begin{figure}[H]
\centering
\includegraphics[width=0.85\textwidth]{figures/figR10b_irf_placebo_novelty.pdf}
\caption{Pre-announcement placebo: IRF for novelty. All coefficients are near zero, corroborating the placebo results for stance (Figure~\ref{fig:placebo_stance}).}
\label{fig:placebo_novelty}
\end{figure}

\subsection{Sub-Period Stability}

\begin{figure}[H]
\centering
\includegraphics[width=0.85\textwidth]{figures/figRob_subperiod_heatmap.pdf}
\caption{Sub-period stability: regression coefficients across six Fed policy regimes. Coefficient signs are generally consistent across sub-periods, though magnitudes vary with the degree of policy uncertainty in each regime.}
\label{fig:rob_subperiod}
\end{figure}
