Detecting, Visualizing, and Trading on Dynamic Thematic Structure in Equity Markets

Abstract

We develop a systematic framework for detecting and visualizing dynamic equity themes, time-varying clusters of stocks experiencing excess co-movement beyond standard risk factors, and study their implications for options markets and trading. Using a fixed universe of liquid US stocks, daily returns are first residualized with respect to the Fama-French five factors and momentum to produce return series that isolate dependence beyond broad market exposures, additionally residualized with respect to sector returns for a robustness check. Rolling correlation networks built from these residuals are then analyzed for thematic structure as well as emergence, shocks, and dissipation, using HDBSCAN for clustering and UMAP with Procrustes alignment to produce coherent visualizations of evolving geometry. Resulting clusters are linked into multi-day lifecycle objects, then we conduct episode-level analyses and state-based modeling and trading tests using options-derived variables. This framework defines themes from residualized dependence rather than from narratives or static industry classifications and treats options markets as a separate layer for mechanism analysis and trading applications rather than as a detection input. Empirically, we find that detected themes exhibit repeating options signatures over their lifecycles, and we identify a theme-conditioned options trading strategy that generates positive payoff, suggesting a potential application of the framework in systematic trading and risk management.