Identifying Risk Regimes in Thai Equities Using Rolling-Correlation Features and Unsupervised Learning
Abstract
This study proposes a model for analyzing Thai equities based on their sensitivities to global and local macroeconomic indicators. By using rolling correlations between stock returns—sampled across sectors according to FTSE Russell's Industry Classification Benchmark (ICB)—and selected global and local market indicators, we develop time-varying exposure profiles for each stock. Applying$K$-means clustering to these profiles yields three behaviorally coherent groups. Principal Component Analysis (PCA) then reduces these exposure features into a two-dimensional regime space, with quadrants representing risk-on and risk-off conditions. Mapping the clustered stocks onto this regime space demonstrates how stock behaviors relate to prevailing macroeconomic conditions and aids transparent, data-driven investment decisions. Empirical results suggest that the proposed framework provides a clear and interpretable foundation for risk-aware investors, especially retirees seeking clarity in portfolio building.
Authors: Saowaluk Watanapa, Bunthit Watanapa
Published in: International Conference on Knowledge and Smart Technology (2026)