When the Rules Change: Adaptive Signal Extraction via Kalman Filtering and Markov-Switching Regimes

Abstract

Most empirical microstructure research assumes that order flow--return parameters are constant, yet these relationships shift substantially across market regimes. Combining adaptive Kalman filtering, Markov-switching regime identification, and asymmetric response estimation, we characterize regime-dependent investor behavior in the Korean stock market during 2020--2024 using daily transaction data disaggregated by investor type. Three principal findings emerge: foreign investor predictive power increases several-fold during crisis periods relative to bull markets; individual investors chase momentum asymmetrically, reacting far more strongly to positive than to negative shocks; and independent information-theoretic validation corroborates both patterns. Rigorous out-of-sample testing reveals that these in-sample regularities do not generalize reliably, underscoring the need for proper validation methodology in microstructure research.