Regime-Aware Stock Index Forecasting Under Latent Market States: A Hybrid Statistical Learning Framework with Cross-Market Validation

This study proposes a hybrid forecasting framework that integrates Kalman Filtering (KF), Markov Switching (MS), and nonlinear recurrent learning for stock-index prediction. The KF component smooths short-term price noise, the MS model identifies latent return–volatility regimes, and the LSTM/GRU co...

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Bibliografische Detailangaben

Hauptverfasser:	Chunxia Tian, Roengchai Tansuchat, Songsak Sriboonchitta
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2026-06-01
Schriftenreihe:	Forecasting
Schlagworte:	stock forecasting Kalman filtering Markov switching regime-aware forecasting LSTM GRU
Online-Zugang:	https://www.mdpi.com/2571-9394/8/3/50
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