PortRSMs: Learning Regime Shifts for Portfolio Policy

Bingde Liu () and Ryutaro Ichise ()
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Bingde Liu: Department of Industrial Engineering and Economics, School of Engineering, Institute of Science Tokyo, Tokyo 152-8550, Japan
Ryutaro Ichise: Department of Industrial Engineering and Economics, School of Engineering, Institute of Science Tokyo, Tokyo 152-8550, Japan

JRFM, 2025, vol. 18, issue 8, 1-17

Abstract: This study proposes a novel Deep Reinforcement Learning (DRL) policy network structure for portfolio management called PortRSMs. PortRSMs employs stacked State-Space Models (SSMs) for the modeling of multi-scale continuous regime shifts in financial time series, striking a balance between exploring consistent distribution properties over short periods and maintaining sensitivity to sudden shocks in price sequences. PortRSMs also performs cross-asset regime fusion through hypergraph attention mechanisms, providing a more comprehensive state space for describing changes in asset correlations and co-integration. Experiments conducted on two different trading frequencies in the stock markets of the United States and Hong Kong show the superiority of PortRSMs compared to other approaches in terms of profitability, risk–return balancing, robustness, and the ability to handle sudden market shocks. Specifically, PortRSMs achieves up to a 0.03 improvement in the annual Sharpe ratio in the U.S. market, and up to a 0.12 improvement for the Hong Kong market compared to baseline methods.

Keywords: deep reinforcement learning; portfolio management; financial time series; regime shift models; state-space models (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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