Regime‐switching recurrent reinforcement learning for investment decision making

This paper presents the regime‐switching recurrent reinforcement learning (RSRRL) model and describes its application to investment problems. The RSRRL is a regime‐switching extension of the recurrent reinforcement learning (RRL) algorithm. The basic RRL model was proposed by Moody and Wu (1997) and presented as a methodology to solve stochastic control problems in finance. We argue that the RRL is unable to capture all the intricacies of financial time series, and propose the RSRRL as a more suitable algorithm for such type of data. This paper gives a description of two variants of the RSRRL, namely a threshold version and a smooth transition version, and compares their performance to the basic RRL model in automated trading and portfolio management applications. We use volatility as an indicator/transition variable for switching between regimes. The out‐of‐sample results are generally in favour of the RSRRL models, thereby supporting the regime‐switching approach, but some doubts exist regarding the robustness of the proposed models, especially in the presence of transaction costs.