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• W2018964251 abstract "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 (Proceedings of the IEEE/IAFE 1997 on Computational Intelligence for Financial Engineering (CIFEr). IEEE, New York, pp 300–307 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." @default.
• W2018964251 created "2016-06-24" @default.
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• W2018964251 date "2011-09-10" @default.
• W2018964251 modified "2023-09-30" @default.
• W2018964251 title "Regime-switching recurrent reinforcement learning for investment decision making" @default.
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• W2018964251 doi "https://doi.org/10.1007/s10287-011-0131-1" @default.
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