Regime-switching recurrent reinforcement learning for investment decision making

Maringer, Dietmar and Ramtohul, Tikesh. (2012) Regime-switching recurrent reinforcement learning for investment decision making. Computational Management Science, Vol. 9, H. 1. pp. 89-107.

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Official URL: http://edoc.unibas.ch/dok/A6070165

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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.
Faculties and Departments:06 Faculty of Business and Economics > Departement Wirtschaftswissenschaften > Professuren Wirtschaftswissenschaften > Computational Economics and Finance (Maringer)
UniBasel Contributors:Maringer, Dietmar and Ramtohul, Tikesh
Item Type:Article, refereed
Article Subtype:Research Article
Note:Publication type according to Uni Basel Research Database: Journal article
Last Modified:01 Mar 2013 11:13
Deposited On:01 Mar 2013 11:09

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