Computational Management Science

, Volume 9, Issue 1, pp 89–107

Regime-switching recurrent reinforcement learning for investment decision making

Authors

    • Universität Basel
  • Tikesh Ramtohul
    • Universität Basel
Original Paper

DOI: 10.1007/s10287-011-0131-1

Cite this article as:
Maringer, D. & Ramtohul, T. Comput Manag Sci (2012) 9: 89. doi:10.1007/s10287-011-0131-1

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.

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© Springer-Verlag 2011