Regime-switching recurrent reinforcement learning in automated trading

Maringer, Dietmar and Ramtohul, Tikesh. (2012) Regime-switching recurrent reinforcement learning in automated trading. In: Natural Computing in Computational Finance , 4. Berlin , pp. 93-121.

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The regime-switching recurrent reinforcement learning (RSRRL) model was first presented in [19], in the form of a GARCH-based threshold version that extended the standard RRL algorithm developed by [22]. In this study, the main aim is to investigate the influence of different transition variables, in multiple RSRRL settings and for various datasets, and compare and contrast the performance levels of the RRL and RSRRL systems in algorithmic trading experiments. The transition variables considered are GARCH-based volatility, detrended volume, and the rate of information arrival, the latter being modelled on the Mixture Distribution Hypothesis (MDH). A frictionless setting was assumed for all the experiments. The results showed that the RSRRL models yield higher Sharpe ratios than the standard RRL in-sample, but struggle to reproduce the same performance levels out-of-sample. We argue that the lack of in- and out-of-sample correlation is due to a drastic change in market conditions, and find that the RSRRL can consistently outperform the RRL only when certain conditions are present. We also find that trading volume presents a lot of promise as an indicator, and could be the way forward for the design of more sophisticated RSRRL systems.
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:Book Section, refereed
Book Section Subtype:Further Contribution in a Book
Series Name:Studies in Computational Intelligence
Note:Publication type according to Uni Basel Research Database: Book item
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Last Modified:13 Apr 2018 06:17
Deposited On:06 Apr 2017 12:40

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