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Transition Variable Selection for Regime Switching Recurrent Reinforcement Learning

Maringer, Dietmar and Zhang, Jin. (2014) Transition Variable Selection for Regime Switching Recurrent Reinforcement Learning. In: Proceedings of the 2014 IEEE Conference on Computational Intelligence for Financial Engineering and Economics. London, UK, pp. 407-413.

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Official URL: http://edoc.unibas.ch/42884/

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Abstract

Non-linear time series models, such as regime-switching (RS), have become increasingly popular in economics. In the literature, regime-switching recurrent reinforcement learning (RS-RRL), a combined technique of statistical modeling and machine learning, has been proposed to build financial trading platforms and enhance trading profits by modeling the nonlinear dynamics of stock returns with smooth transition autoregressive (STAR) models. In this paper, we address the transition variable selection issue in the RS-RRL trading system. Four indicators, namely volume, relative strength index, implied volatility and conditional volatility are considered as possible options for transition variable selection in RS-RRL. Of the four indicators, it is found that the RS-RRL trading system with the volume indicator produces a better Sharpe ratio than others.
Faculties and Departments:06 Faculty of Business and Economics > Departement Wirtschaftswissenschaften > Professuren Wirtschaftswissenschaften > Computational Economics and Finance (Maringer)
UniBasel Contributors:Zhang, Jin and Maringer, Dietmar
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:IEEE
e-ISBN:978-1-4799-2380-9
Series Name:IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)
Issue Number:2014
ISSN:2380-8454
Note:Publication type according to Uni Basel Research Database: Conference paper
Identification Number:
Last Modified:06 Apr 2018 09:00
Deposited On:06 Apr 2018 08:59

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