Automating transition functions: a way to improve trading profits with recurrent reinforcement learning

Zhang, Jin. (2014) Automating transition functions: a way to improve trading profits with recurrent reinforcement learning. In: Artificial Intelligence Applications and Innovations, 436. Heidelberg, pp. 39-49.

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Official URL: http://edoc.unibas.ch/42885/

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This paper presents an application of the logistic smooth transition function and recurrent reinforcement learning for designing financial trading systems. We propose a trading system which is an upgraded version of the regime-switching recurrent reinforcement learning (RS-RRL) trading system referred to in the literature. In our proposed system (RS-RRL 2.0), we use an automated transition function to model the regime switches in equity returns. Unlike the original RS-RRL trading system, the dynamic of the transition function in our trading system is driven by utility maximization, which is in line with the trading purpose. Volume, relative strength index, price-to-earnings ratio, moving average prices from technical analysis, and the conditional volatility from a GARCH model are considered as possible options for the transition variable in RS-RRL type trading systems. The significance of Sharpe ratios, the choice of transition variables, and the stability of the trading system are examined by using the daily data of 20 Swiss SPI stocks for the period April 2009 to September 2013. The results from our experiment show that our proposed trading system outperforms the original RS-RRL and RRL trading systems suggested in the literature in terms of better Sharpe ratios recorded in three consecutive out-of-sample periods.
Faculties and Departments:06 Faculty of Business and Economics > Departement Wirtschaftswissenschaften > Professuren Wirtschaftswissenschaften > Computational Economics and Finance (Maringer)
UniBasel Contributors:Zhang, Jin
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Series Name:IFIP Advances in Information and Communication Technology
Note:Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:06 Apr 2018 12:00
Deposited On:06 Apr 2018 12:00

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