Two Parameter Update Schemes for Recurrent Reinforcement Learning

Zhang, Jin and Maringer, Dietmar. (2014) Two Parameter Update Schemes for Recurrent Reinforcement Learning. In: 2014 IEEE Congress on Evolutionary Computation (CEC). Beijing, China , pp. 1449-1453.

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Official URL: http://edoc.unibas.ch/42886/

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Recurrent reinforcement learning (RRL) is a machine learning algorithm which has been proposed by researchers for constructing financial trading platforms. When an analysis of RRL trading performance is conducted using low frequency financial data (e.g. daily data), the weakening autocorrelation in price changes may lead to a decrease in trading profits as compared to its applications in high frequency trading. There therefore is a need to improve RRL for the purposes of daily equity trading. This paper presents two parameter update schemes (the `average elitist' and the `multiple elitist') for RRL. The purpose of the first scheme is to improve out-of-sample performance of RRL-type trading systems. The second scheme aims to exploit serial dependence in stock returns to improve trading performance, when traders deal with highly correlated stocks. Profitability and stability of the trading system are examined by using four groups of S&P stocks for the period January 2009 to December 2012. It is found that the Sharpe ratios of the stocks increase after we use the two parameter update schemes in the RRL trading system.
Faculties and Departments:06 Faculty of Business and Economics > Departement Wirtschaftswissenschaften > Professuren Wirtschaftswissenschaften > Computational Economics and Finance (Maringer)
UniBasel Contributors:Maringer, Dietmar and Zhang, Jin
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Series Name:IEEE Congress on Evolutionary Computation (CEC)
Issue Number:2014
Note:Publication type according to Uni Basel Research Database: Conference paper
Identification Number:
Last Modified:06 Apr 2018 08:47
Deposited On:06 Apr 2018 08:47

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