Transition variable selection for regime switching recurrent reinforcement learning - researchr publication related

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Dietmar Maringer, Jin Zhang. Transition variable selection for regime switching recurrent reinforcement learning. In IEEE Conference on Computational Intelligence for Financial Engineering & Economics, CIFEr 2014, London, UK, March 27-28, 2014. pages 407-413, IEEE, 2014. [doi]

The following publications are possibly variants of this publication:

Regime-Switching Recurrent Reinforcement Learning in Automated TradingDietmar G. Maringer, Tikesh Ramtohul. In Anthony Brabazon, Michael O'Neill, Dietmar Maringer, editors, Natural Computing in Computational Finance - Volume 4. Volume 380 of Studies in Computational Intelligence, pages 93-121, Springer, 2012. [doi]

Regime-switching recurrent reinforcement learning for investment decision makingDietmar G. Maringer, Tikesh Ramtohul. cms, 9(1):89-107, 2012. [doi]

Automating Transition Functions: A Way To Improve Trading Profits with Recurrent Reinforcement LearningJin Zhang. ifip12 2014: 39-49 [doi]

Indicator selection for daily equity trading with recurrent reinforcement learningJin Zhang, Dietmar Maringer. gecco 2013: 1757-1758 [doi]

Portfolio Selection in the Enlarged Markovian Regime-Switching MarketXin Zhang, Tak Kuen Siu, Qingbin Meng. siamco, 48(5):3368-3388, 2010. [doi]

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