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• W3091221572 abstract "Reinforcement learning has been shown capable of learning optimal strategies from imperfect information environments in order to create robust decision support systems. This paper shows that two automatic feature transformation techniques - Bayesian recurrent neural network (BRNN) for modelling future price trends and Generative Adversarial Networks (GANs) for modelling short-term realistic price variability - are able to improve the performance of reinforcement learning agents in solving portfolio management problem effectively, when measured in terms of increasing profitability and reducing risks." @default.
• W3091221572 created "2020-10-08" @default.
• W3091221572 creator A5014556856 @default.
• W3091221572 creator A5060560857 @default.
• W3091221572 date "2020-01-01" @default.
• W3091221572 modified "2023-10-14" @default.
• W3091221572 title "Feature Transformation and Simulation of Short Term Price Variability in Reinforcement Learning for Portfolio Management" @default.
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• W3091221572 doi "https://doi.org/10.22360/springsim.2020.ais.002" @default.
• W3091221572 hasPublicationYear "2020" @default.
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