@inproceedings{56cb33373f454269aae1b1d4c4582ff2,
title = "Feature Transformation and Simulation of Short Term Price Variability in Reinforcement Learning for Portfolio Management",
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.",
author = "Lin, {Yen Chih} and Jeremy Blum",
note = "Publisher Copyright: {\textcopyright} 2020 SCS.; 2020 Spring Simulation Conference, SpringSim 2020 ; Conference date: 18-05-2020 Through 21-05-2020",
year = "2020",
month = may,
doi = "10.22360/SpringSim.2020.AIS.002",
language = "English (US)",
series = "Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Barros, {Fernando J.} and Xiaolin Hu and Hamdi Kavak and {Del Barrio}, {Alberto A.}",
booktitle = "Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020",
address = "United States",
}