Feature transformation and simulation of short term price variability in reinforcement learning for portfolio management

Yen Chih Lin, Jeremy Blum

Research output: Contribution to journalConference article

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.

Original languageEnglish (US)
Pages (from-to)13-22
Number of pages10
JournalSimulation Series
Volume52
Issue number1
StatePublished - 2020
Event2020 Spring Simulation Multiconference, SpringSim 2020 - Virtual, Online
Duration: May 18 2020May 21 2020

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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