An intelligent financial portfolio trading strategy using deep Q-learning

作者：

Highlights：

• An approach for financial portfolio trading using deep Q-learning is proposed.

• The approach can derive a multi-asset portfolio trading strategy.

• The approach adopts a discrete combinatorial action space.

• To overcome the technical challenges, the approach has three novel features.

• Numerical tests show the superiority of our approach.

摘要

•An approach for financial portfolio trading using deep Q-learning is proposed.•The approach can derive a multi-asset portfolio trading strategy.•The approach adopts a discrete combinatorial action space.•To overcome the technical challenges, the approach has three novel features.•Numerical tests show the superiority of our approach.

论文关键词：Portfolio trading,Reinforcement learning,Deep Q-learning,Deep neural network,Markov decision process

论文评审过程：Received 3 January 2020, Revised 26 April 2020, Accepted 14 May 2020, Available online 18 May 2020, Version of Record 29 May 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113573