An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown

作者：

Highlights：

• A reinforcement learning trading algorithm with expected drawdown risk is proposed.

• The expected maximum drawdown is shown to improve portfolio signal generation.

• The effectiveness of the method is validated using different transaction costs.

• An adaptive portfolio rebalancing system with automated retraining is recommended.

摘要

•A reinforcement learning trading algorithm with expected drawdown risk is proposed.•The expected maximum drawdown is shown to improve portfolio signal generation.•The effectiveness of the method is validated using different transaction costs.•An adaptive portfolio rebalancing system with automated retraining is recommended.

论文关键词：Recurrent reinforcement learning,Expected maximum drawdown,Optimal portfolio rebalancing,Downside risk

论文评审过程：Received 24 March 2017, Revised 29 May 2017, Accepted 14 June 2017, Available online 15 June 2017, Version of Record 21 June 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.06.023