Portfolio management via two-stage deep learning with a joint cost
作者：
Highlights：
• A two-stage deep learning framework for portfolio management is proposed.
• A cost function that addresses both absolute return and relative return is proposed.
• The proposed methods are evaluated with an exchange traded fund dataset.
• The two-stage deep learning outperforms ordinary deep learning models.
• An additional simulation verifies the generality of the proposed methods.
摘要
•A two-stage deep learning framework for portfolio management is proposed.•A cost function that addresses both absolute return and relative return is proposed.•The proposed methods are evaluated with an exchange traded fund dataset.•The two-stage deep learning outperforms ordinary deep learning models.•An additional simulation verifies the generality of the proposed methods.
论文关键词：Deep learning,Long short-term memory,Portfolio management,Joint cost function
论文评审过程：Received 21 May 2019, Revised 24 September 2019, Accepted 16 October 2019, Available online 18 October 2019, Version of Record 31 October 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.113041
