Forecasting financial signal for automated trading: An interpretable approach

作者：

Highlights：

• Deep learning was employed in this study for automated financial trading.

• Data clustering was used for the interpretability of the models’ decisions.

• Outlier elimination was used to avoid drastic fluctuations in the market.

摘要

•Deep learning was employed in this study for automated financial trading.•Data clustering was used for the interpretability of the models’ decisions.•Outlier elimination was used to avoid drastic fluctuations in the market.

论文关键词：Autoencoder,Deep learning,Forecasting,Forex,Outlier,Trading

论文评审过程：Received 5 May 2021, Revised 1 August 2022, Accepted 13 August 2022, Available online 19 August 2022, Version of Record 30 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118570