COVID19-HPSMP: COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction
作者：
Highlights：
• Introduction of COVID-19 related PRIce MOvement prediction (COVID19 PRIMO) dataset.
• Proposing COVID-19 adopted hybrid deep fusion framework for stock price prediction.
• Integration of COVID-19 related Twitter data with extended horizon market data.
• Generalization performance of price movement prediction across various scenarios.
• Outperforming stand-alone (non-hybrid) deep learning-based price prediction models.
摘要
•Introduction of COVID-19 related PRIce MOvement prediction (COVID19 PRIMO) dataset.•Proposing COVID-19 adopted hybrid deep fusion framework for stock price prediction.•Integration of COVID-19 related Twitter data with extended horizon market data.•Generalization performance of price movement prediction across various scenarios.•Outperforming stand-alone (non-hybrid) deep learning-based price prediction models.
论文关键词：COVID-19 pandemic,Deep Neural Networks,Hybrid models,Information fusion,Stock movement prediction
论文评审过程：Received 7 January 2021, Revised 8 July 2021, Accepted 4 September 2021, Available online 20 September 2021, Version of Record 4 October 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115879
