Back to Search
Start Over
[formula omitted]: COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction.
- Source :
-
Expert Systems with Applications . Jan2022, Vol. 187, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- The novel of coronavirus (COVID-19) has suddenly and abruptly changed the world as we knew at the start of the 3rd decade of the 21st century. Particularly, COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe. Artificial Intelligence (AI) and Machine Learning (ML)-based prediction models, especially Deep Neural Network (DNN) architectures, have the potential to act as a key enabling factor to reduce the adverse effects of the COVID-19 pandemic and future possible ones on financial markets. In this regard, first, a unique COVID-19 related PRIce MOvement prediction (COVID19 PRIMO) dataset is introduced in this paper, which incorporates effects of social media trends related to COVID-19 on stock market price movements. Afterwards, a novel hybrid and parallel DNN-based framework is proposed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction (COVID19-HPSMP), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data. The proposed COVID19-HPSMP consists of two parallel paths (hence hybrid), one based on Convolutional Neural Network (CNN) with Local/Global Attention modules, and one integrated CNN and Bi-directional Long Short term Memory (BLSTM) path. The two parallel paths are followed by a multilayer fusion layer acting as a fusion center that combines localized features. Performance evaluations are performed based on the introduced COVID19 PRIMO dataset illustrating superior performance of the proposed framework. • 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 187
- Database :
- Academic Search Index
- Journal :
- Expert Systems with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 153176524
- Full Text :
- https://doi.org/10.1016/j.eswa.2021.115879