COVID19-HPSMP : COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction
Expert Systems with Applications, ISSN: 0957-4174, Vol: 187, Page: 115879
2022
- 22Citations
- 106Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations22
- Citation Indexes22
- 22
- CrossRef8
- Captures106
- Readers106
- 106
Article Description
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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0957417421012380; http://dx.doi.org/10.1016/j.eswa.2021.115879; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85116379143&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/34566272; https://linkinghub.elsevier.com/retrieve/pii/S0957417421012380; https://dx.doi.org/10.1016/j.eswa.2021.115879
Elsevier BV
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