  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 centre that combines localized
features. Performance evaluations are performed based on the introduced COVID19
PRIMO dataset illustrating superior performance of the proposed framework.
