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The novel of coronavirus (COVID-19) has suddenly and abruptly changed theworld as we knew at the start of the 3rd decade of the 21st century.Particularly, COVID-19 pandemic has negatively affected financial econometricsand stock markets across the globe. Artificial Intelligence (AI) and MachineLearning (ML)-based prediction models, especially Deep Neural Network (DNN)architectures, have the potential to act as a key enabling factor to reduce theadverse effects of the COVID-19 pandemic and future possible ones on financialmarkets. In this regard, first, a unique COVID-19 related PRIce MOvementprediction (COVID19 PRIMO) dataset is introduced in this paper, whichincorporates effects of social media trends related to COVID-19 on stock marketprice movements. Afterwards, a novel hybrid and parallel DNN-based framework isproposed that integrates different and diversified learning architectures.Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion frameworkfor Stock price Movement Prediction (COVID19-HPSMP), innovative fusionstrategies are used to combine scattered social media news related to COVID-19with historical mark data. The proposed COVID19-HPSMP consists of two parallelpaths (hence hybrid), one based on Convolutional Neural Network (CNN) withLocal/Global Attention modules, and one integrated CNN and Bi-directional LongShort term Memory (BLSTM) path. The two parallel paths are followed by amultilayer fusion layer acting as a fusion centre that combines localizedfeatures. Performance evaluations are performed based on the introduced COVID19PRIMO dataset illustrating superior performance of the proposed framework.

COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Prediction