2021/01/02 by Farnoush Ronaghi, Ronaghi, Farnoush, Mohammad Salimibeni +5
Decision Sciences · Economics, Econometrics and Finance · Engineering · Medicine · #COVID-19 diagnosis using AI #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Economics and business #FOS: Electrical engineering #Machine Learning (cs.LG) #Market Dynamics and Volatility #Risk Management (q-fin.RM) #Signal Processing (eess.SP) #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2101.02287
openalex publication_date 2021/01/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.