[Abadi, 2016] Abadi, M., et al. (2016). Tensorflow: Large-scale Machine Learning on Heterogeneous Distributed Systems. arXiv:1603.04467. [Al-Awadhi, 2020] Al-Awadhi, A.M., Alsaifi, K., Al-Awadhi, A., & Alhammadi, S. (2020). Death and Contagious Infectious Diseases: Impact of the COVID-19 Virus on Stock Market Returns. Journal of Behavioral and Experimental Finance, 27. [Al-Dulaimi, 2019] Al-Dulaimi, A., Zabihi, S., Asif, A., & Mohammadi, A. (2019). A Multimodal and Hybrid Deep Neural Network Model for Remaining Useful Life Estimation. Computers in Industry, 108, 186-196. [Anik, 2020] Anik, M.M., Arefin, M.S., & Dewan, M.A.A. (2020). An Intelligent Technique for Stock Market Prediction. Proceedings of International Joint Conference on Computational Intelligence, Singapore. [Ansari, 2020] Ansari, S.A., & Del Val, E.B. (2020). SutteARIMA: Short-term Forecasting Method, a Case: Covid-19 and Stock Market in Spain. Science of the Total Environment, 729. [Ansari, 2017] Ansari, A. (2017). Sutte Indicator: A Technical Indicator in Stock Market. International Journal of Economics and Financial Issues, 7. [Baek, 2020] Baek, S., Mohanty, S.K., & Glambosky, M. (2020). COVID-19 and Stock Market Volatility: An Industry Level Analysis. Finance Research Letters, 37. [Beckmann, 2017] Beckmann, M. (2017). Stock Price Change Prediction Using News Text Mining. Federal University of Rio de Janeiro. [Bhatt, 2021] Bhatt, A. R., Ganatra, A., & Kotecha, K. (2021) Cervical Cancer Detection in Papsmear whole slide Images using convNet with Transfer Learning and Progressive Resizing. PeerJ Computer Science7:e348. [Bollen, 2011] Bollen, J., & Mao, H. (2011). Twitter Mood as a Stock Market Predictor. in Computer, 44,91-94. [Bustos, 2020] Bustos, O., & Pomares-Quimbaya, A. (2020). Stock Market Movement Forecast: A Systematic Review. Expert Systems with Applications, 156. [Caruana, 1998] Caruana, R. (1998). Multitask Learning. Machine Learning, 28, 41-75. [Chong, 2019] Chong, E., Han, C., Park, F.C. (2019). Deep Learning Networks for Stock Market Analysis and Prediction: Methodology, Data Representations, and Case Studies. Expert Systems with Applications, 83. [PRIMO, 2021]COVID19 PRIMO Dataset: https://github.com/MSBeni/COVID19 PRIMO#COVID19-PRIMO. [Choudrie, 2021] Choudrie, J., Banerjee, S., Kotecha, K., Walambe, R., Karende, H., & Ameta, J. (2021). Machine Learning Techniques and Older Adults Processing of online Information and Misinformation: A COVID-19 Study. Computers in Human Behavior, 119. [Clement, 2020] Clement, Number of Monetizable Daily ActiveTwitter Users (mDAU) Worldwide from 1st Quarter 2017 to 1st Quarter 2020. https://www.statista.com/statistics/970920/monetizable-daily-active-twitter-users-worldwide/. [Di, 2018] Di, W., Bhardwaj, A., & Wei, J. (2018). Deep Learning Essentials: your Hands-on Guide to the Fundamentals of Deep Learning and Neural Network Modeling. Packt Publishing Inc. [Edwards, 2007] Edwards, R. D., Bassetti, W., & Magee, J. (2007). Technical Analysis of Stock Trends(4th ed.). in CRC Press, 134-140. [Fama, 1998] Fama, E.F. (1998). Market Efficiency, Long-term Returns, and Behavioral Finance. Journal of Financial Economics, 49, 283-306. [Frankel, 1995] Frankel, J. A. (1995). Financial Markets And Monetary Policy. MIT Press, 87-95. [Gite, 2021] Gite, S., Khatavkar, H., Kotecha, K., Srivastava, S., Maheshwari, P., & Pandey, N. (2021). Explainable Stock Prices Prediction from Financial News Articles using Sentiment Analysis. PeerJ Computer Science, 7:e340. [Hochreiter, 1997] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. [Hoseinzade, 2019] Hoseinzade, E., & Haratizadeh, S. (2019). CNNpred: CNN-based Stock Market Prediction using a Diverse Set of Variables. Expert Systems with Applications, 129. [Hu, 2019] Hu, Z., Liu, W., Bian, J., & Liu, X. (2019). Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction. arXiv:1712.02136. [Huang, 2020] Huang, J.Y., & Li, J.H. (2020). Using Social Media Mining Technology to Improve Stock Price Forecast Accuracy. Journal of Forcasting, 39, 104-116. [Ioffe, 2015] Ioffe, S., & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv preprint arXiv:1502.03167. [Jiang, 2020] Jiang, W. (2020). Applications of Deep Learning in Stock Market Prediction: Recent Progress. arXiv preprint arXiv:2003.01859. [Kingma, 2014] Kingma, D.P., Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980. [Koshiyama, 2020] Koshiyama, S., Firoozye, N., & Treleaven, P. (2020). Algorithms in Future Capital Markets. https://dx.doi.org/10.2139/ssrn.3527511. [Li, 2014] Li, X., Xie, H., Chen, L., Wang, J., & Deng, X. (2014). News Impact on Stock Price Return via Sentiment Analysis. Knowledge-Based Systems, 69, 14-23. [Loper, 2002] Loper, E., & Bird, S. (2002). NLTK: The Natural Language Toolkit. In Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, Philadelphia, PA. [Luss, 2015] Luss, R., & D’Aspremont, A. (2015). Predicting Abnormal Returns from News Using Text Classification. Quantitative Finance, 15, 999-1012. [Mazue, 2020] Mazur, M., Dang, M., & Vega, M. (2020). COVID-19 and the March 2020 Stock Market Crash. Evidence from S&P1500. Finance Research Letters, 38. [Mohammadi, 2017] Mohammadi, A., Zhang, X., & Plataniotis, K. N. (2017). Interactive Gaussian-sum Filtering for Estimating Systematic Risk in Financial Econometrics. IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, QC. [Narkhede, 2021] Narkhede, P., Walambe, R., Mandaokar, S., Chandel, P., Kotecha, & K., Ghinea, G. (2021). Gas Detection and Identification Using Multimodal Artificial Intelligence based Sensor Fusion. Applied System Innovation, 4(3). [Patel, 2015] Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting Stock and Stock Price Index Movement using Trend Deterministic Data Preparation and Machine Learning Techniques. Expert Systems with Applications, 42, 259-268. [Radojicic, 2020] Radojicic, D., & Kredatus, S. (2020). The Impact of Stock Market Price Fourier Transform Analysis on the Gated Recurrent Unit Classifier Model. Expert Systems with Applications, 159. [Rekabsaz, 2017] Rekabsaz, N., et al. (2017). Volatility Prediction Using Financial Disclosures Sentiments with Word Embedding-based IR Models. Association for Computational Linguistics, Vancouver, BC. [Pennington, 2014] Pennington, J., Socher, R., Manning, C.D. (2014). Glove: Glove Vector of Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing(EMNLP), Doha, Qatar. [Plyakha, 2017] Plyakha, Y., Uppal, R., & Vilkov, G. (2017). Why Does an Equal-Weighted Portfolio Outperform Value- and Price-Weighted Portfolios?. http://dx.doi.org/10.2139/ssrn.2724535. [Rezaei, 2020] Rezaei, H., Faaljou, H., & Mansourfar, G. (2020). Stock Price Prediction using Deep Learning and Frequency Decomposition. Expert Systems with Applications, 169. [Ronaghi, 2021] Ronaghi, F., Salimibeni, M., Naderkhani, F., & Mohammadi, A. (2020). OVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Prediction. Minor Revisions Submitted at Expert Systems With Applications, July 2021. [Ronaghi, 2020] Ronaghi, F., Salimibeni, M., Naderkhani, F., & Mohammadi, A. (2020). NDSMPF: A Noisy Deep Neural Network Fusion Framework for Stock Price Movement Prediction. 2020 IEEE 23rd International Conference on Information Fusion (FUSION), Sun City, Africa. [Schumaker, 2009] Schumaker, R.P., & Chen, H. (2009). Textual Analysis of Stock Market Prediction Using Breaking Financial News: The AZFin Text System. ACM Transactions on Information Systems, 12. [Seo, 2017] Seo, S., Huang, J., Yang, H., & Liu, Y. (2017). Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction. Association for Computing Machinery, New York, NY. [Seong, 2021] Seong, N., & Nam, K. (2021). Predicting Stock Movements based on Financial News with Segmentation. Expert Systems with Applications, 64. [Tetlock, 2007] Tetlock, P.C. (2017). Giving Content to Investor Sentiment: The Role of Media in The Stock Market. The Journal of Finance, 62, 1139-1168. [Vaidya, 2020] Vaidya, R. (2020). Moving Average Convergence-Divergence (MACD) Trading Rule: An Application in Nepalese Stock Market(NEPSE). Quantitative Economics and Management Studies (QEMS), 1, 6. https://doi.org/10.35877/454RI.qems197. [Xie, 2013] Xie, B., Passonneau, R.J., Wu, L., & Creamer, G.G. (2013). Semantic Frames to Predict Stock Price Movement. Association for Computational Linguistics, Sofia, Bulgaria. [Xu, 2018] Xu, Y., & Cohen, S.B. (2018). Stock Movement Prediction from Tweets and Historical Prices. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia. [Yun, 2019] Yun, H., Sim, G., & Seok, J. (2019). Stock Prices Prediction using the Title of Newspaper Articles with Korean Natural Language Processing. 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Okinawa, Japan. [Zhang, 2021] Zhang, Y., Chu, G., & Shen, D. (2021). The Role of Investor Attention in Predicting Stock Prices: The Long Short-Term Memory Networks Perspective. Finance Research Letters, 38. [Zhang, 2020] Zhang, Z., Zohren, S., & Roberts, S. (2020). Deep Learning for Portfolio Optimization. https://arxiv.org/abs/2005.13665. [1] J. A Frankel, “Financial Markets And Monetary Policy,” MIT Press, 1995, pp. 87-95. [Arash, 2017] A. Mohammadi, X. Zhang and K. N. Plataniotis, “Interactive Gaussian-sum Filtering for Estimating Systematic Risk in Financial Econometrics,” IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, QC, 2017, pp. 903-907. [Jiang, 2020] W. Jiang, “ Applications of Deep Learning in Stock Market Prediction: Recent Progress,” arXiv, Statistical Finance, 2020.