machine learning for predicting stock market movement using news headlines CORD-Papers-2021-10-25 (Version 1)

Title: Machine Learning for Predicting Stock Market Movement using News Headlines
Abstract: There are many factors that affect performance of stock market, such as global and local economy, political events, supply and demand, and out of the ordinary events, as COVID-19 pandemic The factors may not only influence the stock market movement, but also influence each other We propose to observe the movement of Dow Jones Industrial Average in relations to daily news We use top-5 news headlines from Reddit to create 1Day and 5-Day models to predict if Dow Jones Industrial Average movement will be in Down and Up direction from the moment the market opens till it closes We propose use of shallow (traditional) Machine Learning algorithms and Deep Learning algorithms Additionally, we explore the effect of word representation, using TF-IDF and GloVE approaches Moreover, we evaluate our models in terms of accuracy of prediction on data sets containing data before pandemic and during pandemic Our models show that Deep Learning models uniformly have higher accuracy than Machine Learning ones Convolution Neural Network with TFIDF and 5 Days prediction performs the best for the dataset before the pandemic with accuracy of 59 6% Gated Recurrent Unit (GRU), a class of Recurrent Neural Networks, with GloVe and 1 Day prediction outperforms the other models for dataset during the pandemic with the accuracy of 62 9% 2020 IEEE
Published: 2020
Journal: IEEE Green Energy Smart Syst. Conf., IGESSC
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Trajkovic, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/trajkovic_j
Author Name: Yeh, H G H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/yeh_h_g_h
Author Name: Zhang, W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_w
license: unk
license_url: [unknown license]
source_x: WHO
source_x_url: https://www.who.int/
who_covidence_id: #1039786
has_full_text: FALSE
G_ID: machine_learning_for_predicting_stock_market_movement_using_news_headlines
S2 ID: 228098294