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Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach
The problem of stock market prediction is a challenging task owing to its complex nature and the numerous indirect factors at play. The sentiments regarding socio-political issues such as wars and pandemics can affect stock prices. The spread of the COVID-19 pandemic continues to take a toll on the...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier B.V. on behalf of African Institute of Mathematical Sciences / Next Einstein Initiative.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9705006/ https://www.ncbi.nlm.nih.gov/pubmed/36465525 http://dx.doi.org/10.1016/j.sciaf.2022.e01480 |
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author | Jena, Pradyot Ranjan Majhi, Ritanjali |
author_facet | Jena, Pradyot Ranjan Majhi, Ritanjali |
author_sort | Jena, Pradyot Ranjan |
collection | PubMed |
description | The problem of stock market prediction is a challenging task owing to its complex nature and the numerous indirect factors at play. The sentiments regarding socio-political issues such as wars and pandemics can affect stock prices. The spread of the COVID-19 pandemic continues to take a toll on the economy and fluctuations in sentiment of the concerns about the health impacts of the disease can be captured from the microblogging platform, Twitter. We examined how these sentiments during the Covid-19 pandemic and the health impacts arising from the disease along with other macroeconomic indicators provide useful information to predict the stock indices in a more accurate manner. We developed a machine learning model namely, long-short term memory (LSTM) networks to predict the impact of the Covid-19 induced sentiments on the stock values of different sectors in the United States and India. We did the same predictions using the timeseries statistical models such as autoregressive moving average model and the linear regression model. We then compared the performance of the LSTM and the timeseries statistical models to find that the machine learning model has produced more accurate predictions of the stock indices. The performance of the models across the sectors and between the United States and India are compared to draw economic inferences. |
format | Online Article Text |
id | pubmed-9705006 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Author(s). Published by Elsevier B.V. on behalf of African Institute of Mathematical Sciences / Next Einstein Initiative. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97050062022-11-29 Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach Jena, Pradyot Ranjan Majhi, Ritanjali Sci Afr Article The problem of stock market prediction is a challenging task owing to its complex nature and the numerous indirect factors at play. The sentiments regarding socio-political issues such as wars and pandemics can affect stock prices. The spread of the COVID-19 pandemic continues to take a toll on the economy and fluctuations in sentiment of the concerns about the health impacts of the disease can be captured from the microblogging platform, Twitter. We examined how these sentiments during the Covid-19 pandemic and the health impacts arising from the disease along with other macroeconomic indicators provide useful information to predict the stock indices in a more accurate manner. We developed a machine learning model namely, long-short term memory (LSTM) networks to predict the impact of the Covid-19 induced sentiments on the stock values of different sectors in the United States and India. We did the same predictions using the timeseries statistical models such as autoregressive moving average model and the linear regression model. We then compared the performance of the LSTM and the timeseries statistical models to find that the machine learning model has produced more accurate predictions of the stock indices. The performance of the models across the sectors and between the United States and India are compared to draw economic inferences. The Author(s). Published by Elsevier B.V. on behalf of African Institute of Mathematical Sciences / Next Einstein Initiative. 2023-03 2022-11-29 /pmc/articles/PMC9705006/ /pubmed/36465525 http://dx.doi.org/10.1016/j.sciaf.2022.e01480 Text en © 2022 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Jena, Pradyot Ranjan Majhi, Ritanjali Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach |
title | Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach |
title_full | Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach |
title_fullStr | Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach |
title_full_unstemmed | Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach |
title_short | Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach |
title_sort | are twitter sentiments during covid-19 pandemic a critical determinant to predict stock market movements? a machine learning approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9705006/ https://www.ncbi.nlm.nih.gov/pubmed/36465525 http://dx.doi.org/10.1016/j.sciaf.2022.e01480 |
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