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• W4310137307 startingPage "e01480" @default.
• W4310137307 abstract "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." @default.
• W4310137307 created "2022-11-30" @default.
• W4310137307 creator A5007797350 @default.
• W4310137307 creator A5088406406 @default.
• W4310137307 date "2023-03-01" @default.
• W4310137307 modified "2023-10-18" @default.
• W4310137307 title "Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach" @default.
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• W4310137307 doi "https://doi.org/10.1016/j.sciaf.2022.e01480" @default.
• W4310137307 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36465525" @default.
• W4310137307 hasPublicationYear "2023" @default.
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