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Analysis of Social Media Impact on Stock Price Movements Using Machine Learning Anomaly Detection
Intelligent Automation and Soft Computing ; 36(3):3405-3423, 2023.
Article in English | Scopus | ID: covidwho-2255844
ABSTRACT
The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from new perspec-tives. The meme stock mania of 2021 brought together stock traders and investors that were also active on social media. This mania was in good part driven by retail investors' discussions on investment strategies that occurred on social media platforms such as Reddit during the COVID-19 lockdowns. The stock trades by these retail investors were then executed using services like Robinhood. In this paper, machine learning models are used to try and predict the stock price movements of two meme stocks GameStop ($GME) and AMC Entertainment ($AMC). Two sentiment metrics of the daily social media discussions about these stocks on Red-dit are generated and used together with 85 other fundamental and technical indicators as the feature set for the machine learning models. It is demonstrated that through the use of a carefully chosen mix of a meme stock's fundamental indica-tors, technical indicators, and social media sentiment scores, it is possible to predict the stocks' next-day closing prices. Also, using an anomaly detection model, and the daily Reddit discussions about a meme stock, it was possible to identify potential market manipulators. © 2023, Tech Science Press. All rights reserved.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies Language: English Journal: Intelligent Automation and Soft Computing Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies Language: English Journal: Intelligent Automation and Soft Computing Year: 2023 Document Type: Article