Abstract

AbstractThe market capitalization of GameStop (GME) was listed to be 918 million dollars at the beginning of 2020, increasing 25 fold to 23 billion dollars by the end of January 2021. Similar shifts in market capitalization were seen in other publicly traded companies as well. In this study, sentiment data from r/WallStreetBets discussion board and stock price data over time are garnered. This information is utilized to train a longitudinal long short-term memory model (LSTM) to predict the stock prices of GME and AMC Entertainment Holdings (AMC). Using LSTM architecture, three models are developed with separate input features: previous day’s close price, sentiment data only, and a model with both sets of data. It is observed that sentiment data alone can be predictive of stock prices. However, the models containing solely close price or close price and sentiment data perform significantly better in terms of validation loss, and an average difference in stock price prediction during the validation set of $X and $Y for GME and AMC, respectively. These results show that if institutional investors had included sentiment data in their longitudinal models for predicting AMC and GME stock prices, they might have been able to avoid the short squeeze event.KeywordsFinanceMachine learningLSTMSentiment analysisStocks

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