Stock Market Price Prediction: Text Analytics of the GameStop Short Squeeze

Abstract

Analytics on the stock market is always a topic of interest by many including researchers to prove that financial outcomes could be analyzed beforehand therefore producing insights. In the year 2020 where the pandemic hit globally, the share price of GameStop suffered an unprecedented short squeeze which was a result of selling activities by major investors and buying activities by netizens primarily active on Reddit. Online media was actively covering surface stories about the short squeeze but detailed and extensive research about the event was not seen and done by many. Upon further investigation, a research gap was found that a limited scale of research had performed analysis on the event with text analytics approach and that formulates the larger goal of this research. In this paper, the scope of analytics was mainly split into two approaches, where we first build a clustering model to understand the text behavior of the community, and then a regression model to predict the changes of share price based on the features of their text. With that, we will not only be able to discover the behaviors and sentiment of the community towards the stock, but also predicting the movement of share price using textual data.