GRAPES: semi-automatic approach for forecasting models to predict GameStop prices using cloud computing and machine learning Online publication date: Fri, 14-Oct-2022
by Tan Van Vo; Sukhpal Singh Gill
International Journal of Grid and Utility Computing (IJGUC), Vol. 13, No. 5, 2022
Abstract: Since the Covid-19 pandemic, we have seen a surge of retail investors that now can easily trade anywhere in the world with just a Smartphone. Social media groups like Reddit's WallStreetBets have almost put a few hedge funds close to bankruptcy by driving GameStop share prices to the sky. In this work, we propose a framework called GRAPES which uses Cloud Computing and Machine Learning to explore various forecasting techniques in predicting GameStop prices. In addition to this, this work also provides light insight into semi-automating forecasting models using tools such as Google Cloud Platform (GCP), Airflow and Streamlit. Moreover, we monitored the investment funds from Ark Invest to provide additional insight into the market in general. Overall, the paper shows the Autoregressive Moving Average (ARMA) model gives the best accuracy based on the Mean Absolute Percentage Error (MAPE) of 1.12%. This means the predictive model is out with an average of 1.12% from the actual price.
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