Forecasting the Stability of COVID-19 Vaccine Companies Stock Market using LSTM and Time-series Models

Abstract

Stock price forecasting is challenging because stock markets are volatile and nonlinear in nature. With the impact of the Covid-19 outbreak in 2019, stock market forecasting has become more complicated. The goal of this study is to identify the top brands in the global covid-19 vaccination market and their impact on the stock market. By forecasting the next months stock exchange, traders will be able to select the most suitable company to invest in. In this paper, MSE, RMSE, and MAPE are used as evaluation matrices to compare timeseries and neural network models such as Arima, Prophet, and LSTM. According to the results of a comparative analysis, the LSTM model produced the most accurate predictions on majority stock prices dataset for the selected vaccination brands. Brand stability is achieved by predicting future stock exchange prices using an outperformed LSTM model. As a result, BioNTech has been identified as a high potential stable brand that the buyer can invest to gain enormous returns among the massive number of probabilistic events.