Stock Market Prediction during Coronavirus Outbreak : The Application of Geometric Brownian Motion and Prophet Model

This paper explores the capacity of Geometric Brownian Motion (GBM) and Prophet model to predict stock market returns during coronavirus outbreak. To the best of our knowledge this is the first research that compares GBM and prophet model in order to forcast stock market volatility especially during COVID-19 pandemic.The data set used in this study consists of the daily index of the Standard and Poor’s 500 (S& P 500) during the period (2017-2021). Forecasting accuracy is measured through four metrics: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Theil Inequality Coefficient (U). The S& P 500 stock prices are collected from Datastream database.Despite the high volatiliy of the stock market especially during COVID-19 pandemic period, the empirical results show that the proposed models have higher accuracy in predicting the future value of the stock returns. More precisely, the GBM model can be considered as the most suitable technique for short-term stock returns prediction. Therefore, the accurate predictions that provide this model can significantly help investors in his decision making