  With the heightened volatility in stock prices during the Covid-19 pandemic,
the need for price forecasting has become more critical. We investigated the
forecast performance of four models including Long-Short Term Memory, XGBoost,
Autoregression, and Last Value on stock prices of Facebook, Amazon, Tesla,
Google, and Apple in COVID-19 pandemic time to understand the accuracy and
predictability of the models in this highly volatile time region. To train the
models, the data of all stocks are split into train and test datasets. The test
dataset starts from January 2020 to April 2021 which covers the COVID-19
pandemic period. The results show that the Autoregression and Last value models
have higher accuracy in predicting the stock prices because of the strong
correlation between the previous day and the next day's price value.
Additionally, the results suggest that the machine learning models (Long-Short
Term Memory and XGBoost) are not performing as well as Autoregression models
when the market experiences high volatility.
