The US stock market experienced instability following the recession
(2007-2009). COVID-19 poses a significant challenge to US stock traders and
investors. Traders and investors should keep up with the stock market. This is
to mitigate risks and improve profits by using forecasting models that account
for the effects of the pandemic. With consideration of the COVID-19 pandemic
after the recession, two machine learning models, including Random Forest and
LSTM are used to forecast two major US stock market indices. Data on historical
prices after the big recession is used for developing machine learning models
and forecasting index returns. To evaluate the model performance during
training, cross-validation is used. Additionally, hyperparameter optimizing,
regularization, such as dropouts and weight decays, and preprocessing improve
the performances of Machine Learning techniques. Using high-accuracy machine
learning techniques, traders and investors can forecast stock market behavior,
stay ahead of their competition, and improve profitability. Keywords: COVID-19,
LSTM, S&P500, Random Forest, Russell 2000, Forecasting, Machine Learning, Time
Series JEL Code: C6, C8, G4.Comment: Pennsylvania Economic Association (PEA)- June 202