Abstract:

As our world moves more and more into the online medium so does our data. This massive movement [1] allows the analysis of nancial markets using methods that would not have been possible 20 years ago due to a lack of sucient data. In this paper, we review some basic ideas of stock market data handled as a time series, need of RNN (Recurrent Neural Network), survey previous works, and use a LSTM (Long Short-Term Memory) network to forecast stock value during and after the 2020 market crash. The prediction accuracy is calculated and investigated with revefence to S&P 500, PHLX Semiconductor (SOX), and XLRE.