Prediction of Stock Prices using LSTM Networks

Stock or equity market has a big impact on the economy today. The primary determinant of an investor’s profit is whether the share price increases or decreases. Today’s forecasting techniques combine non-linear and linear algorithms (AR, MA, and ARIMA). (ARCH, GARCH, Neural Networks), yet their primary goal is forecasting the movement of a stock index or the prediction of a single employing the closing price for each day. The suggested approach is a method independent of models. This research paper focuses on the scope of the LSTM network in prediction of stock prices. The model uses the LSTM network which is the upgraded model of the RNN and explores the features of LSTM network provides over the RNN.