Utilizing Machine Learning to Predict the Stock Market

Abstract

For a very long time, stock price forecasting has been a focus of significant research. According to supporters of the efficient market hypothesis, it is difficult to make accurate stock price estimates. Researchers have also examined stock market technical analysis to use the most sophisticated data mining tools to identify patterns in the price movements of companies. In this research, we create several Models based on deep learning and machine learning can forecast stock values using a hybrid modelling technique. To conduct our analysis we used the NIFTY 50 index values from the National Stock Exchange (NSE) of India from July 2000 to June 2020. The regression models all have comprehensive findings on numerous measures set. The results unambiguously demonstrate that the most precise model is an LSTM-based univariate model that forecasts the open value of the ifty 50 time series for the upcoming week using one week's worth of historical data as input.