The stock market is a highly volatile industry with ever changing bull (rise) and bear (fall) trends. This paper proposes a new hybrid model using Long Short-Term Memory (LSTM), a Recurrent Neural Network (RNN) technique and Auto Regressive Integrated Moving Average (ARIMA), a time series forecasting technique to capture the live stock market data of S&P 500 using preexisting Application Programming Interface (API). Rise and fall in stock values in the previous years is analyzed. A novel LSTM-ARIMA hybrid is designed for capturing the linear and non-linear portions of the time series. The Prophet forecasting library by Facebook has also been used that requires less preprocessing. Finally, both the approaches are compared and the one with better performance is accepted for the final stock market prediction system. In this case, Prophet has a high Root Mean Square Error (RMSE) of 27.59 and Mean Square Error (MSE) of 761.33 whereas the ARIMA-LSTM hybrid gives an MSE of 3.03 and RMSE of 1.74 along with a 99% fit of the model. Hence the hybrid performs much better than Prophet and is accepted as the final algorithm for implementation. © 2020 School of Science.