It is difficult to predict the future price of assets due to the abnormally high volatility of the financialmarket. Financialtime seriesdata ismore complexandprone toerrorsthanotherstatisticaldata. Itshowslong term trends and seasonal variations. Developing more realistic models for estimating and extractingmeaningful statistics from it is a great research endeavor. Due to the nature of data nonlinearity, thetraditional statistical models were not very accurate in terms of financial forecasting. Hence, various softcomputing techniques have been developed to make the models more robust and accurate. This paperaims to build a Deep learning model that can predict the future asset values of provided financial datawith the help of Recurrent Neural Network (RNN), especially (LSTM) Long Short-Term Memory topredictionofstock marketdata.