It has been long since researchers as well as investors and stakeholders who are actively pursuing financial markets are trying to analyze stock price movements and predict its trend more accurately. To minimize the forecasting risk and make the most profit, several methods have been used among which Deep Learning was at the center of attention in recent years. Deep learning techniques include analyses of historical data and recognizing patterns that can assist scientists to make a more precise prediction. This paper focuses on the application of a optimization approach called Neural Network Algorithm to optimize Long short-term Memory for the prediction of financial time series. The findings reveal that the utilization of an optimization technique such as Neural Network Algorithm to optimize Long Short-term Memory neural networks results in a notable improvement of 40%, 65%, 4%, and 85% in the MAPE, Theil U, R, and RMSE metrics, respectively. Consequently, this leads to even more accurate results and more precise predictions.