In the past, academics have had difficulty predicting stock prices and movements. We can accurately predict stock prices, despite the widely accepted efficient market hypothesis, according to formal arguments in the literature. Regression models for stock price forecasting developed using deep learning are presented in this article. Predictive models were developed for a well-known American company in this study. Stock prices are updated every five minutes on weekdays and weekends. Stock price data was used to build four convolutional neural networks (CNN) and five LSTM-based deep learning models for forecasting future stock prices. To determine the accuracy of forecasts, this research thoroughly examines all proposed models, including their execution time and root mean square error (RMSE).