LSTM Networks for Improved Stock Price Prediction: A Comparison of Apple, Microsoft, and Amazon Stocks

Stock price prediction remains a critical endeavor in financial markets, facilitating informed decision-making for investors and analysts. In this research, we present an innovative approach utilizing (LSTM) networks to forecast future stock prices for three prominent companies: Amazon, Apple, and Microsoft. Our model leverages historical stock price data spanning from January 2013 to January 2022, focusing on the closing prices of the respective stocks. By employing LSTM networks with stacked layers and dropout regularization, our methodology aims to capture intricate temporal dependencies inherent in stock price movements. The performance of our model is very much measured with the most crucial metrics such as(MSE), (RMSE) and (MAE). Through a comparison of our performed analyses we show the effectiveness of our approach in the generate of the accurate forecasts for the next trading day. Notably, our results indicate superior predictive performance for Amazon stock, followed by Apple and Microsoft. This research contributes to the advancement of predictive modeling techniques in financial forecasting and offers valuable insights for investors and financial analysts seeking to optimize their investment strategies. Further exploration of LSTM-based approaches holds promising potential for enhancing stock price prediction accuracy and informing decision-making in the dynamic landscape of financial markets.