Comparison of LSTM and LSTM with Grid Search Optimization for Stock Price Prediction of Saudi Arabian Oil Company (Aramco)

Yilmaz, Eva
Tcrol, Ema

International Journal Artificial Intelligent and Informatics
Vol 3, No 2 (2025)

This study compares the performance of the Long Short-Term Memory (LSTM) model without optimization and LSTM with Grid Search optimization in predicting Saudi Arabian Oil Company (Aramco) stock prices. Using stock price data from December 2019 to December 2023, this study aims to identify a more accurate prediction model. Results show that the LSTM model with Grid Search optimization provides a significant performance improvement compared to the standard LSTM model, with a decrease in Root Mean Square Error (RMSE) of 11.63% on the test data. This finding indicates the importance of hyperparameter optimization in improving the accuracy of stock price prediction models, especially for the world's largest oil company such as Aramco, whose stock price can be affected by various macroeconomic and geopolitical factors.