Exploring Deep Learning on Apple Stock Volatility: Long Short-Term Memory and Gated Recurrent Unit Performance
DOI:
https://doi.org/10.57152/predatecs.v4i1.2109Keywords:
Apple Inc. (APPL), Deep Learning, Gated Recurrent Unit, Long Short-Term Memory, Stock VolatilityAbstract
Forecasting Apple Inc. (AAPL) stock prices is challenging due to the nonlinear and dynamic nature of financial data. This study compares the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures in predicting AAPL stock volatility. Daily stock data from January 1, 2020, to May 5, 2025, were used to evaluate both models under various hyperparameters. Performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that GRU with the AdamW optimizer (learning rate 0.01, batch size 8) achieved the best performance (MAE: 3.4188, RMSE: 4.9261, MAPE: 1.5648). Beyond identifying optimal hyperparameters, this study highlights critical architectural differences. The findings reveal that GRU's streamlined gating mechanism adapts more effectively and more rapidly to sudden price fluctuations than LSTM's complex memory-retention structure. These results demonstrate GRU's distinct advantage for computationally agile and accurate stock forecasting under highly volatile market conditions.
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