Hybrid CNN-LSTM Deep Learning Framework for Predicting Stock Market Movements Using Technical Indicators and Historical Price Data

This study investigates the application of deep learning models for stock market forecasting, focusing on the comparative performance of a baseline Long Short-Term Memory (LSTM) model and a Hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) architecture. The research aims to evaluate whether the integration of convolutional and recurrent layers can enhance predictive accuracy, stability, and generalization in financial time-series modeling. Historical stock data from Apple Inc. were used, with 80 percent allocated for training and 20 percent for validation. The models were trained using technical indicators and closing price data, and their performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results show that both models achieved high forecasting accuracy, with RMSE values below 40, demonstrating strong capability in capturing nonlinear market dynamics. Although the LSTM-only model achieved slightly lower numerical error values, the Hybrid CNN-LSTM produced smoother and more stable predictions with better resistance to market noise and short-term volatility. The residual distribution and convergence patterns confirmed that the hybrid model achieved balanced performance and effective generalization. These findings indicate that the Hybrid CNN-LSTM framework provides a reliable approach for stock market forecasting, offering both precision and stability. The study concludes that this hybrid deep learning approach holds significant potential for supporting digital financial analytics and long-term investment decision-making.