Smapf-hnna: a novel Stock Market Analysis and Prediction Framework using Hybrid Neural Network Architectures Across Major U.S. Indices
摘要
Financial markets exhibit high volatility due to various external factors, making stock price prediction a complex yet crucial task for investors and financial institutions. Accurate forecasting not only enhances decision making but also mitigates financial risks. This paper introduces SMAPF-HNNA, an advanced framework that integrates multiple neural network (NN) architectures for robust time-series analysis and stock price forecasting. The proposed approach leverages Convolutional Neural Networks (CNNs) for automatic feature extraction, followed by the application of diverse NN models, including Simple Recurrent Neural Networks (SRNNs), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Units (GRU), Bidirectional GRU (BiGRU), and Multilayer Perceptron (MLP) for precise stock price prediction. The framework is rigorously evaluated on multiple benchmark datasets, including NYSE, S&P 500, NASDAQ, and SSE, through extensive training and testing phases. Experimental results demonstrate that the hybrid CNN-MLP model outperforms other architectures across all datasets, achieving exceptionally low error rates with five key regression metrics. The model yields mean squared error (MSE) values between 0.000031 and 0.000004, root mean squared error (RMSE) between 0.0020 and 0.0056, mean absolute error (MAE) between 0.0018 and 0.0042, mean absolute percentage error (MAPE) between 0.12% and 0.32%, and R-squared (R