Hybrid Deep Learning and BPNN Models for Stock Price Forecasting: A Quality and Performance Perspective

Stock price forecasting has long been a challenging task due to the complex, nonlinear, and highly volatile nature of financial markets. Traditional statistical approaches and standalone machine learning models often fail to capture intricate temporal dependencies and dynamic patterns present in stock data. To address these challenges, this study proposes a hybrid forecasting framework that integrates deep learning architectures with Backpropagation Neural Networks (BPNN), combining the feature extraction and sequence modeling strengths of deep networks with the adaptive learning capabilities of BPNN. The hybrid model is designed to enhance both the quality of predictions—measured in terms of accuracy, robustness, and consistency—and the performance efficiency, with emphasis on reduced error rates and improved convergence speed. Empirical evaluation is carried out on benchmark stock market datasets, where the proposed model outperforms conventional deep learning and standalone BPNN methods in terms of metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared values. The findings highlight that hybridization not only strengthens predictive precision but also offers resilience against market noise and volatility, making it a promising tool for investors, analysts, and decision-makers. This research contributes to the growing body of literature on intelligent financial forecasting by demonstrating how hybrid deep learning–BPNN models can deliver a balanced perspective on quality and performance in stock price prediction.