Enhancing Stock Market Prediction: A Robust LSTM-DNN Model Analysis on 26 Real-Life Datasets - Khorshed Alam; Mahbubul Haq Bhuiyan; Injamam Ul Haque; Md Fahad Monir; Tarem Ahmed (IEEE Access, 2024)
Predicting the closing price of the stock market with accuracy is highly uncertain and volatile. Deep learning (DL) can analyze vast amounts of historical stock data to identify patterns and correlations, aiding in predictive modeling. By learning from past market behavior, deep learning algorithms can potentially forecast future price movements with some degree of accuracy. This research presents a hybrid Long Short-Term Memory (LSTM) and Deep Neural Network (DNN) model designed to tackle the complexities of stock market prediction, including market volatility and intricate patterns. Initially the proposed model is being trained on Bajaj’s stock dataset. Getting noteworthy performance in single dataset does not prove robustness of the model. The research validates the model’s robustness and scalability through rigorous comparative analysis on 26 company’s stock datasets, achieving an average R-squared (R2) score of 0.98606, a Mean Absolute Error (MAE) of 0.0210, and a Mean Squared Error (MSE) of 0.00111. To assess the contribution of our proposed model, we retrained previously used deep learning models alongside our new approach, utilizing a shared dataset for validation. Additionally, we provide ablation study of LSTM-DNN model which provides insights into the individual contributions of each component towards detecting closing price of stocks, offering valuable information for optimizing future stock market prediction models. The model’s exceptional performance sets a new standard in stock market prediction, offering promising implications for investors, traders, and financial analysts. Making our work available open-source on https://github.com/codewithkhurshed/SMP-IUB can enhance its accessibility and promote future research opportunities in stock market price prediction.