Multimodal Deep Learning Ensemble Framework for Accurate Stock Market Prediction Using Multisource Data - Lavanya M.; P. Gnanasekeran (International Journal of Computational and Experimental Science and Engineering, 2025)
Stock market forecasting presents substantial challenges due to the inherent volatility of financial data, impacted by a number of variables, including as investor sentiment and economic indices. This study proposes an advanced hybrid ensemble framework, MDSFE (Multimodal Deep Stock Forecasting Ensemble), which integrates multiple deep learning architectures such as LSTM, VMD-BiLSTM-AM, and RoBERTa-TextCNN. Utilizing a multimodal data assimilation strategy, MDSFE leverages historical stock prices, real-time financial news, social media sentiment, and economic indicators. Benchmarked against traditional models like ARIMA and standalone LSTM models, MDSFE demonstrates superior predictive accuracy, achieving an R² value of 0.97 and a MAPE of 0.80%. Trained on a robust dataset comprising 10,000 instances collected from 2003 to 2024, MDSFE highlights its practical applicability in real-world scenarios, offering enhanced decision-making capabilities for investors and analysts.