A Hybrid Artificial Intelligence Model For Stock Market Prediction Using Financial News, Social Media Sentiment, And Corporate Performance Indicators
Abstract
Predicting stock market movements remains a challenging task due to the complex interactions among financial indicators, corporate performance, investor sentiment, and rapidly evolving digital information. Traditional forecasting methods that primarily rely on historical price trends or technical indicators often fail to capture the significant influence of qualitative factors such as financial news and social media discussions. The model integrates unstructured textual information from financial news articles and social media platforms with structured corporate performance metrics to develop a comprehensive prediction model for stock price movements. Advanced Natural Language Processing (NLP) techniques are employed for text preprocessing and sentiment extraction, while transformer-based language representations enhance contextual understanding of financial content. The extracted sentiment features are fused with company performance indicators, including earnings reports and financial disclosures, to create a multimodal feature space. A hybrid predictive architecture combining Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) models is utilized to capture linear, nonlinear, and temporal relationships within the integrated dataset. Model reliability is evaluated using cross-validation along with performance metrics such as Accuracy, Precision, Recall, F1-score, and Root Mean Square Error (RMSE).
Experimental results demonstrate the effectiveness of the proposed model, achieving an overall classification accuracy of 86.7%, a macro F1-score of 0.84, and a strong correlation coefficient of 0.89 between predicted and actual stock price variations. The findings further reveal that social media sentiment exerts a stronger short-term influence on stock prices than positive financial news, while corporate performance indicators provide greater long-term market stability, highlighting the value of integrating heterogeneous information sources for AI-driven financial forecasting.
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