Real-Time Adaptive Multi-Modal Stock Prediction with Temporal Graph Attention and Dynamic Interaction Networks

Stock price prediction is a complex task due to the nature of non-linearity and interaction of many different factors: investor sentiment, macroeconomic trends, and events happening in real-time. Most traditional models that assume linearity and static data often fail to respond to the dynamic conditions prevailing in the markets, especially in the face of extreme volatility or other high-impact events. AMSPF successfully overcomes those challenges by infusing innovative methodologies such as actionable insights extraction based on volume-weighted sentiment, volatility-sensitive reweighting mechanism to adapt and adjust the dynamics of feature importance, and employing event-triggered attention mechanisms, which enhances response to market shifting developments. Combining momentum analysis with volatility sensitivity, its engine ranks and selects high-returnpotential stocks to support actual real-time trading strategies. Experimental results have shown that AMSPF performs well, as it has an improvement of 15% in directional accuracy and a Sharpe ratio of 2.89, which serves as a new benchmark for the predictive performance. This comprehensive framework offers robust and adaptable solutions for accurate stock price forecasting and actionable investment decision-making.