Hybrid AI Models for Real-Time Stock Management and Market Price Prediction

Efficient stock management and accurate market price prediction are essential in dynamic and volatile business environments, where traditional forecasting and inventory control methods often lack adaptability and real-time responsiveness. This paper proposes a hybrid artificial intelligence (AI) framework for real-time stock management and market price prediction, integrating machine learning and deep learning models to capture both linear trends and nonlinear market patterns. The system processes real-time transactional data, historical stock prices, and relevant market indicators to continuously update inventory levels and forecast future price movements. Feature engineering, data normalization, and model fusion techniques are employed to enhance prediction accuracy and robustness. A decision-support module utilizes predicted demand and price trends to optimize inventory replenishment, reduce stockouts, and minimize overstocking costs. Experimental evaluation using real-world market datasets demonstrates that the proposed hybrid model outperforms individual predictive approaches in terms of forecasting accuracy, adaptability, and inventory efficiency, as reflected by improved MAE and RMSE values. The results confirm the effectiveness of the proposed approach as a scalable and intelligent solution for real-time stock management and market price prediction applications. Experimental results show that the proposed RT-HAF model reduces RMSE by approximately 23% compared to LSTM and improves inventory service level by over 10%.