From Data To Decision: Empowering Companies and Investors With Hybrid AI Stock Prediction Method

This research presents a hybrid Artificial Intelligence (AI) model for stock price prediction, combining several advanced techniques to enhance the accuracy and reliability of financial forecasting. The model integrates neural network methods such artificial Neural Networks (ANN), in conjunction with a sliding window approach and hierarchical clustering. The sliding window method segments historical stock data into fixed intervals, enabling the model to detect localized temporal trends, while hierarchical clustering groups similar historical patterns to improve forecasting relevance. A comprehensive literature review was conducted to evaluate existing hybrid AI approaches and identify research gaps. Feature selection was performed using stepwise regression and leverage analysis to refine the dataset before model training. The hybrid model demonstrated superior performance compared to traditional methods, based on evaluation metrics such as RMSE and MAE, both in backtesting and real-time simulation scenarios. The results confirm the model’s ability to generate timely and accurate predictions, supporting more informed investment decisions. This study also recommends future enhancements such as sentiment analysis integration, broader market validation, and real-time deployment capabilities, affirming the strong potential of hybrid AI models in financial forecasting.