An AI-Powered Approach for Accurate Stock Price Movement Prediction

Predicting how stock prices will change has always been a tough problem in financial analysis. This is because financial markets are unpredictable, messy, and full of complex patterns. Old methods in statistics often can't keep up with these challenges. This study introduces a new model that uses AI to combine different tools for better stock price predictions. The model uses technical indicators, deep learning, and analysis of market feelings from news. It mixes Convolutional Neural Networks (CNN) to find important features, Long Short-Term Memory (LSTM) networks to understand time-based patterns, and a Transformer-based system called BERT to analyze news sentiment. The model is trained and tested using past stock prices and sentiment data from various global markets. The results show that this new model works better than old methods in predicting price changes, direction, and stability. The study shows that using both price data and market sentiment leads to more accurate predictions, offering a useful tool for investors and automated trading systems.