Developing an AI-Driven Predictive Model for Stock Market Forecasting in the Banking Sector

This study develops an AI-driven predictive model for stock market forecasting in the banking sector, using LSTM, Random Forest, and Linear Regression. Historical stock prices, macroeconomic indicators, and banking sector metrics were analyzed, with data preprocessing techniques applied to enhance accuracy. Model performance was evaluated using MAE, RMSE, and R², with LSTM achieving the best results (R² = 0.92). Findings suggest AI models can improve investment decisions, trading strategies, and risk management. Future research should explore real-time data integration, sentiment analysis, and hybrid AI models for enhanced forecasting accuracy.