Gradient Boosting Decision Tree with LSTM for Investment Prediction

A hybrid ensemble framework combining LSTM networks with LightGBM and CatBoost significantly enhances stock price prediction accuracy by 10-15% over individual models, effectively capturing market dynamics. The approach integrates multiple machine learning techniques and benchmarks performance across various metrics, demonstrating the potential of ensemble methods for financial forecasting.