Gradient Boosting Decision Tree with LSTM for Investment Prediction

This paper proposes a hybrid modeling framework that synergistically integrates LSTM (Long Short-Term Memory) networks with LightGBM and CatBoost for stock price prediction. We systematically preprocess time-series financial data and conduct comprehensive evaluations using seven classical models: Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Bidirectional LSTM (BiLSTM), vanilla LSTM, XGBoost, LightGBM, and standard Neural Networks (NNs). Through rigorous comparison of performance metrics, including MAE, R2, MSE, and RMSE, we establish baseline references across multiple temporal scales. Building on these empirical benchmarks, we introduce a novel ensemble architecture that harmonizes the complementary strengths of sequential and tree-based models. Experimental results demonstrate that our integrated framework achieves 10-15% improvement in predictive accuracy compared to individual constituent models, particularly in reducing error volatility during market regime transitions. These findings validate the underexplored potential of heterogeneous ensemble strategies in financial forecasting and establish methodological foundations for developing adaptive prediction systems in non-stationary market environments. The proposed architecture's modular design enables seamless integration of emerging machine learning components, suggesting promising directions for future research in computational finance.