Stock Market Prediction: XGBoost and LSTM Comparative Analysis

This paper is a comprehensive comparative analysis of two major machine learning models, XGBoost and Long and Short Term Memory (LSTM), for stock price forecasting using historical market data Subtle strengths and weaknesses a within the model remains to be clarified Taking advantage of different approaches ranging from hybrid algorithms to mixed models, we examine their performance on a complete dataset of US-based stock About ETFsThe XGBoost model is optimized using GridSearchCV, performing hyperparameter optimization, while the LSTM model uses a sequential structure with multiple layers and dropout regularization Evaluation metrics such as mean squared error (MSE) and mean absolute error (MAE) are provided quantitative measure of predictive accuracy. The simulation of forecasted and actual prices greatly enhances comparative analysis.Our findings highlight the complementary strengths of XGBoost in tabular data processing and LSTM in time-dependent capture. The analysis provides insight into the definition, computational effort, and overall predictive performance of each model. Exploring a hybrid approach combining the strengths of both models opens avenues for future research.