Analysis of the Principle and Implementations for Predicting Stock Prices Using AI Models

Contemporarily, AI techniques have been widely implemented into quantitative finance fields. forecasting is intrinsically difficult due to the volatility of financial data. This study investigates the application of advanced artificial intelligence models to predict stock prices, focusing on Long Short-Term Memory networks, Transformer architectures, and Extreme Gradient Boosting. Stock market This study uses historical data from significant benchmarks such as the SandP 500 index, Facebook, and JPMorgan to analyze and compare the performance of various models. LSTM networks are assessed for their ability to capture temporal dependencies and handle sequential data. The Transformer model is evaluated for its ability to manage nonsequential data and improve forecasting accuracy. XGBoost is examined for its robust performance and efficiency in boosting predictive accuracy through iterative improvements. The comparative analysis of these models highlights their respective strengths and limitations, providing valuable insights for investors and financial analysts. These results underscore the importance of model selection in financial forecasting and offers recommendations for future research to advance AI-driven market prediction methodologies.