This comprehensive research delves into the burgeoning field of stock market forecasting, emphasizing the use of advanced artificial intelligence (AI) and machine learning (ML) technologies. The primary objective is to develop a robust model capable of predicting short-term stock market movements for major US-listed companies across various sectors. The predictive algorithm relies heavily on historical price data, technical indicators, and sentiment analysis derived from news sources to generate directional forecasts.

This study investigates several critical components of stock market analysis, including pattern recognition, risk assessment, and the use of machine learning algorithms to predict investment returns. A thorough examination of the Efficient Market Hypothesis (EMH) is conducted to understand its implications on forecasting stock prices using historical data. Additionally, the research evaluates a range of approaches and models pertinent to financial prediction. These include the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, the AutoRegressive Integrated Moving Average (ARIMA) model, and Long Short-Term Memory (LSTM) networks.

Furthermore, this study addresses the inherent data limitations, the risks of overfitting, and the ethical considerations associated with the application of AI and ML in stock market forecasting. By examining these factors, this research aims to highlight the potential and challenges of employing technology-driven methods in financial markets. The ultimate goal is to enhance the accuracy and reliability of stock market predictions, thereby providing valuable insights for investors and stakeholders. Through this rigorous exploration, this study contributes to the ongoing development of more sophisticated and effective forecasting models in the financial industry.