Neural Networks Stock Market Price Forecasting Model: Integrating Economic Indicators and Investment Technical Analysis Toward Advanced Financial Analysis

Stock market volatility presents challenges for investors aiming to balance risk and return. Traditional stock prediction methods, often based on statistical models, struggle to handle the complexity of influencing factors. This study proposes an advanced stock forecasting system using machine learning, integrating deep ensemble learning models with investment technical analysis tools, such as exponential moving average (EMA), and economic indicators from sources, such as Yahoo Finance and FRED. The model used the LSTM neural networks method with cross-validation and practical testing to ensure accuracy. The study found significant improvement using the LSTM method over traditional methods by offering a valuable tool for informed investment decisions and advancing financial analytics.