Stock Market Prediction

This project focused on developing an advanced stock market prediction system using machine learning techniques to forecast stock prices for companies in the S&P500.

Key Achievements

Recurrent Neural Network Implementation

Constructed a sophisticated Recurrent Neural Network (RNN) using TensorFlow to predict stock prices of companies in the S&P500. The neural network architecture was specifically designed to capture temporal dependencies in stock price movements.

Technical Indicator Integration

Incorporated various technical indicators into the prediction model and identified the most influential indicators using a best subset selection algorithm. This approach allowed for more accurate predictions by focusing on the most relevant market signals.

Company-Specific Models

Optimized the prediction approach by creating individual models for each company, tailoring the analysis to the specific characteristics and patterns of each stock. This personalized approach resulted in more accurate predictions compared to a one-size-fits-all model.

Market-Beating Performance

Developed a holistic portfolio strategy based on the prediction models that successfully beat the S&P500 benchmark 70% of the time, demonstrating the effectiveness of the machine learning approach in real-world financial applications.

Technologies Used

• TensorFlow
• Recurrent Neural Networks (RNN)
• Time Series Analysis
• Technical Indicators
• Best Subset Selection Algorithm
• Portfolio Optimization