BSc IT Project Guide: Option Pricing Prediction using Machine Learning
1. Project Overview
The Option Pricing Prediction project aims to develop a machine learning model to predict the price of financial stock options. This can assist traders and investors in making informed decisions by estimating option premiums based on market data. The project leverages supervised learning techniques and regression models to forecast option values using factors such as stock price, strike price, volatility, interest rate, and time to maturity.
2. Objectives
- Understand the Black-Scholes model and other pricing theories.
- Collect and preprocess financial data relevant to option pricing.
- Implement and train regression-based ML models for price prediction.
- Evaluate the accuracy and performance of different models.
- Build a user interface for model interaction (optional).
3. Tools and Technologies
- Python, Jupyter Notebook
- Pandas, NumPy, Scikit-learn, XGBoost
- Matplotlib, Seaborn
- Financial data APIs (e.g., Yahoo Finance, Alpha Vantage)
4. System Architecture
1. Data Collection: Pull option data from financial APIs.
2. Data Preprocessing: Clean and normalize data.
3. Feature Engineering: Create features like implied volatility, time to
maturity, etc.
4. Model Training: Use regression models such as Random Forest, XGBoost, and
Linear Regression.
5. Evaluation: Assess models using metrics like RMSE and MAE.
6. Deployment: (Optional) Interface using Flask or Streamlit.
5. Implementation Steps
1. Research and gather relevant financial theories.
2. Set up development environment and dependencies.
3. Fetch and preprocess option data.
4. Train ML models using historical data.
5. Evaluate models and tune hyperparameters.
6. Create visualizations and optional GUI.
7. Prepare documentation and project report.
6. Challenges
- Data availability and quality.
- Handling non-linear and non-stationary behavior of financial data.
- Overfitting and generalization of ML models.
- Interpretation of results for financial decision-making.
7. Conclusion
This project combines financial theory with modern machine learning techniques to develop a practical tool for option pricing prediction. It allows students to explore real-world finance problems and apply AI techniques for predictive analytics.