Week 9: Unsupervised Learning and Clustering in Finance
Learning Objectives:
- Understand the fundamentals of unsupervised learning and its applications in finance
- Master clustering techniques, particularly K-means clustering
- Apply clustering algorithms to financial data analysis
- Interpret and evaluate clustering results in financial contexts
Core Concepts:
1. Unsupervised Learning Fundamentals
- Understanding Unsupervised Learning
- Key Financial Applications:
- Anomaly Detection (fraud, risk management, financial crime)
- Market Segmentation
- Portfolio Diversification
- Investment Signal Identification
2. Clustering in Financial Markets
Practical Resources:
Code Implementations
- S&P 500 Clustering Analysis
- Comprehensive implementation for market analysis
- Focus on financial data preprocessing
- K-means Stock Classification
- Step-by-step K-means implementation
- Feature engineering for financial data
- Advanced Company Clustering (R Implementation)
- Translation from R to Python
Assignment:
Financial Market Clustering Analysis
Project Requirements:
- Data Collection and Preparation
- Collect S&P 500 company data (price, volume, financial ratios)
- Clean and preprocess the data appropriately
- Engineer relevant features for clustering
- Clustering Analysis
- Implement K-means clustering
- Determine optimal number of clusters
- Analyze cluster characteristics
- Results Interpretation
- Compare clusters with traditional sector classifications
- Identify potential investment opportunities
- Document anomalies and insights
Deliverables:
- Code Implementation:
- Data collection and preprocessing
- Clustering analysis implementation
- Results visualization and analysis
- Clear documentation and comments
- Required dependencies listed
- Brief presentation:
- Overview of methodology
- Key findings and insights
- Investment implications
Submission:
- Submit code and presentation through Blackboard
- Include instructions for running your code
- Code Explanation and Insights:
- Explain how your code works
- Walk through your code and explain key decisions
- Include any insights learned from the project
- Document any challenges encountered and how they were overcome