Quant Portfolio Risk & Return Analysis

Overview

This project analyzes the risk and return characteristics of a portfolio of major technology stocks using quantitative finance techniques.

The analysis includes:

• Historical stock return analysis
• Volatility measurement
• Sharpe ratio evaluation
• Correlation analysis
• Portfolio risk-return simulation
• Efficient frontier visualization

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Stocks Analyzed

• Apple (AAPL)
• Amazon (AMZN)
• Google (GOOG)
• Microsoft (MSFT)

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Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• yFinance

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Key Quantitative Concepts

Daily Returns

Computed using percentage price changes.

Volatility

Annualized standard deviation of returns used as a measure of portfolio risk.

Correlation Matrix

Used to analyze relationships between stock returns and diversification effects.

Sharpe Ratio

Measures risk-adjusted return of the portfolio.

Monte Carlo Portfolio Simulation

Generated 5000 random portfolios to analyze the return-risk tradeoff.

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Key Findings

• GOOG achieved the highest cumulative growth and strongest Sharpe ratio.
• AMZN showed the highest volatility among the selected stocks.
• AAPL and MSFT demonstrated the strongest correlation.
• Diversification benefits were limited due to positive correlations among technology stocks.

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Visualizations

Correlation Heatmap

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Efficient Frontier Simulation

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Portfolio Metrics

Metric	Value
Expected Portfolio Return	25.6%
Portfolio Volatility	27.3%
Portfolio Sharpe Ratio	0.94

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Future Improvements

• Minimum variance optimization
• Maximum Sharpe portfolio selection
• CAPM beta analysis
• Value at Risk (VaR)
• Black-Scholes option pricing

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How to Run

Install dependencies:

pip install -r requirements.txt