Author: Emmanuel Ocran
This project analyzes seven years of historical stock data (June 2018 – May 2025) to evaluate sector-level volatility, returns, and risk-adjusted performance. Using key financial metrics like daily returns, rolling volatility, and Sharpe Ratios, the analysis highlights how major sectors perform under different market conditions.
- Clean and unify daily stock price data across sectors
- Engineer features: daily returns, rolling volatility, Sharpe Ratio
- Compare sector risk and performance over time
- Identify the most resilient sectors during downturns
- Provide insights to support informed investment decisions
| Sector | Tickers |
|---|---|
| Technology | AAPL, MSFT, NVDA, AMD |
| Energy | XOM, CVX, COP, HAL |
| Healthcare | JNJ, PFE, UNH, MRK |
| Financials | JPM, BAC, WFC, GS |
| Consumer Discretionary | AMZN, TSLA, HD, MCD |
| Utilities | NEE, DUK, SO, AEP |
- Python (Pandas, NumPy, Seaborn, Matplotlib)
- Jupyter Notebook
- yfinance (data source)
- Technology sector delivered the highest risk-adjusted return (Sharpe Ratio)
- Energy sector exhibited the highest volatility with the lowest efficiency
- Sector returns showed moderate positive correlations, limiting diversification
- Technology and Consumer Discretionary recovered fastest post-COVID-19 downturn
- Clone the repo and install required libraries
- Run the Jupyter notebook to reproduce the analysis
- Explore the saved figures or review the report