Stock Performance Analysis with Sharpe Ratios

Overview

This project evaluates the performance of stocks using the Sharpe Ratio, a key metric for assessing risk-adjusted returns. By analyzing the stocks of Facebook and Amazon, and comparing them against the S&P 500 benchmark, this project demonstrates how to make informed investment decisions based on historical data.

The analysis includes:

• Data preprocessing and cleaning
• Exploratory data analysis (EDA)
• Visualization of stock prices and benchmark trends
• Computation of Sharpe Ratios to assess risk-adjusted returns

About the Sharpe Ratio

The Sharpe Ratio, introduced by Nobel laureate William Sharpe, measures the additional return per unit of risk for an investment compared to a benchmark. It is calculated as:

$$S = \frac{R_p - R_f}{\sigma}$$

Where:

• $R_p$: Return of the portfolio or investment
• $R_f$: Risk-free rate of return
• $\sigma$: Standard deviation of excess returns (a measure of risk)

A higher Sharpe Ratio indicates better risk-adjusted performance.

Data

The dataset includes:

• Daily stock prices for Amazon and Facebook
• Daily prices for the S&P 500 index as the benchmark

Tools and Libraries

The analysis uses the following Python libraries:

• pandas: For data manipulation and analysis
• numpy: For numerical computations
• matplotlib: For data visualization

Key Insights

1. Visualization of historical stock prices reveals trends and volatility patterns for Amazon, Facebook, and the S&P 500.
2. Sharpe Ratios provide a quantitative comparison of risk-adjusted returns for the stocks relative to the benchmark.

Usage

To explore the analysis:

1. Clone this repository.
2. Install the required libraries:

   pip install pandas numpy matplotlib

3. Run the Jupyter Notebook to see the results step-by-step.

Acknowledgments

Special thanks to William Sharpe for his contributions to financial theory and risk management. """