Quantitative Analysis and Prediction of FAANG Stock Performance

Project Overview

This project conducts a detailed quantitative analysis of the FAANG group of stocks, which includes Facebook, Apple, Amazon, Netflix, and Google. Using Python and libraries such as pandas and yfinance, this analysis dives into stock performance metrics including price changes, moving averages, daily return averages, and risk assessments over the past year. The project also explores the correlation between daily returns of these stocks and attempts to predict future stock behavior.

Objectives

• Price Change Analysis: Explore how the stock prices of FAANG companies have changed over time.
• Moving Averages: Calculate and analyze moving averages to identify trends.
• Daily Return Averages: Determine the average daily returns for each stock.
• Correlation Analysis: Investigate the correlation between daily returns of FAANG stocks.
• Risk Assessment: Assess the risk involved in investing in each of the FAANG stocks.
• Future Predictions: Attempt to predict future stock behavior using the gathered data.

Methodology

1. Data Collection: Utilize yfinance to access historical stock data of FAANG companies.
2. Data Analysis: Perform analysis using pandas to understand stock performance, risk, and potential return metrics.
3. Visualization: Use matplotlib and seaborn for visualizing stock trends, correlations, and other key metrics.
4. Risk and Return Analysis: Assess and compare the risk and return profiles of each FAANG stock.
5. Predictive Analysis: Implement basic forecasting techniques to predict future stock performance.

Key Findings

• The document includes detailed observations on stock price trends, with Netflix shown as an example where the stock showed significant upward movement after an initial volatile period.
• Moving averages indicated clear trends, with the 50-day moving average highlighting periods of growth for Netflix.
• Correlation analysis revealed the interrelation between daily returns of FAANG stocks, offering insights into how these stocks move in relation to each other.

How to Run

Ensure you have Python installed along with pandas, yfinance, matplotlib, and seaborn. Clone the project and navigate to the project directory. Run the notebook using Jupyter Lab or Notebook to see the analysis and visualizations.

Requirements

Python 3.x pandas yfinance matplotlib seaborn