Analyzed Tesla and GameStop stock prices and revenue using Python, yfinance, BeautifulSoup, Pandas, and Matplotlib.
This project analyzes the relationship between stock prices and company revenue for Tesla and GameStop.
The analysis includes:
- Extracting stock data using yfinance
- Web scraping revenue data using BeautifulSoup
- Data cleaning using Pandas
- Data visualization using Matplotlib
- Long-term trend analysis using a 30-Day Moving Average
- Python
- Pandas
- yfinance
- BeautifulSoup
- Matplotlib
- Jupyter Notebook
- Tesla demonstrated a long-term upward trend in both revenue and stock price, indicating strong business growth and increasing investor confidence.
- GameStop's stock price experienced significant volatility despite limited revenue growth, suggesting that market sentiment played a major role in its stock performance.
- The 30-Day Moving Average effectively reduced short-term price fluctuations, making it easier to identify long-term market trends for both companies.
- Comparing stock prices with company revenue highlights that while financial performance can influence stock prices, market sentiment and external factors also have a significant impact.
This project demonstrates how Python can be used to collect, clean, visualize, and analyze financial data to support data-driven decision-making.