Lecture: Fundamental Analysis of Stocks with Python

Introduction

• Purpose: Learn how to use Python for fundamental analysis of stocks
• Key Takeaways:
  • Retrieve financial information for any stock (e.g., dividend yield, market cap, P/E ratio)
  • Compare stocks based on fundamentals
  • Example: Finding the highest dividend yield from an index

Libraries Used

• yfinance - For getting fundamental information
• pandas - For data handling
• matplotlib - For visualizing results

Retrieving Stock Information

1. Creating a Ticker Object
   • Use yf.Ticker('AAPL') to create a ticker object for Apple
2. Extracting Information
   • Use ticker.info to get a dictionary with detailed information
   • Key data points include sector, business summary, 200-day moving average, beta, trailing P/E ratio, market cap, forward P/E ratio, dividend yield, etc.

Peer Group Comparison

1. Define Tickers
   • Example: tickers = ['AAPL', 'MSFT', 'IBM']
2. Loop Through Tickers
   • Create an empty list to gather info: info_list = []
   • Loop: for i in tickers: info_list.append(yf.Ticker(i).info)
3. Store Efficiently
   • Store results in a SQL database for improved efficiency if needed

Defining Fundamentals

1. Create Fundamentals List
   • Examples: fundamentals = ['dividendYield', 'marketCap', 'beta', 'forwardPE']
2. Create DataFrame
   • Convert list of dictionaries into DataFrame for better overview: df = pd.DataFrame(info_list)
3. Set Index
   • Use symbol as index: df.set_index('symbol', inplace=True)
4. Filter for Fundamentals
   • Use boolean indexing to filter DataFrame: df_filtered = df[fundamentals]

Visualization

1. Bar Plot for Dividend Yield
   • Example: Use plt.bar for visual comparison
   • Customize colors and labels for readability
2. Other Comparisons
   • Similar logic for comparing metrics like P/E ratio

Practical Application: Dow Jones Analysis

1. Retrieve Dow Jones Ticker Symbols
   • Scrape from Wikipedia: df_tickers = pd.read_html(url)[1]
   • Extract symbols list: tickers = df_tickers['Symbol'].tolist()
2. Gather Fundamental Data
   • Loop through tickers and gather info in a list
3. Create DataFrame and Set Index
   • Convert list to DataFrame: df_dow = pd.DataFrame(info_list)
   • Set index to symbol: df_dow.set_index('symbol', inplace=True)
4. Filter and Visualize
   • Filter for specific fundamental data, e.g., dividend yield
   • Visualize using bar plots

Conclusion

• Perform personalized analysis by selecting key fundamentals and visualizing results
• Option to use subplots for comprehensive comparisons
• Encouragement to explore and customize techniques for better investment decisions

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