How the Silicon Valley sneaker that was once worth $4 billion ended up selling for $39M

Learn how to analyze company financial performance and valuation trends using Python and financial data APIs, with practical examples that apply to companies like Allbirds.

Introduction

In this tutorial, we'll explore how to analyze and track the financial performance of tech companies like Allbirds using Python and financial data APIs. We'll build a practical tool that can fetch stock data, analyze trends, and visualize company performance metrics. This tutorial will teach you how to work with financial data programmatically, which is essential for understanding the business dynamics behind companies that experience dramatic valuation changes.

Prerequisites

Basic Python programming knowledge
Understanding of financial concepts like stock prices, market capitalization, and valuation
Python libraries: yfinance, pandas, matplotlib, and requests
Basic understanding of APIs and data fetching

Step-by-Step Instructions

1. Install Required Libraries

First, we need to install the necessary Python libraries for financial data analysis:

pip install yfinance pandas matplotlib requests

This command installs the libraries we'll need to fetch stock data, manipulate it with pandas, and create visualizations.

2. Import Libraries and Set Up Data Fetching

Now we'll create our main script and import the required modules:

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Set up the stock ticker for Allbirds (if we had one)
# For this example, we'll use a similar sneaker brand for demonstration
symbol = "BIDU"  # Example: Baidu, but we'll use a sneaker brand

We're importing the necessary libraries and setting up our ticker symbol. In a real scenario, we'd use the actual ticker for Allbirds, but we'll demonstrate the approach with a similar tech company.

3. Fetch Historical Stock Data

Let's fetch the historical stock data for our chosen company:

# Fetch stock data for the past 5 years
stock_data = yf.download(symbol, start="2019-01-01", end="2024-01-01")

# Display basic information about the data
print(stock_data.head())
print(f"\nData shape: {stock_data.shape}")

This step retrieves historical price data, which is crucial for understanding the company's market performance over time. The yfinance library makes it easy to pull financial data directly from Yahoo Finance.

4. Analyze Company Valuation Metrics

Next, we'll extract key financial metrics to understand the company's valuation:

# Get company information
company_info = yf.Ticker(symbol).info

# Extract key metrics
market_cap = company_info.get('marketCap', 'N/A')
price = company_info.get('currentPrice', 'N/A')
volume = company_info.get('volume', 'N/A')

print(f"Market Cap: {market_cap:,}")
print(f"Current Price: ${price}")
print(f"Volume: {volume:,}")

This step is crucial for understanding how companies are valued in the market. Market capitalization tells us the total value of the company, which is essential when analyzing companies that experience dramatic valuation changes.

5. Create Price Trend Analysis

Let's create a visualization to understand the price trends:

# Create a simple price chart
plt.figure(figsize=(12, 6))
plt.plot(stock_data.index, stock_data['Close'], label='Closing Price')
plt.title(f'{symbol} Stock Price Trend')
plt.xlabel('Date')
plt.ylabel('Price ($)')
plt.legend()
plt.grid(True)
plt.show()

This visualization helps us see how stock prices have changed over time, which is essential for understanding the market dynamics that led to the valuation changes mentioned in the Allbirds story.

6. Calculate Key Performance Indicators

Let's calculate some important financial indicators:

# Calculate moving averages
stock_data['MA_20'] = stock_data['Close'].rolling(window=20).mean()
stock_data['MA_50'] = stock_data['Close'].rolling(window=50).mean()

# Calculate daily returns
stock_data['Daily_Return'] = stock_data['Close'].pct_change()

# Calculate volatility (standard deviation of returns)
volatility = stock_data['Daily_Return'].std() * np.sqrt(252)  # Annualized

print(f"Annualized Volatility: {volatility:.2%}")

Moving averages help identify trends, while volatility measures the risk associated with price movements. These metrics are crucial for understanding the market behavior of companies experiencing dramatic changes in valuation.

7. Compare with Industry Benchmarks

Let's compare our company's performance with industry peers:

# Define a list of similar companies for comparison
competitors = ['BIDU', 'AAPL', 'MSFT']  # Baidu, Apple, Microsoft

# Fetch data for all competitors
competitor_data = yf.download(competitors, start="2019-01-01", end="2024-01-01")

# Calculate cumulative returns for comparison
cumulative_returns = (competitor_data['Close'] / competitor_data['Close'].iloc[0]) - 1

# Plot comparison
plt.figure(figsize=(12, 6))
for company in competitors:
    plt.plot(cumulative_returns.index, cumulative_returns[company], label=company)

plt.title('Cumulative Returns Comparison')
plt.xlabel('Date')
plt.ylabel('Cumulative Return')
plt.legend()
plt.grid(True)
plt.show()

This comparison helps us understand how the company performed relative to its industry peers, which is essential for understanding market positioning and valuation dynamics.

8. Create a Summary Report

Finally, let's create a summary report of our findings:

# Create a summary report
summary = {
    'Company': symbol,
    'Current_Price': price,
    'Market_Cap': market_cap,
    'Volume': volume,
    'Annualized_Volatility': f'{volatility:.2%}',
    '20-Day_MA': stock_data['MA_20'].iloc[-1],
    '50-Day_MA': stock_data['MA_50'].iloc[-1]
}

print("\nFinancial Summary Report:")
for key, value in summary.items():
    print(f"{key}: {value}")

This summary consolidates our analysis into a readable format, making it easy to understand the key metrics that drive company valuation and performance.

Summary

In this tutorial, we've built a practical financial analysis tool that can help track company performance and valuation trends. By using Python libraries like yfinance, pandas, and matplotlib, we've created a framework that can analyze stock data, calculate key financial indicators, and visualize trends. This approach is directly applicable to understanding companies like Allbirds that experienced dramatic valuation changes. The skills learned here can be extended to monitor any company's financial health and market performance, making it a valuable tool for investors and analysts.

Remember that while this tutorial provides a foundation for financial analysis, actual investment decisions should always be made with professional advice and thorough research.