Samsung’s founding family doubled its wealth in a year. Its workers want a share.

Learn to analyze Samsung's financial performance and wealth growth using Python, with real stock data analysis and visualizations.

Introduction

In the wake of Samsung's dramatic wealth increase driven by AI advancements, this tutorial will teach you how to analyze and visualize the financial data of major corporations using Python. You'll learn to fetch real stock data, perform basic financial analysis, and create visualizations that reveal trends in corporate wealth growth. This is particularly relevant given the rapid wealth accumulation seen in tech dynasties like the Lees of Samsung.

Prerequisites

Basic Python programming knowledge
Installed Python 3.7+ with pip
Required libraries: yfinance, pandas, matplotlib, seaborn
Understanding of basic financial concepts (stock prices, market capitalization)

Step-by-Step Instructions

1. Install Required Libraries

Before we begin, we need to install the necessary Python libraries for financial data analysis and visualization.

pip install yfinance pandas matplotlib seaborn

Why this step: yfinance provides access to financial data from Yahoo Finance, pandas handles data manipulation, and matplotlib/seaborn create visualizations to analyze trends.

2. Import Libraries and Set Up Data Collection

First, we'll import the required libraries and set up our environment to fetch financial data.

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Set style for better-looking plots
sns.set_style("whitegrid")
plt.rcParams['figure.figsize'] = (12, 8)

Why this step: We're preparing our workspace with all necessary tools to fetch, manipulate, and visualize financial data.

3. Fetch Samsung Stock Data

Now we'll download Samsung's stock data to analyze its performance over time.

# Download Samsung stock data
samsung = yf.Ticker("005930.KS")  # Samsung Electronics (Korean Stock Exchange)

# Get historical data for the past 5 years
samsung_data = samsung.history(period="5y")

# Display first few rows to verify data
print(samsung_data.head())

Why this step: This retrieves the actual stock price data that we'll use to understand Samsung's financial performance, which directly correlates with the wealth increase of the Lee family.

4. Calculate Key Financial Metrics

We'll compute some important metrics to understand Samsung's growth trajectory.

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

# Calculate 30-day rolling average of closing prices
samsung_data['30-Day_MA'] = samsung_data['Close'].rolling(window=30).mean()

# Calculate cumulative returns
samsung_data['Cumulative_Return'] = (1 + samsung_data['Daily_Return']).cumprod()

# Display results
print(samsung_data[['Close', '30-Day_MA', 'Cumulative_Return']].tail())

Why this step: These metrics help us understand Samsung's performance trends, volatility, and overall growth pattern, which are crucial for analyzing wealth accumulation.

5. Create Visualizations

Visualizing the data helps us understand the patterns in Samsung's growth.

# Create a comprehensive plot
fig, axes = plt.subplots(2, 2, figsize=(15, 10))

# Plot 1: Stock price over time
axes[0, 0].plot(samsung_data.index, samsung_data['Close'], label='Closing Price')
axes[0, 0].plot(samsung_data.index, samsung_data['30-Day_MA'], label='30-Day Moving Average')
axes[0, 0].set_title('Samsung Stock Price Trend')
axes[0, 0].set_ylabel('Price (KRW)')
axes[0, 0].legend()

# Plot 2: Daily returns distribution
axes[0, 1].hist(samsung_data['Daily_Return'].dropna(), bins=50, alpha=0.7)
axes[0, 1].set_title('Distribution of Daily Returns')
axes[0, 1].set_xlabel('Daily Return')

# Plot 3: Cumulative returns
axes[1, 0].plot(samsung_data.index, samsung_data['Cumulative_Return'])
axes[1, 0].set_title('Cumulative Returns Over Time')
axes[1, 0].set_ylabel('Cumulative Return')

# Plot 4: Volume over time
axes[1, 1].bar(samsung_data.index, samsung_data['Volume'], alpha=0.7)
axes[1, 1].set_title('Trading Volume Over Time')
axes[1, 1].set_ylabel('Volume')

plt.tight_layout()
plt.show()

Why this step: Visualizations help us quickly identify trends, volatility patterns, and growth trajectories that would be difficult to discern from raw numbers alone.

6. Compare with Other Tech Giants

To better understand Samsung's position in the tech industry, let's compare it with other major tech companies.

# Define multiple tickers for comparison
companies = ['005930.KS', 'AAPL', 'MSFT', 'GOOGL']
company_names = ['Samsung', 'Apple', 'Microsoft', 'Google']

# Create a DataFrame to store all data
all_data = pd.DataFrame()

for i, ticker in enumerate(companies):
    company_data = yf.Ticker(ticker).history(period="5y")
    company_data['Company'] = company_names[i]
    all_data = pd.concat([all_data, company_data])

# Reset index and calculate cumulative returns for all companies
all_data = all_data.reset_index()
all_data['Cumulative_Return'] = all_data.groupby('Company')['Close'].pct_change().add(1).groupby(all_data['Company']).cumprod()

# Create comparison plot
plt.figure(figsize=(12, 8))
for company in company_names:
    company_df = all_data[all_data['Company'] == company]
    plt.plot(company_df['Date'], company_df['Cumulative_Return'], label=company, linewidth=2)

plt.title('Cumulative Returns Comparison: Samsung vs. Tech Giants')
plt.xlabel('Date')
plt.ylabel('Cumulative Return')
plt.legend()
plt.grid(True)
plt.show()

Why this step: Comparing Samsung with other tech giants provides context for understanding how its wealth growth compares to industry leaders, highlighting the significance of its recent performance.

7. Analyze Wealth Impact Factors

Let's examine some key factors that contribute to wealth growth in tech companies like Samsung.

# Calculate key performance indicators
samsung_metrics = pd.DataFrame({
    'Company': ['Samsung'],
    'Market_Cap': [samsung.info['marketCap']],
    'P/E_Ratio': [samsung.info['trailingPE']],
    'Dividend_Yield': [samsung.info['dividendYield']],
    '52_Week_High': [samsung.info['fiftyTwoWeekHigh']],
    '52_Week_Low': [samsung.info['fiftyTwoWeekLow']],
    'Volume': [samsung.info['averageVolume']],
    'Avg_Volume_10d': [samsung.info['averageVolume10d']],
    'Beta': [samsung.info['beta']],
    'Revenue': [samsung.info['totalRevenue']],
    'Net_Income': [samsung.info['netIncomeToCommon']]  
})

print("Samsung Financial Metrics:")
print(samsung_metrics)

# Calculate growth rate
current_price = samsung_data['Close'].iloc[-1]
previous_price = samsung_data['Close'].iloc[-252]  # About 1 year ago
annual_growth = (current_price - previous_price) / previous_price * 100

print(f"\nAnnual Growth Rate (1 year): {annual_growth:.2f}%")

Why this step: Understanding these metrics helps us appreciate the factors contributing to wealth accumulation, such as market capitalization, revenue growth, and price performance.

Summary

This tutorial demonstrated how to analyze the financial performance of Samsung, a company whose wealth has rapidly increased due to AI-driven growth. By using Python libraries like yfinance, pandas, and matplotlib, we were able to fetch real stock data, calculate financial metrics, and create visualizations that reveal trends in corporate wealth growth.

The techniques learned here are directly applicable to analyzing any major corporation's performance, particularly relevant given the rapid wealth accumulation seen in tech dynasties like the Lees of Samsung. Understanding these patterns helps investors and analysts make informed decisions about tech investments and wealth distribution in the modern economy.