Stock Market Comparison Analysis using Python

Stock Market Comparison Analysis is the process of evaluating and comparing the performance of multiple stocks or financial instruments within the stock market. It aims to provide insights into how different stocks have performed relative to each other and the broader market indices. So, if you want to learn how to compare the stock prices of different companies, this article is for you. In this article, I’ll take you through the task of Stock Market Comparison Analysis using Python.

Stock Market Comparison Analysis: Process We Can Follow

Stock Market Comparison Analysis is a methodical examination of multiple stocks or financial assets within the stock market. It involves analyzing the performance of various stocks or assets to gain insights into how they have fared relative to each other and the broader market. It helps investors, financial analysts, and decision-makers make informed investment decisions.

Below is the process we can follow for the task of Stock Market Performance Analysis as a Data Science professional:

1. Gather historical stock price data for the companies or financial instruments you want to compare.
2. Clean and preprocess the collected data.
3. Create relevant features or variables that can help in the comparison. 
4. Ensure that the data for different stocks is aligned in terms of time.
5. Use data visualization techniques to present the comparison effectively.
6. Apply statistical methods to identify patterns or relationships between the stocks being compared.
7. Compare the performance of the selected stocks or financial instruments against relevant benchmarks, such as market indices like the S&P 500.

I hope you now understand what Stock Market Comparison Analysis is. In the section below, I will take you through the task of Stock Market Comparison Analysis by comparing the stock prices of Apple and Google using Python.

Stock Market Comparison Analysis using Python

I will start the task of Stock Market Comparison Analysis by collecting the stock price data of Apple and Google for the last quarter. To collect the data, I’ll be using the Yahoo Finance API. You can install the API by executing the pip command mentioned below on your terminal or command prompt:

• pip install yfinance

Now, let’s import the necessary Python libraries to get started with the task:

import pandas as pd
import yfinance as yf
import plotly.io as pio
import plotly.graph_objects as go
pio.templates.default = "plotly_white"

Now, let’s collect stock price data:

# Define the tickers for Apple and Google
apple_ticker = 'AAPL'
google_ticker = 'GOOGL'

# Define the date range for the last quarter
start_date = '2023-07-01'
end_date = '2023-09-30'

# Fetch historical stock price data using yfinance
apple_data = yf.download(apple_ticker, start=start_date, end=end_date)
google_data = yf.download(google_ticker, start=start_date, end=end_date)

The above code sets up the analysis by defining the companies and date range of interest, and it fetches the historical stock price data for Apple and Google within that period. This data will serve as the basis for various financial analyses and comparisons, such as calculating returns, volatility, and other metrics to assess the performance and risk associated with these stocks.

Now let’s calculate the daily returns of Apple and Google in the stock market:

# Calculate daily returns
apple_data['Daily_Return'] = apple_data['Adj Close'].pct_change()
google_data['Daily_Return'] = google_data['Adj Close'].pct_change()

Now, let’s visualize the daily returns:

# Create a figure to visualize the daily returns
fig = go.Figure()

fig.add_trace(go.Scatter(x=apple_data.index, y=apple_data['Daily_Return'],
                         mode='lines', name='Apple', line=dict(color='blue')))
fig.add_trace(go.Scatter(x=google_data.index, y=google_data['Daily_Return'],
                         mode='lines', name='Google', line=dict(color='green')))

fig.update_layout(title='Daily Returns for Apple and Google (Last Quarter)',
                  xaxis_title='Date', yaxis_title='Daily Return',
                  legend=dict(x=0.02, y=0.95))

fig.show()

Now let’s analyze the cumulative returns of Apple and Google for the last quarter:

# Calculate cumulative returns for the last quarter
apple_cumulative_return = (1 + apple_data['Daily_Return']).cumprod() - 1
google_cumulative_return = (1 + google_data['Daily_Return']).cumprod() - 1

# Create a figure to visualize the cumulative returns
fig = go.Figure()

fig.add_trace(go.Scatter(x=apple_cumulative_return.index, y=apple_cumulative_return,
                         mode='lines', name='Apple', line=dict(color='blue')))
fig.add_trace(go.Scatter(x=google_cumulative_return.index, y=google_cumulative_return,
                         mode='lines', name='Google', line=dict(color='green')))

fig.update_layout(title='Cumulative Returns for Apple and Google (Last Quarter)',
                  xaxis_title='Date', yaxis_title='Cumulative Return',
                  legend=dict(x=0.02, y=0.95))

fig.show()

Here, we first calculated the cumulative returns for both Apple and Google over the last quarter. Cumulative returns represent the total percentage change in the stock’s value over a given period, considering the compounding effect of daily returns. Then, we compared the investment performance of Apple and Google during the specified period, showing which stock had higher or lower cumulative returns over that time frame. We can see that Google has higher cumulative returns compared to Apple over the last quarter.

Now, let’s analyze the volatility of Apple and Google:

# Calculate historical volatility (standard deviation of daily returns)
apple_volatility = apple_data['Daily_Return'].std()
google_volatility = google_data['Daily_Return'].std()

# Create a figure to compare volatility
fig1 = go.Figure()
fig1.add_bar(x=['Apple', 'Google'], y=[apple_volatility, google_volatility],
             text=[f'{apple_volatility:.4f}', f'{google_volatility:.4f}'],
             textposition='auto', marker=dict(color=['blue', 'green']))

fig1.update_layout(title='Volatility Comparison (Last Quarter)',
                   xaxis_title='Stock', yaxis_title='Volatility (Standard Deviation)',
                   bargap=0.5)
fig1.show()

We first calculated the historical volatility for both Apple and Google stocks. Volatility is a measure of how much the stock’s price fluctuates over time. In this case, we are calculating the standard deviation of daily returns to measure the volatility. Then we visualized the calculated volatility to assess and compare the volatility or risk associated with both Apple and Google stocks during the specified period. We can see that Google’s volatility is higher than Apple’s.

It indicates that Google’s stock price experienced larger price fluctuations or greater price variability over the last quarter. Here’s what this difference in volatility may indicate:

1. Google’s stock is considered riskier compared to Apple. Investors generally associate higher volatility with higher risk because it implies that the stock price can change significantly in a short period.
2. Google’s stock may be more sensitive to market conditions, economic factors, or company-specific news and events. This heightened sensitivity can result in larger price swings.
3. Traders and investors with a higher risk tolerance might find Google’s stock appealing if they are looking for opportunities to profit from short-term price movements.

Now let’s compare the stock market of Google and Apple according to the stock market benchmark:

market_data = yf.download('^GSPC', start=start_date, end=end_date)  # S&P 500 index as the market benchmark

# Calculate daily returns for both stocks and the market
apple_data['Daily_Return'] = apple_data['Adj Close'].pct_change()
google_data['Daily_Return'] = google_data['Adj Close'].pct_change()
market_data['Daily_Return'] = market_data['Adj Close'].pct_change()

# Calculate Beta for Apple and Google
cov_apple = apple_data['Daily_Return'].cov(market_data['Daily_Return'])
var_market = market_data['Daily_Return'].var()

beta_apple = cov_apple / var_market

cov_google = google_data['Daily_Return'].cov(market_data['Daily_Return'])
beta_google = cov_google / var_market

# Compare Beta values
if beta_apple > beta_google:
    conclusion = "Apple is more volatile (higher Beta) compared to Google."
else:
    conclusion = "Google is more volatile (higher Beta) compared to Apple."

# Print the conclusion
print("Beta for Apple:", beta_apple)
print("Beta for Google:", beta_google)
print(conclusion)

Beta for Apple: 1.2257444782547282
Beta for Google: 1.53030690283569
Google is more volatile (higher Beta) compared to Apple.

In the above code, we are assessing how sensitive Apple and Google stocks are to overall market movements, providing insights into their relative volatility and risk about the broader U.S. stock market represented by the S&P 500 index.

The Standard & Poor’s 500, often referred to as the S&P 500, is a widely recognized stock market index in the United States. The S&P 500 index includes 500 of the largest publicly traded companies in the United States, chosen for their market capitalization, liquidity, and industry representation. These companies span various sectors of the U.S. economy and provide a comprehensive view of the health and performance of the stock market.

In the above output, the beta value for Apple is approximately 1.2257. This beta value suggests that Apple’s stock is estimated to be approximately 22.57% more volatile or sensitive to market movements (as represented by the S&P 500 index) compared to the overall market. The beta value for Google is approximately 1.5303. This beta value suggests that Google’s stock is estimated to be approximately 53.03% more volatile or sensitive to market movements.

A beta greater than 1 suggests that a stock tends to be more volatile than the market. In this case, both Apple and Google have beta values greater than 1, indicating that they are expected to be more volatile and sensitive to market movements. Google’s higher beta value (1.5303) compared to Apple’s (1.2257) suggests that Google’s stock is estimated to have a higher degree of market sensitivity or risk compared to Apple. Investors should consider this information when making investment decisions, as higher-beta stocks can provide greater potential for returns but also carry a higher level of risk.

Summary

So this is how you can perform Stock Market Comparison Analysis using Python. Stock Market Comparison Analysis is a methodical examination of multiple stocks or financial assets within the stock market. It involves analyzing the performance of various stocks or assets to gain insights into how they have fared relative to each other and the broader market. It helps investors, financial analysts, and decision-makers make informed investment decisions. I hope you liked this article on Stock Market Comparison Analysis using Python. Feel free to ask valuable questions in the comments section below.