How to Visualize the Impact of Corporate Restructuring on Stock Prices Using EDA

To visualize the impact of corporate restructuring on stock prices using Exploratory Data Analysis (EDA), we can break down the process into several key steps. Below is a detailed methodology for how to perform this analysis, visualize the results, and interpret the findings.

1. Collect and Prepare the Data

Before starting with the visualization and analysis, it’s essential to gather the necessary data. The key datasets you will need include:

• Stock Price Data: Historical stock prices of the company that underwent restructuring. This can be sourced from platforms like Yahoo Finance, Alpha Vantage, or Quandl.

• Restructuring Event Data: Details about the corporate restructuring event(s), such as mergers, acquisitions, layoffs, or spin-offs. This information can be obtained from corporate filings, news sources, or financial reports.

The stock price data should ideally contain the following columns:

• Date

• Closing Price

• Volume

• Adjusted Closing Price (if available, for stock splits and dividends)

You will need to merge the stock price data with the restructuring event data, ensuring both datasets align based on date.

2. Preprocess the Data

The data needs to be cleaned and transformed to ensure it is in a usable format for analysis:

• Handle Missing Data: Fill or remove missing values in stock prices or restructuring event data.

• Date Parsing: Ensure all date columns are in datetime format to facilitate time-based analysis.

• Event Identification: Mark the dates on which restructuring events occurred.

For example, you could create a column indicating the restructuring event date and categorize the type of event (e.g., acquisition, layoffs, merger).

3. Visualizing Stock Prices Pre- and Post-Restructuring

To visualize the impact of restructuring, the focus should be on how stock prices behave before and after the event. Below are the types of plots that are helpful:

a. Stock Price Trend Before and After Restructuring

A time series plot is the most straightforward way to visualize stock prices over time:

• Plot the Stock Price: Create a line chart with the stock’s closing price over time.

• Highlight the Restructuring Event: Add a vertical line or shaded region to indicate when the corporate restructuring took place.

The following libraries in Python can be used for this purpose:

• matplotlib for basic plotting

• seaborn for improved aesthetics

• plotly for interactive plots

python
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import matplotlib.pyplot as plt
import pandas as pd

# Example data
data = pd.read_csv('stock_data.csv')  # Stock price data with date and closing price

# Convert the date column to datetime format
data['Date'] = pd.to_datetime(data['Date'])

# Define the restructuring date
restructuring_date = pd.to_datetime('YYYY-MM-DD')

# Plot the stock price trend
plt.figure(figsize=(10, 6))
plt.plot(data['Date'], data['Close'], label='Stock Price')
plt.axvline(restructuring_date, color='r', linestyle='--', label='Restructuring Event')
plt.xlabel('Date')
plt.ylabel('Stock Price')
plt.title('Stock Price Trend Before and After Restructuring')
plt.legend()
plt.show()

This plot will help you observe any immediate or delayed reactions to the restructuring event.

b. Stock Price Returns Analysis

It’s crucial to look at stock price returns, as they can reveal changes in market sentiment more clearly than absolute prices.

You can calculate the daily returns as:

$$text{Daily Return} = frac{text{Closing Price}_{t} – text{Closing Price}_{t-1}}{text{Closing Price}_{t-1}}$$

Once you have daily returns, plot the returns for a window before and after the restructuring event.

python
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data['Daily_Return'] = data['Close'].pct_change()

# Plotting returns
plt.figure(figsize=(10, 6))
plt.plot(data['Date'], data['Daily_Return'], label='Daily Returns')
plt.axvline(restructuring_date, color='r', linestyle='--', label='Restructuring Event')
plt.xlabel('Date')
plt.ylabel('Daily Returns')
plt.title('Stock Price Returns Before and After Restructuring')
plt.legend()
plt.show()

c. Cumulative Returns Analysis

To get a clearer picture of the overall change in value, plotting cumulative returns is useful. This can help visualize the total percentage change in stock prices relative to a starting point.

python
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data['Cumulative_Returns'] = (1 + data['Daily_Return']).cumprod() - 1

plt.figure(figsize=(10, 6))
plt.plot(data['Date'], data['Cumulative_Returns'], label='Cumulative Returns')
plt.axvline(restructuring_date, color='r', linestyle='--', label='Restructuring Event')
plt.xlabel('Date')
plt.ylabel('Cumulative Returns')
plt.title('Cumulative Stock Price Returns Before and After Restructuring')
plt.legend()
plt.show()

4. Event Study Analysis

An event study involves analyzing the abnormal returns around the restructuring event. To do this, you would need a benchmark (such as a market index) to compare the stock’s performance against.

a. Calculate Abnormal Returns (AR)

Abnormal returns are the difference between the actual return of the stock and the expected return based on a market model or benchmark.

1. Calculate the expected return based on a market index (like the S&P 500 or a sector index).

2. Subtract the expected return from the actual stock return to get the abnormal return.

python
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# Assuming you have a benchmark return (e.g., market index returns)
# Abnormal Return = Actual Stock Return - Expected Return (Market Return)

data['Abnormal_Return'] = data['Daily_Return'] - benchmark_returns  # Calculate this based on your benchmark

b. Plot Abnormal Returns Around Restructuring Event

A common practice is to plot the abnormal returns in a window before and after the restructuring event (e.g., 20 days before and 20 days after).

python
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# Plotting abnormal returns around restructuring
plt.figure(figsize=(10, 6))
plt.plot(data['Date'], data['Abnormal_Return'], label='Abnormal Returns')
plt.axvline(restructuring_date, color='r', linestyle='--', label='Restructuring Event')
plt.xlabel('Date')
plt.ylabel('Abnormal Returns')
plt.title('Abnormal Returns Around Corporate Restructuring')
plt.legend()
plt.show()

5. Statistical Analysis of Stock Price Movements

To quantify the impact of the restructuring event, you can conduct statistical tests like t-tests or regression analysis.

a. T-Test to Compare Pre- and Post-Restructuring Periods

You can perform a t-test to compare the mean stock returns before and after the restructuring event. This will help determine if there is a statistically significant difference in stock prices around the event.

python
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from scipy import stats

# Split data into pre- and post-restructuring periods
pre_event = data[data['Date'] < restructuring_date]['Daily_Return']
post_event = data[data['Date'] >= restructuring_date]['Daily_Return']

# Perform t-test
t_stat, p_value = stats.ttest_ind(pre_event, post_event)
print(f"T-statistic: {t_stat}, P-value: {p_value}")

A p-value below 0.05 would indicate a statistically significant difference in stock returns before and after the event.

6. Volatility Analysis

You may also want to analyze the volatility of the stock price before and after the restructuring. A significant change in volatility can indicate that the market is reacting strongly to the restructuring.

a. Calculate Rolling Volatility

A rolling standard deviation over a 30-day window can be used to assess the volatility of stock returns.

python
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data['Rolling_Volatility'] = data['Daily_Return'].rolling(window=30).std()

plt.figure(figsize=(10, 6))
plt.plot(data['Date'], data['Rolling_Volatility'], label='Rolling Volatility')
plt.axvline(restructuring_date, color='r', linestyle='--', label='Restructuring Event')
plt.xlabel('Date')
plt.ylabel('Rolling Volatility')
plt.title('Stock Price Volatility Before and After Restructuring')
plt.legend()
plt.show()

Conclusion

Visualizing the impact of corporate restructuring on stock prices using EDA involves a combination of time series plots, returns analysis, and abnormal returns analysis. By visualizing stock prices before and after the event, as well as analyzing returns, abnormal returns, and volatility, you can gain insights into how the market reacts to corporate restructuring. Statistical tests, such as t-tests, can further substantiate whether the changes in stock prices are statistically significant.