Euronext Brussels(BEL20) INDEX analysis in python

Euronext Brussels(BEL20) INDEX is a European index. 

The BEL 20 Index is a key stock market index representing the performance of the 20 largest and most liquid companies listed on the Euronext Brussels stock exchange. It serves as a benchmark for the Belgian stock market and provides insights into the performance of major Belgian corporations. Here’s a detailed overview:

Index Overview

• Name: BEL 20 Index
• Type: Stock Market Index
• Country: Belgium
• Exchange: Euronext Brussels
• Established: 1991

Constituents

• Top Companies: The BEL 20 Index comprises 20 of the largest and most actively traded companies on the Euronext Brussels stock exchange. The constituents are selected based on their market capitalization, liquidity, and overall impact on the Belgian economy.
• Sector Representation: The index includes companies from various sectors, such as finance, pharmaceuticals, consumer goods, and industrials. The sector representation can vary based on market conditions and changes in the economy.

Calculation Method

• Free-Float Market Capitalization-Weighted: The BEL 20 is calculated based on free-float market capitalization. This means the index weights companies according to the value of shares available for public trading, excluding shares held by insiders and strategic investors.
• Index Rebalancing: The index is reviewed quarterly to ensure it remains representative of the Belgian market. Adjustments are made based on changes in market capitalization, liquidity, and other factors.

Purpose and Use

• Benchmark: The BEL 20 serves as a key benchmark for the Belgian stock market, providing insights into the performance of Belgium’s largest and most influential companies.
• Investment: It is widely used by investors, fund managers, and analysts to track market performance and make investment decisions. Financial products such as ETFs (Exchange-Traded Funds) and mutual funds are often designed to track or benchmark against the BEL 20.

Historical Performance

• 1991: The BEL 20 Index was launched on January 2, 1991, with a base value of 1,000 points.
• Growth: Since its inception, the index has experienced various market trends and cycles, reflecting Belgium’s economic conditions and the performance of major Belgian companies.

Significance

• Economic Indicator: The BEL 20 provides a snapshot of the performance of Belgium’s largest companies and reflects broader economic trends in Belgium.
• Global Influence: Although primarily focused on the Belgian market, many companies in the BEL 20 have international operations, which can affect the index’s performance and make it relevant to global investors.

Recent Developments

• Market Trends: The BEL 20 Index has been influenced by domestic economic policies, global economic conditions, and changes in the sectoral composition of the index.
• Sector Shifts: The index’s composition can change based on shifts in market dynamics, including increasing representation from technology or healthcare sectors.

Investment Products

• ETFs and Mutual Funds: Several ETFs and mutual funds are available that track the BEL 20 Index, providing investors with exposure to the performance of the index.
• Derivatives: Futures and options contracts based on the BEL 20 are available for trading, offering tools for hedging and speculation.

Notable Companies

• Leading Firms: Some of the prominent companies included in the BEL 20 Index are:
• Anheuser-Busch InBev: A major global brewing company.
• KBC Group: A leading financial services provider in Belgium.
• Solvay: A multinational chemical and advanced materials company.
• UCB: A global biopharmaceutical company specializing in immunology and neurology.

Analyzing the Euronext Brussels BEL20 Index using Python involves collecting historical price data, performing data analysis, and creating visualizations to gain insights into the performance of the Belgian stock market. Here's a step-by-step guide on how to conduct BEL20 Index analysis in Python:

Import Libraries:

Start by importing the necessary Python libraries for data manipulation, analysis, and visualization. Commonly used libraries include pandas, numpy, matplotlib, and yfinance to fetch historical data:

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import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import yfinance as yf

Data Retrieval:

Use the yfinance library or other financial data sources to fetch historical data for the BEL20 Index. Specify the start and end dates for the data you want to analyze:

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bel20 = yf.download('^BFX', start='2020-01-01', end='2021-12-31')

Data Exploration:

Explore the fetched data to understand its structure and contents. Use functions like head(), tail(), describe(), and info() to inspect the dataset:

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print(bel20.head())

Data Visualization:

Create visualizations to analyze the historical performance of the BEL20 Index. Common visualizations include line charts to visualize index price movements:

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plt.figure(figsize=(12, 6))

plt.plot(bel20['Adj Close'], label='BEL20')

plt.title('BEL20 Index Price')

plt.xlabel('Date')

plt.ylabel('Price')

plt.legend()

plt.show()

Technical Analysis (Optional):

Perform technical analysis by calculating and visualizing technical indicators like moving averages, relative strength index (RSI), and MACD. Libraries like ta-lib can be used for these calculations.

Statistical Analysis (Optional):

Conduct statistical analysis to calculate summary statistics, volatility measures, and correlations with other assets. numpy and pandas are useful for these calculations.

Sentiment Analysis (Optional):

Consider incorporating sentiment analysis of news articles or social media data related to the Belgian stock market to understand market sentiment's impact on the BEL20 Index.

Fundamental Analysis (Optional):

Analyze fundamental factors affecting the Belgian economy, such as GDP growth, inflation rates, and interest rates, which can influence the BEL20 Index's performance.

Prediction and Forecasting (Optional):

You can use time series forecasting techniques like ARIMA or machine learning models to make predictions about future BEL20 Index movements.

Risk Management and Decision Making:

Based on your analysis, formulate investment strategies, set risk management parameters, and make informed investment decisions regarding the BEL20 Index or Belgian stocks.

Regular Updates:

Keep your analysis up to date with the latest data to adapt to changing market conditions and make timely decisions.

Remember that investing in stock markets carries risks, and it's crucial to do thorough research, consider economic and geopolitical factors, and potentially consult with financial experts before making investment decisions based on your analysis of the BEL20 Index or any other stock index.

df = yf.download('^BFX',

start='1985-01-01',

end='2021-08-13',

progress=False)

df = df.loc[:, ['Adj Close']]

df.rename(columns={'Adj Close':'adj_close'}, inplace=True)

df[['simple_rtn','log_rtn']].tail(20)

Log return of BEL20

def realized_volatility(x):

 return np.sqrt(np.sum(x**2))

df_rv = df.groupby(pd.Grouper(freq='M')).apply(realized_volatility)

df_rv.rename(columns={'log_rtn': 'rv'}, inplace=True)

df_rv.rv = df_rv.rv * np.sqrt(12)

fig, ax = plt.subplots(2, 1, sharex=True)

ax[0].plot(df)

ax[1].plot(df_rv)

[<matplotlib.lines.Line2D at 0x7f2509bbc510>,
 <matplotlib.lines.Line2D at 0x7f2509c550d0>,
 <matplotlib.lines.Line2D at 0x7f2509c552d0>]

fig, ax = plt.subplots(3, 1, figsize=(24, 20), sharex=True)

df.adj_close.plot(ax=ax[0])

ax[0].set(title = 'BFX time series',

ylabel = 'Stock price ($)')

df.simple_rtn.plot(ax=ax[1])

ax[1].set(ylabel = 'Simple returns (%)')

df.log_rtn.plot(ax=ax[2])

ax[2].set(xlabel = 'Date',

ylabel = 'Log returns (%)')

[Text(0, 0.5, 'Log returns (%)'), Text(0.5, 0, 'Date')]

import cufflinks as cf

from plotly.offline import iplot, init_notebook_mode

init_notebook_mode()

df_rolling = df[['simple_rtn']].rolling(window=21) \

.agg(['mean', 'std'])

df_rolling.columns = df_rolling.columns.droplevel()

df_outliers = df.join(df_rolling)

def indentify_outliers(row, n_sigmas=3):

   x = row['simple_rtn']

   mu = row['mean']

   sigma = row['std']

   if (x > mu + 3 * sigma) | (x < mu - 3 * sigma):

    return 1

   else:

    return 0

df_outliers['outlier'] = df_outliers.apply(indentify_outliers,

axis=1)

outliers = df_outliers.loc[df_outliers['outlier'] == 1,

['simple_rtn']]

fig, ax = plt.subplots()

ax.plot(df_outliers.index, df_outliers.simple_rtn,

color='blue', label='Normal')

ax.scatter(outliers.index, outliers.simple_rtn,

color='red', label='Anomaly')

ax.set_title("BFX returns")

ax.legend(loc='lower right')

<matplotlib.legend.Legend at 0x7f2505f4a690>

r_range = np.linspace(min(df.log_rtn), max(df.log_rtn), num=1000)

mu = df.log_rtn.mean()

sigma = df.log_rtn.std()

norm_pdf = scs.norm.pdf(r_range, loc=mu, scale=sigma)

fig, ax = plt.subplots(1, 2, figsize=(16, 8))

# histogram

sns.distplot(df.log_rtn, kde=False, norm_hist=True, ax=ax[0])

ax[0].set_title('Distribution of BFX returns', fontsize=16)

ax[0].plot(r_range, norm_pdf, 'g', lw=2,

label=f'N({mu:.2f}, {sigma**2:.4f})')

ax[0].legend(loc='upper left');

Distribution of BEL 20

df.log_rtn.plot(title='Daily BFX returns')

<matplotlib.axes._subplots.AxesSubplot at 0x7f2505ec8a90>

df = yf.download(['^BFX', '^VIX'],

start='1985-01-01',

end='2021-08-13',

progress=False)

df.tail()

Data of BEL 20 AND VIX

df = df[['Adj Close']]

df.tail()

Adj close of BEL20 and VIX

df.columns = df.columns.droplevel(0)

df = df.rename(columns={'^BFX': 'bfx', '^VIX': 'vix'})

df['log_rtn'] = np.log(df.bfx / df.bfx.shift(1))

df['vol_rtn'] = np.log(df.vix / df.vix.shift(1))

df.dropna(how='any', axis=0, inplace=True)

corr_coeff = df.log_rtn.corr(df.vol_rtn)

ax = sns.regplot(x='log_rtn', y='vol_rtn', data=df,

line_kws={'color': 'red'})

ax.set(title=f'BFX vs. VIX ($\\rho$ = {corr_coeff:.2f})',

ylabel='VIX log returns',

xlabel='BFX log returns')

[Text(0, 0.5, 'VIX log returns'),
 Text(0.5, 0, 'BFX log returns'),
 Text(0.5, 1.0, 'BFX vs. VIX ($\\rho$ = -0.40)')]

More New BEL20