Switzerland(SMI) stock market

What is the name of Switzerland's (SMI) stock market?

Switzerland’s Swiss Market Index (SMI) is a key benchmark for the Swiss stock market, representing the performance of the largest and most liquid companies listed on the SIX Swiss Exchange. Here’s a detailed overview:

Index Overview

• Name: Swiss Market Index (SMI)
• Type: Stock Market Index
• Country: Switzerland
• Exchange: SIX Swiss Exchange
• Established: 1988

Constituents

• Top Companies: The SMI includes the 20 largest and most liquid Swiss companies based on market capitalization and liquidity. These companies are leaders in their respective sectors and are influential in the Swiss economy.
• Sector Representation: The index has a strong representation from sectors such as healthcare, financial services, consumer goods, and pharmaceuticals.

Calculation Method

• Free-Float Market Capitalization-Weighted: The SMI is calculated based on free-float market capitalization, meaning that the index weights companies according to the value of the 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 Swiss market. Adjustments are made based on changes in market capitalization, liquidity, and other factors.

Purpose and Use

• Benchmark: The SMI serves as the primary benchmark for the Swiss stock market, providing insights into the performance of Switzerland’s largest and most influential companies.
• Investment: It is widely used by investors, fund managers, and analysts to track market performance. Various financial products, including ETFs (Exchange-Traded Funds) and mutual funds, are designed to track or benchmark against the SMI.

Historical Performance

• 1988: The SMI was introduced on June 30, 1988, with a base value of 1,500 points.
• Growth: Over the years, the index has reflected Switzerland’s economic growth and stability, showcasing the performance of major Swiss corporations.

Significance

• Economic Indicator: The SMI is a key indicator of the health of the Swiss stock market and economy, reflecting the performance of the largest Swiss companies and broader economic trends.
• Global Influence: Many SMI companies have significant international operations and play a major role in global markets, making the index relevant to global investors.

Recent Developments

• Economic Factors: The SMI has been influenced by various economic factors, including Swiss monetary policy, global economic conditions, and shifts in market sentiment.
• Sector Dynamics: The index’s composition has evolved over time, with significant representation from sectors like pharmaceuticals and financial services.

Investment Products

• ETFs and Mutual Funds: Numerous ETFs and mutual funds are designed to track the SMI, offering investors exposure to the performance of the index.
• Derivatives: Futures and options based on the SMI are available for trading, providing tools for hedging and speculative strategies.

Notable Companies

• Leading Firms: Some of the prominent companies included in the SMI are:
• Nestlé S.A.: A leading global food and beverage company.
• Roche Holding AG: A major pharmaceutical and diagnostics company.
• Novartis AG: A global healthcare company specializing in pharmaceuticals and diagnostics.
• UBS Group AG: A major global financial services company.
• Credit Suisse Group AG: Another significant player in the global banking and financial services sector.

The Swiss Market Index (SMI), often referred to as the SMI, is the most prominent stock market index in Switzerland. It is the benchmark index for the Swiss equity market, representing the performance of the largest and most liquid publicly traded companies listed on the SIX Swiss Exchange, which is the principal stock exchange in Switzerland. 

Some key details about the Swiss Market Index (SMI):

Composition: The SMI is composed of the 20 largest and most actively traded companies listed on the SIX Swiss Exchange. These companies come from various sectors, including finance, pharmaceuticals, healthcare, technology, and consumer goods. The composition of the index is reviewed and adjusted regularly to ensure it reflects the current market conditions.

Market Capitalization Weighted: The SMI is a market capitalization-weighted index. This means that the weight of each component stock in the index is determined by its market capitalization, which is the product of its stock price and the number of outstanding shares. Larger companies have a more significant impact on the index's performance.

Diversification: The SMI offers diversification across different sectors of the Swiss economy, reducing concentration risk. Key industries represented in the index include banking and financial services (e.g., Credit Suisse, UBS), pharmaceuticals (e.g., Roche, Novartis), food and beverages (e.g., Nestlé), and industrial companies.

Global Significance: While the SMI primarily reflects the performance of Swiss-listed companies, it is closely monitored by investors and financial professionals worldwide. Switzerland's financial market stability, strong economy, and the global reach of Swiss corporations contribute to the index's international significance.

How to get Switzerland(SMI) stock market data?

you get data from Yahoo Finance, Quanda, google, etc.

How to install data in Python?

Here install python lib.

import yfinance as yf

import numpy as np

import pandas as pd

import seaborn as sns

import scipy.stats as scs

import statsmodels.api as sm

import statsmodels.tsa.api as smt

import matplotlib.pyplot as plt

Some Python code data collected from Yahoo Finance

df = yf.download('^SSMI',

start='1985-01-01',

end='2021-07-07',

progress=False)

How to get individual column SMI adj price?

df = df.loc[:, ['Adj Close']]

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

Simple return and Log return SSMI

df['simple_rtn'] = df.adj_close.pct_change()

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

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

Simple and log return

How to get realized volatility SMI in Python?

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)

Plot Realised volatility SMI

[<matplotlib.lines.Line2D at 0x7f8af4356f50>,
 <matplotlib.lines.Line2D at 0x7f8af4366090>,
 <matplotlib.lines.Line2D at 0x7f8af4366250>]

Plot time series

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

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

ax[0].set(title = 'SSMI 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 (%)')

Plot time series in Python

Get mean and standard deviation

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

How do plot Outliers?

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("SSMI returns")

ax.legend(loc='lower right')

<matplotlib.legend.Legend at 0x7f8aef0d5690>

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 SSMI 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');

Plot normal probability distribution function

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

<matplotlib.axes._subplots.AxesSubplot at 0x7f8aeeea6810>

How to compare SSMI and VIX?

df = yf.download(['^SSMI', '^VIX'],

start='1985-01-01',

end='2021-07-07',

progress=False)

Data ssmi and vix

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

line_kws={'color': 'red'})

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

ylabel='VIX log returns',

xlabel='SSMI log returns')

[Text(0, 0.5, 'VIX log returns'),
 Text(0.5, 0, 'SSMI log returns'),
 Text(0.5, 1.0, 'SSMI vs. VIX ($\\rho$ = -0.38)')]

Click Here

NEW SSMI DATA ANALYSIS