Motherson Sumi Systems Stock price target by 2023

Motherson Sumi System Price Analysis 

Motherson Sumi Systems Limited

Company Overview

Name

Business Segments

Automotive Wiring Harnesses

Global Presence

Geographical Reach

Financial Performance

Revenue

Key Achievements

Industry Recognition

Strategic Initiatives

Expansion

Stock Market

Listing

Leadership

Management

Corporate Social Responsibility (CSR)

Community Engagement

Recent Developments

• Acquisitions and Investments: MSSL has made strategic acquisitions to expand its product offerings and enhance its technological capabilities. These acquisitions include companies in automotive components and related sectors.
• Market Trends: The company continues to adapt to changing market trends, including advancements in electric vehicles and autonomous driving technologies.

Motherson Sumi Systems' stock price involves examining historical price data, performing data analysis, and creating visualizations to gain insights into the company's stock performance. Here's a step-by-step guide on how to conduct Motherson Sumi Systems' stock price analysis:

Import Libraries:

Start by importing the necessary Python libraries for data manipulation, analysis, and visualization. Commonly used libraries include pandas, numpy, matplotlib, and yfinance for fetching 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 price data for Motherson Sumi Systems' stock. Specify the start and end dates for the data you want to analyze:

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motherson_sumi = yf.download('MOTHERSUMI.NS', 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(motherson_sumi.head())

Data Visualization:

Create visualizations to analyze the historical performance of Motherson Sumi Systems' stock. Common visualizations include line charts to visualize price movements:

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

plt.plot(motherson_sumi['Adj Close'], label='Motherson Sumi')

plt.title('Motherson Sumi Systems Stock 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. Use numpy and pandas for these calculations.

Sentiment Analysis (Optional):

Consider incorporating sentiment analysis of news articles or social media data related to Motherson Sumi Systems to understand market sentiment's impact on the stock price.

Fundamental Analysis (Optional):

Analyze fundamental factors affecting Motherson Sumi Systems, such as earnings reports, revenue growth, product launches, and market share, which can influence the stock's performance.

Prediction and Forecasting (Optional):

You can use time series forecasting techniques like ARIMA or machine learning models to make predictions about future Motherson Sumi Systems' stock price movements.

Risk Management and Decision Making:

Based on your analysis, formulate investment strategies, set risk management parameters, and make informed investment decisions regarding Motherson Sumi Systems' stock.

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 stocks carries risks, and it's crucial to do thorough research, consider factors like company news and market trends, and potentially consult with financial experts before making investment decisions based on your analysis of Motherson Sumi Systems' stock price or any other stock.

How to get the historical Motherson Sumi Systems Stock price in Python?

There are a number of ways to get historical Motherson Sumi Systems Stock price in Python. you see code.

df = yf.download('MOTHERSUMI.NS',

 start='2010-01-01',

 end='2021-09-08',

 adjusted=True,

 progress=False)

We have gotten data from Yahoo Finance.

See data structural MOTHERSUMI

This is Motherson Sumi's stock-price monthly chart.

<matplotlib.axes._subplots.AxesSubplot at 0x7f4bea8a4450>

 Describing monthly Residual, seasonal trends,s and observed mothersonsumi charts is called Multiplicative Decomposition.

Motherson Sumi Systems Stock price target  by 2022

This chart forecasts price, rolling mean,s, and rolling standard deviation.

Regression Analisis

<matplotlib.axes._subplots.AxesSubplot at 0x7f4cb848d3d0>

What is the future of Mother Sumi stock?

Mothersonsumi stock price forecast charts

Here we describe components of yearly, monthly, weekly, and trends.

Now see Mothersonsumi stock price and actual prediction

[Text(0, 0.5, 'Stock Price '),
 Text(0.5, 0, 'Date'),
 Text(0.5, 1.0, 'Stock Price - actual vs. predicted')]

This is an ADF test to use forecast.

Test Statistic -0.937269 p-value 0.775452 # of Lags Used 0.000000 # of Observations Used 2879.000000 Critical Value (1%) -3.432623 Critical Value (5%) -2.862544 Critical Value (10%) -2.567305 dtype: float64

Correlation

KPSS TEST

 InterpolationWarning:

p-value is smaller than the indicated p-value

Test Statistic            7.177769
p-value                   0.010000
# of Lags                28.000000
Critical Value (10%)      0.347000
Critical Value (5%)       0.463000
Critical Value (2.5%)     0.574000
Critical Value (1%)       0.739000
dtype: float64

Beta =1.403554948944072

MOTHERSON SUMI CAPM ANALYSIS

OLS Regression Results ============================================================================== Dep. Variable: asset R-squared: 0.359 Model: OLS Adj. R-squared: 0.355 Method: Least Squares F-statistic: 77.43 Date: Sat, 18 Sep 2021 Prob (F-statistic): 5.02e-15 Time: 12:41:14 Log-Likelihood: 131.63 No. Observations: 140 AIC: -259.3 Df Residuals: 138 BIC: -253.4 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const 0.0108 0.008 1.314 0.191 -0.005 0.027 market 1.4036 0.160 8.800 0.000 1.088 1.719 ============================================================================== Omnibus: 7.706 Durbin-Watson: 2.259 Prob(Omnibus): 0.021 Jarque-Bera (JB): 10.663 Skew: 0.288 Prob(JB): 0.00484 Kurtosis: 4.223 Cond. No. 19.8 ==============================================================================

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