# Motherson Sumi Systems Stock price target by 2023

### 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.
start='2010-01-01',
end='2021-09-08',
progress=False)
We have gotten data from Yahoo Finance.
This is Motherson Sumi's stock-price monthly chart.
`<matplotlib.axes._subplots.AxesSubplot at 0x7f4bea8a4450>`

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

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

#### What is the future of Mother Sumi stock?

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

#### 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 ==============================================================================

Learn Python