Title: Relationship of State Bank of India with Nifty 50
Author: Shweta Sharma
Introduction:
State Bank of India (SBI) is one of the biggest state-owned financial institutions in India. Headquartered in Mumbai, the bank provides a wide range of products and services to its customers, which includes commercial enterprises, large corporate, public bodies and institutional customers. SBI is also one of the largest banks in India in terms of market capitalisation.
The bank descends from the Imperial Bank of India, which was formed by merging Bank of Calcutta, Bank of Madras and the Bank of Bombay in 1806. The Imperial Bank of India became the State Bank of India in 1955 after Government of India took control of it with Reserve Bank of India (RBI) taking a 60 per cent stake in it. In 2008, the government took over the stake held by the RBI. Representing the legacy of over 200 years, SBI has shown consistent growth compared to other public sector lenders in India.
Objective: Calculate beta of the State Bank of India and see its significance.
Data collection:
Firstly, have downloaded the data of Equities and Nity-50 dated from 1st April 2021 to 31st March 2022 from the website of National Stock Exchange(nseindia.com) Firstly, have taken the data of Nifty-50 and after deleting all the column except date and closing price, I have added the weekday column in the data using weekday function of excel, then we have deleted all rows except the 5th day (Friday) rows. Then we have added another column named as Return column where we have found the returns by using the values in closing price column and named it as X (which will be X for regression also). Similarly repeating all these steps for our selected company, I named it as Y (which will be Y for regression also) Here is how I collected how the values of X and Y.
Data Analysis:
We can write the equation in the form of Y= a + b(X)
Where Y=Equities Return
X=Nifty-50 Returns
a = intercept and b = slope
Hence, the equation becomes
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.623979824
R Square 0.389350821
Adjusted R Square 0.375149677
Standard Error 39.4454812
Observations 45
ANOVA
df SS MS F Significance F
Regression 1 42659.15898 42659.15898 27.41686365 4.65572E-06
Residual 43 66905.67746 1555.945987
Total 44 109564.8364
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -718.4552102 203.8612924 -3.524235531 0.001021837 -1129.580688 -307.329732 -1129.580688 -307.329732
X PRICE 0.071921012 0.013735577 5.236111501 4.65572E-06 0.044220581 0.099621443 0.044220581 0.099621443
Y=a+bx
A= 718.4552102
B= 0.071921012
Y= 718.4552102-0.071921012X
T stat= 5.24
Since P value is less than 0.05 the model is significant @5%.
The above equation shows the relationship between price and demand.
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