TITLE : RELATIONSHIP OF WEEKLY RETURNS BETWEEN REDINGTON (INDIA) & NIFTY 50 (RERESSION ANALYSIS).

AUTHOR :- ROSHAN DHONDIRAM SHEDGE
ROLL NO :- P033
BATCH :- EMBA 15 (KHARGHAR).
SUBJECT :- RESEARCH METHODOLOGY

INTRODUCTION :
An integrated supply chain solution provider Redington India Limited was incorporated on 2nd May 1961. Being the second largest distributor of IT products in India the company and through all its subsidiaries distributes products from over 220 leading manufacturer services over 39800 channel partners in India. It is engaged in distribution of Information Technology mobility and other technology products besides supply chain solutions and after sales services.
R.Srinivasan is an Indian businessman. He is the founder of Redington India, a US $4.2 billion technology products supply chain solution company operating in India, the Middle East, Africa and Turkey.

OBJECTIVES :
To calculate BETA (β) of REDINGTON (INDIA) LTD & find its significance using regression analysis with NIFTY 50.

DATA COLLECTION :
Data for REDINGTON INDIA LTD and NIFTY 50 has been downloaded from NSE site from date 1-3-2019 to 28-2-2020. From the available data, the closing rates of all the Fridays in the year was sorted to find out weekly returns for both NIFTY as well as REDINGTON INDIA LTD. Then the weekly returns was calculated for both by using formula – Weekly Return = (C3-C2)/(C2*100) where , C3 is present week closing price and C2 is the previous week closing price. Once the data is calculated, weekly return column for NIFTY50 is considered as “X” variable and the weekly returns column for REDINGTON INDIA LTD is considered as “Y” variable.
The Model and formulas used are:-
Y = a +bX
X ̅ =∑X/N
Y ̅=∑Y/N
x = X – X ̅
y = Y – Y ̅
b=∑xy/∑(x)^2
a= Y ̅- bX ̅
e = Y – Y ̅
Variance of error=(σe)^2 =∑e^2/N-K
S.E of b = √ ((σe)^2 /∑x^2)
t stat of b = b/ S.E of b
TSS=ESS+RSS
ESS = (b^2)*(∑x^2)
RSS = ∑e^2
R^2 = ESS/TSS
F = Mean ESS/Mean RSS

DATA ANALYSIS:
Using the Regression Add-on in Microsoft Excel Data Analytics tool we get following values:
R Square
R2=0.1430
a = -0.2782
b = 0.8580
N (Observations) = 50
F = 8.0101
Therefore, formulating below question:
Y = -0.2782-0.8580X
(2.8302)*

RESULT :- The above equation tells us the relationship between “Y” and “X”, that is Demand and Price. If price falls by unit, demand raises by 0.8580 units. Positive says that, there is reverse relationship. Means if price rises demand rises, similarly if price falls then demand also falls.

Figure In bracket is t-stat for “b”. b value for which is more than 0.05 so “b” is statistically significant at 5%. R2 = .1430, which means 14% Y is explained by X. 86% error or other variables aren’t in the models. F is 8.0101. The P value for which is less than 0.05, overall model is statistically significant at 5%.

CONCLUSION:-
Price is significant and overall model is significant but error will be 86 %.