Topic: Relationship of Nestle India Ltd. With Nifty 50
(Date: 1-Jan-2024 – 31-Dec-2024)
Author Name: Nilesh Subhash Turankar
Introduction:
Nestlé India Limited, a subsidiary of the Swiss multinational Nestlé, is a major player in India’s fast-moving consumer goods (FMCG) sector, known for its diverse portfolio of food and beverage products under brands like Nescafé, Maggi, and Kit Kat, with a history dating back to 1912.
After India’s independence in 1947, the economic policies of the Indian Government emphasized the need for local production. NESTLÉ responded to India’s aspirations by forming a company in India and set up its first factory in 1961 at Mega, Punjab, where the Government wanted NESTLÉ to develop the milk economy.
Progress in Moga required the introduction of NESTLÉ’s Agricultural Services to educate, advice and help the farmer in a variety of aspects. From increasing the milk yield of their cows through improved dairy farming methods, to irrigation, scientific crop management practices and helping with the procurement of bank loans.
NESTLÉ set up milk collection centres that would not only ensure prompt collection and pay fair prices, but also instil amongst the community, a confidence in the dairy business. Progress involved the creation of prosperity on an on-going and sustainable basis that has resulted in not just the transformation of Moga into a prosperous and vibrant milk district today, but a thriving hub of industrial activity, as well.
NESTLÉ has been a partner in India’s growth for over a century now and has built a very special relationship of trust and commitment with the people of India.
Objective:
To find out the Beta of Nestle India Ltd. With respect to Nifty 50
Literature Review:
Nestlé India, a subsidiary of the Swiss multinational Nestlé, has a long history in India, dating back to 1912, and has grown to become a major player in the Indian fast-moving consumer goods (FMCG) sector. The company often highlights its commitment to quality, nutrition, and local production, while also acknowledging the importance of understanding and adapting to the changing Indian consumer landscape.
· Focus on Consumer Needs:
The company continuously strives to understand the changing lifestyles and needs of Indian consumers to offer products that meet their preferences.
· Innovation and Product Development:
Nestlé India invests in innovation and product development to create value and cater to a wide range of consumer tastes.
· Local Production and Employment:
Nestlé India contributes significantly to employment opportunities, including those for farmers, packaging material suppliers, and service providers.
Nestlé’s Impact on Indian Economy:
Nestlé India, a major player in the Indian FMCG market, significantly impacts the Indian economy by contributing to employment, agricultural development, and technological advancement, while also serving countless consumers with a range of products.
· Employment:
Nestle India’s operations facilitate direct and indirect employment, providing livelihoods for about one million people, including farmers, suppliers, and those involved in packaging, services, and other goods.
· Agricultural Development:
Nestle India has a history of supporting the milk economy, educating and advising farmers on improved dairy farming methods, irrigation, and scientific crop management practices.
· Technological Advancement:
The company is a significant part of the Indian market, serving countless consumers with a range of quality products, and is constantly striving to improve its product offerings in terms of convenience, taste, nutrition, and wellness.
Data Collection:
Nestle India Ltd. and Nifty50 data was download for period 1-1-24 to 31-12-24 and data was manipulated to find out the Friday closing prices were calculated of Nifty50 as X and Nestle India Ltd. As Y. Y was regression on X
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SUMMARY OUTPUT |
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Regression Statistics |
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Multiple R |
0.025657142 |
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R Square |
0.000658289 |
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Adjusted R Square |
-0.003387629 |
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Standard Error |
0.061273732 |
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Observations |
249 |
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ANOVA |
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df |
SS |
MS |
F |
Significance F |
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Regression |
1 |
0.000610869 |
0.000610869 |
0.162704475 |
0.687027635 |
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Residual |
247 |
0.92735414 |
0.00375447 |
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Total |
248 |
0.927965009 |
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Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
Lower 95.0% |
Upper 95.0% |
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Intercept |
0.001665684 |
0.003890476 |
0.428144044 |
0.668919338 |
-0.005997055 |
0.009328424 |
-0.006 |
0.009328 |
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X Variable 1 |
-0.024433018 |
0.06057276 |
-0.403366428 |
0.687027635 |
-0.14373802 |
0.094871984 |
-0.14374 |
0.094872 |
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Data Analysis:
Equation:
Intercept (0.001665684) (Nestle India Ltd.)
X Variable 1 (-0.024433018) (Nifty 50)
- Multiple R (0.025657142): This represents the correlation coefficient between the observed and predicted values of the dependent variable. It’s very low, indicating a weak linear relationship.
- R Square (0.000658289): This is the coefficient of determination. It tells us the proportion of the variance in the dependent variable that is predictable from the independent variable. In this case, 1 only about 0.066% of the variance is explained, which is extremely low.
- Adjusted R Square (-0.003387629): This adjusts the R-squared for the number of predictors in the model. It’s even lower than the R-squared, suggesting that the model doesn’t explain the data well and might be over fitting (though with only one predictor, over fitting is less of a concern).
- Standard Error (0.061273732): This represents the average distance that the observed values fall from the regression line. It’s a measure of the variability of the data around the regression line.
- Observations (249): This is the sample size used in the regression.
ANOVA (Analysis of Variance)
- df (Degrees of Freedom):
- Regression: 1 (This reflects the number of predictors in the model, which is one.)
- Residual: 247 (This is calculated as the number of observations minus the number of parameters estimated, which is 249 – 2.)
- Total: 248 (This is the total number of observations minus 1, or 249 – 1.)
- SS (Sum of Squares):
- Regression: 0.000610869 (This represents the variability explained by the model.)
- Residual: 0.92735414 (This represents the variability not explained by the model.)
- Total: 0.927965009 (This is the total variability in the dependent variable.)
- MS (Mean Square):
- Regression: 0.000610869 (This is the SS divided by the df for regression.)
- Residual: 0.00375447 (This is the SS divided by the df for residuals.)
- F (F-statistic): 0.162704475 (This is the ratio of the mean square regression to the mean square residual. It’s used to test the overall significance of the model.)
- Significance F (P-value): 0.687027635 (This is the p-value associated with the F-statistic. It’s the probability of observing the F-statistic if there were no relationship between the variables. Since it’s greater than 0.05, the overall model is not statistically significant.)
Coefficients
- Intercept (0.001665684): This is the predicted value of the dependent variable when the independent variable is zero.
o Standard Error (0.003890476): The standard error of the intercept coefficient.
o t Stat (0.428144044): The t-statistic for the intercept.
o P-value (0.668919338): The p-value associated with the intercept. Since it’s greater than 0.05, the intercept is not statistically significant.
o Lower 95% (-0.005997055) and Upper 95% (0.009328424): The 95% confidence interval for the intercept.
- X Variable 1 (-0.024433018): This is the coefficient for the independent variable. It represents the change in the dependent variable for a one-unit change in the independent variable.
o Standard Error (0.06057276): The standard error of the coefficient for X Variable 1.
o t Stat (-0.403366428): The t-statistic for the coefficient.
o P-value (0.687027635): The p-value associated with the coefficient. Since it’s greater than 0.05, the coefficient is not statistically significant.
o Lower 95% (-0.14373802) and Upper 95% (0.094871984): The 95% confidence interval for the coefficient.
Conclusion:
- Poor Model Fit: The R-squared is extremely low, indicating that the independent variable does not explain much of the variance in the dependent variable.
- Lack of Significance: The overall model (as indicated by the F-statistic and its p-value) and the individual coefficient for X Variable 1 are not statistically significant.
- Weak Relationship: The low Multiple R and the non-significant coefficient suggest a very weak linear relationship between the variables.
- Implications: This regression model is not useful for making predictions or drawing meaningful conclusions about the relationship between the variables.
References:
https://www.nestle.in/about-us/nestle-in-india