Title: Relationship between Nifty 50 and MRF Limited.
Author: Madhura S. Bijjargi.
Introduction:
MRF Limited, founded in 1946, is a leading Indian tire manufacturer headquartered in Chennai. Renowned for its quality and innovation, MRF has a global presence and specializes in producing high-performance tires for diverse vehicles. Beyond tires, the company has diversified into paints, conveyor belts, and advanced materials. MRF’s commitment to research, cutting-edge technology, and ethical business practices has solidified its position in the global market. With a strong focus on customer satisfaction, MRF remains a key player in the automotive industry. The company’s success is underlined by its resilience, community engagement, and dedication to environmental sustainability.
Objective: To calculate beta and its significance.
Literature Review:
1) Jain, A. (2022), states that for efficient decision making for the retail investors, this study focused on analysis of the relationship between the main institutional investors and Indian stock index, i.e., Nifty 50. After the pandemic, every investor is seeking the best investment portfolio to earn significant return. The preconditions are satisfied for cointegration: that all series are stationary at I(1) and the error term is stationary at I(0). In the first segment of relationship between Nifty 50 (Indian stock market index) and mutual funds (DIIs), there is cointegration between the series as per Johansen Cointegration, which leads to error correction model. VECM shows the speed of adjustment at 0.02% in case of disequilibrium. Moreover, Granger causality also shows the behavior of Nifty 50 impacting mutual funds net sales, that is unidirectional causal relation. Then Variance Decomposition also clarifies if shock is given to Nifty 50, then after certain period, 10.41% of the deviation will be transmitted to mutual funds.
In the second segment of the relationship between Nifty 50 and FIIs, Johansen Cointegration test implies the null hypothesis is rejected and the series are cointegrated. Although the correlation between these did not show much relation, VECM indicates the adjustment coefficient at 0.16% at the time of disequilibrium. Granger Causality shows unidirectional causal relation between the series that the movement comes from Nifty to FIIs, and Variance Decomposition data also reflects if any is shocked given to Nifty 50, then after certain period of time, 59.34% of the deviation will be shifted to FIIs.
2) Meena, V. K., and et al conclude that the impact of risk on the bottom lines of commercial banks. Using CAR, COI and NNPA as independent variables and ROA and ROE as dependent variables, the study established a causal link between risk and financial health. Panel data estimate was performed using the pooled panel data regression and fixed effects model. The findings show that banks’ ROA and ROE are both strongly and negatively correlated with increases in the COI, NNPA and operating expenses. According to available research, the rising cost-toincome ratios, operating expenses and NNPAs of banks are mostly attributable to a lack of oversight and monitoring. There is a rise in risk for banks because of the rise in NPAs. Although there is a favorable relationship between CAR, ROA and ROE, this metric has no practical bearing on business results. A bank must keep a certain amount of capital on hand at all times, but the variation in CAR is not materially impacting the banks’ financial standing. All the sample banks did not reflect any effect of interest expense ratio on their financial health. The financial health of any bank is mainly affected by increasing cost to income, operating expenses and NPAs. So, the bank management has to make strategies to control the operating expenses, and further reduce the cost to income. NPA is a critical factor to risk. The increasing NNPA in a bank is a worrisome situation, and it badly affects the financial health of any bank. So, banks should make policies for loan recovery and monitoring of their credit portfolio. In order to improve the market and the economy, authorities need accurate information on the financial standing of the country’s largest banks included in the Nifty 50 index so that they can implement targeted measures to lower cost to income and NPA costs.
Data Collection:
Data for NIFTY 50 has been downloaded from NSE website from date 1st February 2023 to 31st January 2024 and the data has been manipulated for Friday closing prices of NIFTY 50 and MRF Limited. Friday closing prices of NIFTY 50 is X and of MRF Limited is Y.
Data Analysis:
Y was regressed on X.
MRF share=46.48241+9.322 Nifty
n=48, R square=0.000203, F=0,00915
The above equation shows relationship of MRF and Nifty 50.
If Nifty 50 rises by 1 unit, MRF will rise by 9.322.
t-stat for b is 0.09565 and the p value is 0.92422 which is greater than 0.05, so b is not equal to 0 , meaning Nifty doesn’t impacts MRF.
Rsquare is 0.000203, meaning 0.02% of MRF share is explained by the movement in Nifty, meaning 99.98% depends upon other things like fundamentals.
F = 0.00915, and p value is greater than 0.05, so the model is not statistically significant at 5% level.
Conclusions:
As beta is (9.322) is more than 1, invest for short term as it will give higher return on the stock rather than market returns.
References:
1) Jain, A. (2022). Impact of institutional investors on indian stock market performance. IUP Journal of Applied Finance, 28(4), 5-29. Retrieved from https://www.proquest.com/scholarly-journals/impact-institutional-investors-on-indian-stock/docview/2764532962/se-2
2) Meena, V. K., Kumar, S., & Shambharkar, R. T. (2023). Determinants of financial health of commercial banks in india: A study of nifty 50 banks. IUP Journal of Financial Risk Management, 20(2), 5-21. Retrieved from https://www.proquest.com/scholarly-journals/determinants-financial-health-commercial-banks/docview/2899445502/se-2
