Impact of Nifty50 on Ashok Leyland’s Share Price
Author: Aarya Mahesh Karandikar
Introduction:
Ashok Leyland is one of India’s leading commercial vehicle manufacturers, headquartered in Chennai. It is a flagship company of the Hinduja Group and has played a crucial role in shaping India’s transportation industry. Established in 1948, the company is known for producing trucks, buses, defense vehicles, and power solutions. Ashok Leyland is the second-largest manufacturer of commercial vehicles in India and ranks among the top 10 truck manufacturers worldwide. The company has a strong presence in the domestic and international markets, offering innovative mobility solutions through electric and alternative fuel vehicles. With a focus on technology and sustainability, Ashok Leyland continues to drive growth in India’s automobile sector.
Objective:
To determine the Beta of Ashok Leyland and assess its significance in relation to the Nifty50 index.
Literature Review:
Madhavedi et al. (2024), in their study “Risk and Return Analysis in the Indian Automobile Industry”, conducted a comprehensive analysis of the risk-return dynamics of selected stocks in the Indian automobile industry listed on the National Stock Exchange. The research examined the equity performance of companies such as Maruti Suzuki, Mahindra & Mahindra, Tata Motors, Hindustan Motors, and Ashok Leyland from 2017 to 2023. Utilizing statistical tools like mean, standard deviation, variance, and beta, the study provided insights into the volatility, return potential, and overall risk profiles of these companies. The findings indicated a direct correlation between higher risk and higher returns, with Ashok Leyland emerging as a top performer, while Maruti Suzuki demonstrated more stable returns. The study emphasized the importance of diversification and long-term analysis for investors navigating the evolving Indian automobile sector.
Gooding and O’Malley (1977), in their research “Market Phases and Beta Stationarity”, examined the stationarity of beta coefficients concerning significant stock market trends. They introduced a method involving paired t-tests to assess the stability of portfolio betas, adjusting for measurement errors as suggested by Blume. Their findings contribute to understanding how beta coefficients may vary across different market phases, which is crucial for investors relying on beta as a measure of systematic risk.
Data Collection :
For this analysis, data for Ashok Leyland and Nifty50 were collected for the period from January 1, 2024, to December 31, 2024. The Friday closing prices were extracted to maintain consistency.
– Nifty50 (X) = Independent Variable
– Ashok Leyland (Y) = Dependent Variable
A regression analysis was performed with Ashok Leyland’s stock returns as the variable and Nifty50 returns as the independent variable.
Data Analysis:
Regression Equation:
Ashok Leyland = 0.00294 + 1.03519 × Nifty50
Interpretation:
The regression equation describes the relationship between Nifty50 (X) and Ashok Leyland’s share price (Y), indicating that Ashok Leyland’s share price is the dependent variable, while Nifty50 is the independent variable.
· The positive coefficient (β) of 1.03519 suggests that for every unit increase in Nifty50, Ashok Leyland’s share price is expected to increase by 1.03519 units.
· With 47 observations (N=47), the R² value is 0.1949, implying that approximately 19.49% of the variation in Ashok Leyland’s share price can be explained by changes in Nifty50.
· The F-value for the model is 10.8929.
· The p-value for the slope is 0.00189, which is less than the conventional threshold of 0.05. This indicates that the relationship between Nifty50 and Ashok Leyland’s share price is statistically significant at the 5% level.
Conclusion:
Ashok Leyland’s beta (β) of 1.03519 suggests that its stock is slightly more volatile than the overall market. The statistical significance of the regression model supports a meaningful relationship between Ashok Leyland’s stock price and Nifty50. However, the relatively moderate R-squared value implies that other external factors, such as industry trends, economic policies, and company-specific events, also influence the stock’s performance.
References:
1. Sudhakar Madhavedi & Wong Chee Hoo & Ng Chee Pung & Anugu Anil Reddy, 2024. “Risk and return analysis of selected stocks in the Indian automobile industry on the national stock exchange,” Edelweiss Applied Science and Technology, Learning Gate, vol. 8(6), pages 7063-7138.
2. Gooding, Arthur E. & O’Malley, Terence P., 1977. “Market Phase and the Stationarity of Beta,” Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(5), pages 833-857, December.