Title: Relationship of Tech Mahindra Ltd. With Nifty 50
Author: Tejas Pote
Introduction
Tech Mahindra Ltd. is a leading Indian multinational technology company headquartered in Pune, India. Established in 1986, the company provides IT services, including digital transformation, consulting, and business process outsourcing solutions. With a strong presence in over 90 countries, Tech Mahindra serves diverse industries such as telecommunications, banking, healthcare, and retail. It is a key constituent of the Nifty 50 index, reflecting its significant influence on the Indian stock market and the technology sector.
Objective
To determine the Beta of Tech Mahindra Ltd. and its significance.
Literature Review
New Approaches to IT Sector Volatility Assessments
Misra and Mohapatra (2015) examine the momentum effect in the CNX NIFTY 50 index, analyzing whether past-performing stocks continue their trend. The study applies risk-adjusted return analysis and regression models to evaluate momentum-based investment strategies. Their findings suggest that the Indian stock market is not entirely efficient, as investors can generate excess returns using momentum strategies. Since Tech Mahindra is a key player in the IT sector, the study highlights how technology stocks may exhibit unique momentum characteristics depending on global digital trends and market cycles.
Impact of IT Sector on Market Stability
Gali (2021) investigates how Nifty 50 stocks, including Tech Mahindra, react to financial crises. Using extreme value theory and volatility clustering techniques, the study assesses stock resilience during downturns. The results indicate that while IT stocks are sensitive to global economic trends, they often recover strongly due to high demand for digital services. The study also identifies volatility patterns, highlighting how macroeconomic shocks affect technology stocks differently from defensive sectors like pharmaceuticals.
Stock Market Volatility and Sectoral Interdependence in India
Sharma and Gupta (2019) explore the volatility trends of various sectors in the Indian stock market, including the IT sector. Using econometric models such as GARCH and VAR, the study finds that IT stocks, including Tech Mahindra, exhibit higher volatility compared to defensive sectors but tend to outperform during periods of economic recovery. The research suggests that sectoral interdependence plays a crucial role in shaping stock price movements, with IT stocks responding dynamically to macroeconomic indicators such as exchange rates and interest rates.
Data Collection
Tech Mahindra and Nifty 50 data were downloaded for the period 01-01-2024 to 31-12-2024, and data was manipulated to find the Friday closing prices for Nifty 50 (X) and Tech Mahindra (Y). Y was regressed on X.
Data Analysis
Equation:
Tech Mahindra = -0.0057 + (-4.12) Nifty50
Interpretation:
The regression equation describes the relationship between Nifty50 (X) and Tech Mahindra’s share price (Y), indicating that Tech Mahindra’s share price is the dependent variable, while Nifty50 is the independent variable. The negative coefficient of -4.12 suggests that for every unit increase in Nifty50, Tech Mahindra’s share price is expected to decrease by 4.12 units. With 47 observations (N = 47), the R² value is 0.1370, implying that approximately 13.7% of the variation in Tech Mahindra’s share price can be explained by changes in Nifty50. The F-value for the model is 3.58, and the p-value for the slope is 0.0647, which is slightly above the 0.05 threshold, indicating that the relationship is moderately significant but not strongly statistically significant.
Conclusion
Tech Mahindra’s beta of -4.12 indicates that it is highly volatile and moves in the opposite direction of the market. This suggests that Tech Mahindra may act as a hedge against market movements, making it more suitable for short-term trading rather than long-term investment due to its inverse correlation with Nifty50.
References
Arun Kumar Misra & Sabyasachi Mohapatra, 2015. “Indexing CNX NIFTY 50 Momentum Effects,” Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 9(2), pages 157-178, May.
Srilakshminarayana Gali, 2021. “The Behaviour of Extreme and Cumulative Stock Price Random Variables during the Crisis Periods—A Study of Nifty 50 Stocks,” Economic Research Guardian, Mutascu Publishing, vol. 11(1), pages 103-129, June.
Rakesh Sharma & Anurag Gupta, 2019. “Stock Market Volatility and Sectoral Interdependence in India,” International Journal of Financial Research, vol. 10(4), pages 210-225, December.