Sensex–Stock Performance Relationship: Evidence from Indian Banking and IT Sectors

This study set out to quantify, using a common five-year daily dataset (April 2021-March 2026), the relationship between the SENSEX and the performance of five leading Banking-sector and five leading IT-sector stocks in India. The evidence confirms that both sectors are significant drivers of SENSEX movement: correlation coefficients are positive and moderate-to-strong for nine of the ten sample stocks, and a multiple regression using all ten stocks jointly explains over three-quarters of the variation in SENSEX daily returns. At the level of individual securities, Banking-sector heavyweights — particularly ICICI Bank and SBI — show the single strongest association with the index. Yet when the two sectors are compared on a like-for-like, equal-weighted composite basis, the IT sector emerges as the more consistent and more powerful explanatory factor for SENSEX returns, a result driven by the greater internal homogeneity of the IT sample relative to the Banking sample, in which two constituents (Kotak Mahindra Bank and HDFC Bank) behave quite differently from the index and from their own sector peers. For investors and portfolio managers, the practical implication is twofold. First, both sectors matter to SENSEX performance and neither can be ignored when constructing a portfolio intended to track or outperform the index. Second, and more subtly, 'Banking exposure' and 'IT exposure' are not each internally uniform: within Banking, stock selection makes a material difference to how closely a portfolio will track the index, whereas a broadly diversified IT basket behaves comparatively predictably as a group. Because the two sector composites are only weakly correlated with each other (r = 0.145, computed from the study data), combining exposure to both sectors continues to offer genuine diversification benefit within a large-cap, SENSEX-oriented Indian equity portfolio — the two sectors should be treated as complementary rather than substitutable sources of index-related risk and return. Future research could usefully extend this analysis by (i) incorporating a longer time series spanning multiple interest-rate and global-demand cycles, (ii) applying GARCH-family models to capture time-varying volatility and correlation between the two sectors, as has been done for the Bank Nifty index in isolation, and (iii) examining whether the relative dominance of the IT sector documented here is a stable, structural feature of the Indian market or a period-specific artefact of the particular five-year window (2021-2026) studied, which spans both the post-pandemic recovery in IT services demand and a subsequent normalisation phase.

Agarwalla, S. K., Varma, J. R., & Virmani, V. (2021). The impact of COVID-19 on tail risk: Evidence from Nifty index options. Economics Letters, 204, 109878. Al-Awadhi, A. M., Alsaifi, K., Al-Awadhi, A., & Alhammadi, S. (2020). Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns. Journal of Behavioral and Experimental Finance, 27, 100326. Alam, M. N., Alam, M. S., & Chavali, K. (2020). Stock market response during COVID-19 lockdown period in India: An event study. Journal of Asian Finance, Economics, and Business, 7(7), 131-137. Arora, R. K. (2016). The relationship between investment of domestic and foreign institutional investors and stock market returns in India. Indian Journal of Finance and Economics, 12(2), 44-58. Ashri, D., Sahoo, B. P., Gulati, A., & Haq, I. U. (2021). Repercussions of COVID-19 on the Indian stock market: A sectoral analysis. In Advances in Business and Management Forecasting. Springer. Awalakki, M. (2022). Neurotransmitters impact on emotional responses and decision-making in investment: A comprehensive exploration. International Journal of Food and Nutritional Sciences, 11(5), 690-708. Awalakki, M., & Archanna. (2021). A study on accounting ratios and stock returns with reference to National Stock Exchange of India. Turkish Online Journal of Qualitative Inquiry, 12(7), 6858-6888. Awalakki, M., & Archanna. (2023). Exploring the dynamics of investor attention and market volatility: A behavioral finance perspective. International Journal of Science and Research, 12(8), 2245-2251. https://doi.org/10.21275/SR23823155339 Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78. Bardoliwala, H. N. (2023). The relationship between interest rates and stock market performance. International Journal for Multidisciplinary Research, 5(6).