March 2025
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Purpose This study aims to examine the evidence and magnitude of the sector-specific herding behaviour in the Indian equity market, focusing on the COVID-19 epoch. Design/methodology/approach This study uses high-frequency daily data of the 11 sector indices of the National Stock Exchange from January 2010 to December 2022. Cross-sectional absolute deviation and quantile regression estimation methods using dummy variables are used to capture herding in skewed time series distribution across a range of return quantiles and sub-periods corresponding to the COVID-19 epoch. The magnitude of beta herd strength and variation in intensity to decipher the impact of COVID-19 is examined. Findings The statistical results are significant at lower returns across the entire sample period, implying evidence of herding. Notably, pre-COVID-19 herding during high returns in stocks of Public Sector Banks and post-COVID-19 herding during low returns in the information technology (IT) stocks was observed. However, regression estimates were significant across all sectors during the phase of COVID-19, with the IT sectors exhibiting the maximum increase in beta herd strength. Research limitations/implications Robust statistical techniques of quantile regression and beta dispersion to decipher herd behaviour provide insights for practitioners to broaden the understanding of market efficiency for actionable responses. Furthermore, the findings emphasise regulatory monitoring to prevent speculative bubbles and advocate for targeted investor education programmes to mitigate panic-driven investment decisions. Originality/value This paper is a pioneer in providing an alternative understanding, in contrast to the traditional one, into the micro-level analysis of herding phenomenon from the lens of the COVID-19 epoch. The results are instrumental in broadening the understanding of the market dynamics in turbulent periods, highlighting the importance of informed investment decisions.