Analysis of Sectoral Herding through Quantile Regression: A Study of S&P BSE 500 Stocks

Vijay Kumar Shrotryia and Himanshi Kalra
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Vijay Kumar Shrotryia: Department of Commerce, Faculty of Commerce and Business, Delhi School of Economics, University of Delhi, India
Himanshi Kalra: Department of Commerce, Faculty of Commerce and Business, Delhi School of Economics, University of Delhi, India

International Journal of Business and Economics, 2021, vol. 20, issue 1, 1-16

Abstract: The current study empirically investigates sector-wide flock activity for the S&P BSE 500 stocks over 8 years spanning from October 2010 till September 2018. Drawing on absolute deviation model by Chang et al. (2000), the present analysis tends to unravel the curvilinear relationship between consensus return and dispersion via Ordinary Least Squares and Quantile Regression approaches. Using conventional regression, a nonexistent herd hunch is inferred under both normal and asymmetric scenarios. However, the examination of distribution tails discovers herding in auto and engineering sector during bull markets and healthcare sector during bearish conditions. However, the two crises namely the oil crisis of 2014 and the Chinese crash of 2015 subject the Indian bourse to mimicking behavior. This may be a matter of concern for the policy makers as the evidences reflect on the unstable nature of the S&P BSE 500 index and the Indian stock market as a whole. Therefore, the regulatory bodies have to make consistent efforts to bridge the informational distance between various classes of investors and corporate houses to ensure more transparent and honest practices so that investors can make informed and better decisions. Finally, the investors may resort to active trading rules during turbulence to earn more than what market warrants.

Keywords: Herding; Ordinary Least Squares; Quantile Regression; Indian Stock Market (search for similar items in EconPapers)
JEL-codes: C3 C31 G1 G4 G41 (search for similar items in EconPapers)
Date: 2021
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