Intensified geopolitical conflicts and herding behavior: An evidence from selected Nifty sectoral indices during India-China tensions in 2020

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The recent India-China geopolitical conflicts have presented enormous uncertainty to the investors in various sectoral indices of the Indian stock market. This empirical study aims to examine the impact of intensified India-China geopolitical conflicts 2020 on investors’ herding behavior in the National Stock Exchange sectoral indices. The high-frequency data of three major NIFTY sectoral indices (Auto, Energy, and Pharma) are used in an intensified geopolitical event window to spot precisely the traces of the investors’ herding behavior. Furthermore, multifractal detrended fluctuation analysis (MFDFA) is employed to obtain Hurst Exponent values (h(q)) for the NIFTY sectoral indices. The findings reveal that these NIFTY sectoral indices exhibited profound traces of herding behavior on the event day (t = 0) due to the heightened India-China geopolitical clashes. In addition, these indices depicted an overall higher level herding behavior with the (h(q)) values close to 0.72 throughout the intensified geopolitical event window. The study concludes that the sectors highly reliant on the Chinese supplies and with significant trade linkages with China depicted a higher level of herding behavior in their indices. Further, the presence of herding behavior in these sectoral indices is due to the operational and supply-chain risks posed by the geopolitical event.

Acknowledgments
The authors express their sincere thanks of gratitude to Dr. Bikramaditya Ghosh (Associate Professor, Symbiosis Institute of Business and Management, Bangalore, India) and Dr. Iqbal Thonse Hawaldar (Professor, College of Business Administration, Kingdom University, Riffa, Bahrain) for their instrumental role in encouraging and motivating them to accomplish this publication. The authors also extend their sincere thanks to Dr. Manu K.S and Dr. Surekha Nayak (Assistant Professor, School of Business and Management, CHRIST (Deemed to be university), Bangalore, India) for their continued support throughout this empirical investigation.

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    • Figure 1. The Hq(5) of Nifty Auto index on June 15, 2020, i.e., (t = –1)
    • Figure 2. The Hq(5) of Nifty Auto index on June 16, 2020, i.e., (t = 0)
    • Figure 3. The Hq(5) of Nifty Auto index on June 17, 2020, i.e., (t = +1)
    • Figure 4. The Hq(5) of Nifty Energy index on June 15, 2020, i.e., (t = –1)
    • Figure 5. The Hq(5) of Nifty Energy index on June 16, 2020, i.e., (t = 0)
    • Figure 6. The Hq(5) of Nifty Energy index on June 17, 2020, i.e., (t = +1)
    • Figure 7. The Hq(5) of Nifty Pharma index on June 15, 2020, i.e., (t = –1)
    • Figure 8. The Hq(5) of Nifty Pharma index on June 16, 2020, i.e., (t = 0)
    • Figure 9. The Hq(5) of Nifty Pharma index on June 17, 2020, i.e., (t = +1)
    • Table 1. Intensified geopolitical event window for India-China tensions, 2020
    • Table 2. Nifty sectoral indices and their description
    • Table 3. Number of intraday tick-by-tick unique observations considered for analysis
    • Table 4. Hurst exponent value range
    • Table 5. Illustrating results of Hurst exponent (Hq) from MFDFA analysis
    • Conceptualization
      Krishna T. A., Suresha B.
    • Data curation
      Krishna T. A.
    • Formal Analysis
      Krishna T. A., Suresha B.
    • Funding acquisition
      Krishna T. A.
    • Investigation
      Krishna T. A., Suresha B.
    • Methodology
      Krishna T. A., Suresha B.
    • Project administration
      Krishna T. A., Suresha B.
    • Software
      Krishna T. A.
    • Validation
      Krishna T. A.
    • Writing – original draft
      Krishna T. A.
    • Resources
      Suresha B.
    • Supervision
      Suresha B.
    • Visualization
      Suresha B.
    • Writing – review & editing
      Suresha B.