Do all shocks produce embedded herding and bubble? An empirical observation of the Indian stock market
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Received July 27, 2022;Accepted September 21, 2022;Published September 27, 2022
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Author(s)Link to ORCID Index: https://orcid.org/0000-0001-5564-5111Link to ORCID Index: https://orcid.org/0000-0002-0409-4599
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DOIhttp://dx.doi.org/10.21511/imfi.19(3).2022.29
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Article InfoVolume 19 2022, Issue #3, pp. 346-359
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Cited by2 articlesJournal title: Asia-Pacific Financial MarketsArticle title: Does G7 Engross the Shock of COVID 19: An Assessment with Market Volatility?DOI: 10.1007/s10690-023-09398-8Volume: 30 / Issue: 4 / First page: 795 / Year: 2023Contributors: Nupur Moni Das, Bhabani Sankar Rout, Yashmin KhatunJournal title: Banks and Bank SystemsArticle title: Banking system stability in crisis periods: The impact of the banking regulator independenceDOI: 10.21511/bbs.18(3).2023.18Volume: 18 / Issue: 3 / First page: 221 / Year: 2023Contributors: Atik Kerimov, Azer Babayev, Viktoria Dudchenko, Yaryna Samusevych, Milos Tumpach
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Herding has a history of igniting large, irrational market ups and downs, usually based on a lack of fundamental support. Intuitively, most herds start with an external shock. This empirical study seeks to detect shock-induced herding and the creation of nascent bubbles in the Indian stock market. Initially, the multifractal form of the detrended fluctuation analysis was applied. Then the Reformulated Hurst exponent for the Bombay stock exchange (BSE) was determined using Kantelhardt’s calibration. The investigation found evidence of high-level herding and a bubble in 2012, with a high value of Hurst Exponent (0.7349). The other years of the research period (2011, 2013, 2016, 2018, 2020–2021) observed mild to significant herding with comparatively lower Hurst values. The results confirm that herding behavior occurs during a crisis and harsh situations emitting shocks. The study concludes that shock-based herding is prevalent in all six shocks: the economic meltdown, commodities and currency devaluation, geo-political problems, the Central Bank’s decision on liquidity management, and the Pandemic. Additionally, the years following the Financial Crisis and the years of the Pandemic are when herding and bubble are prominent.
Acknowledgments
We thank Dr. Bikramaditya Ghosh (Associate Professor, Symbiosis International University, Bangalore, India) for motivating us in this research. We also thank Dr. Natchimuthu N (Assistant Professor, Commerce, CHRIST (Deemed to be University), Bangalore, India) and Dr. Mahesh E. (Assistant Professor, Economics, CHRIST (Deemed to be University), Bangalore, India) for their support throughout this study.
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JEL Classification (Paper profile tab)D53, G14, G41
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References47
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Tables3
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Figures12
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- Figure 1. Hurst exponent values of BSE 100 from January 1, 2011 to December 31, 2021
- Figure A1. Hurst exponent value Hq (5) in 2011
- Figure A2. Hurst exponent value Hq (5) in 2012
- Figure A3. Hurst exponent value Hq (5) in 2013
- Figure A4. Hurst exponent value Hq (5) in 2014
- Figure A5. Hurst exponent value Hq(5) in 2015
- Figure A6. Hurst exponent value Hq (5) in 2016
- Figure A7. Hurst exponent value Hq (5) in 2017
- Figure A8. Hurst exponent value Hq (5) in 2018
- Figure A9. Hurst exponent value Hq (5) in 2019
- Figure A10. Hurst exponent value Hq (5) in 2020
- Figure A11. Hurst exponent value Hq (5) in 2021
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- Table 1. Hurst values and their associated interpretations
- Table 2. Illustrating the results of Hurst for BSE 100
- Table 3. Summary of events during periods of High Hurst Exponent
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Conceptualization
Tabassum Khan, Suresh G.
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Data curation
Tabassum Khan
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Formal Analysis
Tabassum Khan
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Investigation
Tabassum Khan, Suresh G.
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Methodology
Tabassum Khan
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Software
Tabassum Khan
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Writing – original draft
Tabassum Khan
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Writing – review & editing
Tabassum Khan, Suresh G.
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Validation
Suresh G.
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Visualization
Suresh G.
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Conceptualization
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