Do all shocks produce embedded herding and bubble? An empirical observation of the Indian stock market
-
Author(s)Tabassum KhanLink to ORCID Index: https://orcid.org/0000-0001-5564-5111
,
Suresh G.Link to ORCID Index: https://orcid.org/0000-0002-0409-4599
-
DOIhttp://dx.doi.org/10.21511/imfi.19(3).2022.29
-
Article InfoVolume 19 2022, Issue #3, pp. 346-359
- Cited by
- 380 Views
-
125 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
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.
- Keywords
-
JEL Classification (Paper profile tab)D53, G14, G41
-
References47
-
Tables3
-
Figures12
-
- 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
-
- 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
-
- Akhtaruzzaman, M., Boubaker, S., Lucey, B. M., & Sensoy, A. (2021). Is gold a hedge or a safe-haven asset in the COVID–19 crisis? Economic Modelling, 102(June), 105588.
- Ali, S., Shahzad, S. J. H., Raza, N., & Al-Yahyaee, K. H. (2018). Stock market efficiency: A comparative analysis of Islamic and conventional stock markets. Physica A: Statistical Mechanics and Its Applications, 503, 139-153.
- Aslam, F., Aziz, S., Nguyen, D. K., Mughal, K. S., & Khan, M. (2020). On the efficiency of foreign exchange markets in times of the COVID-19 Pandemic. Technological Forecasting and Social Change, 161, 120261.
- Aslam, F., Ferreira, P., Ali, H., & Kauser, S. (2021). Herding behavior during the Covid-19 Pandemic: a comparison between Asian and European stock markets based on intraday multifractality. Eurasian Economic Review, 0123456789.
- Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics, 116(1), 261-292.
- Battaglia, F., & Mazzuca, M. (2014). Securitization and Italian banks’ risk during the crisis. Journal of Risk Finance, 15(4), 458-478.
- Białkowski, J., Gottschalk, K., & Wisniewski, T. P. (2008). Stock market volatility around national elections. Journal of Banking and Finance, 32(9), 1941-1953.
- Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992-1026.
- Business Standard. (2022). BSE investor count touches 100 mn milestone, up 58% in the past one year. Business Standard.
- Cajueiro, D. O., & Tabak, B. M. (2006). Testing for predictability in equity returns for European transition markets. Economic Systems, 30(1), 56-78.
- Caraiani, P., & Cǎlin, A. C. (2020). The impact of monetary policy shocks on stock market bubbles: International evidence. Finance Research Letters, 34, 1-8.
- Cont, Rama, & Bouchaud, J.-P. (2000). Herd behavior and aggregate fluctuations in Financial Markets. Macroeconomic Dynamics, 170-196.
- De Las Nieves López García, M., & Ramos Requena, J. P. (2019). Different methodologies and uses of the Hurst exponent in Econophysics. Estudios de Economia Aplicada, 37(2), 96-108.
- Devenow, A., & Welch, I. (1996). Rational herding in financial economics. European Economic Review, 40, 603-615.
- Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
- Fernández-Martínez, M., Sánchez-Granero, M. A., Muñoz Torrecillas, M. J., & McKelvey, B. (2017). A comparison of three hurst exponent approaches to predict nascent bubbles in S&P500 stocks. Fractals, 25(1), 1-10.
- Ferreruela, S., & Mallor, T. (2021). Herding in the bad times: The 2008 and COVID-19 crises. North American Journal of Economics and Finance, 58(August), 101531.
- Ghosh, B., & Bouri, E. (2022). Long Memory and Fractality in the Universe of Volatility Indices. Complexity, 2022.
- Gopal, S., & Munusamy, J. (2016). Causal Relationship between Gold, Crude Oil & U.S. Dollar Rates and S&P BSE 100 in India: An Experimental Study. International Journal of Financial Management, 6(2).
- Graves, T., Gramacy, R., Watkins, N., & Franzke, C. (2017). A brief history of long memory: Hurst, Mandelbrot and the road to ARFIMA, 1951-1980. Entropy, 19(9), 1-22.
- Hasan, R., & Mohammad, S. M. (2015). Multifractal analysis of Asian markets during 2007-2008 financial crisis. Physica A: Statistical Mechanics and Its Applications, 419, 746-761.
- Hindustan Times. (2021). How did the Indian markets perform in 2021? Hindustan Times.
- Hkiri, B., Bejaoui, A., Gharib, C., & Al Nemer, H. A. (2021). Revisiting efficiency in MENA stock markets during political shocks: evidence from a multi-step approach. Heliyon, 7(9).
- Hurst, H. (1951). Long-Term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116(1).
- Ihlen, E. A. F. (2012). Introduction to multifractal detrended fluctuation analysis in Matlab. Frontiers in Physiology, 3 JUN(June), 1-19.
- Johansen, A., Ledoit, O., & Sornette, D. (2000). Crashes as critical points. International Journal of Theoretical and Applied Finance, 3(2), 219-255.
- Jovanovic, F., & Schinckus, C. (2013). Econophysics: A new challenge for financial economics? Journal of the History of Economic Thought, 35(3), 319-352.
- Kantelhardt, J. W. (2008). Fractal and Multifractal Time Series. Encyclopedia of Complexity and Systems Science, 1-37.
- Kantelhardt, J. W., Zschiegner, S. A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Eugene Stanley, H. (2002). Multifractal detrended fluctuation analysis of nonstationary time series Jan. Physica A, 87-114.
- Lee, H., Song, J. W., & Chang, W. (2016). Multifractal Value at Risk model. Physica A: Statistical Mechanics and Its Applications, 451(xxxx), 113-122.
- Lobato, I. N., & Velasco, C. (2000). Long memory in stock-market trading volume. Journal of Business and Economic Statistics, 18(4), 410-427.
- Lux, T., & Sornette, D. (2002). On Rational Bubbles and Fat Tails. Journal of Money, Credit, and Banking.
- Makololo, P., & Seetharam, Y. (2020). The effect of economic policy uncertainty and herding on leverage: An examination of the BRICS countries. Cogent Economics and Finance, 8(1).
- Mandelbrot, B. B. (1999). A Multifractal walk down Wall Street. Scientific American, 280(2), 70-73.
- Mandelbrot, B. B., Fisher, A., & Calvet, L. (1997). A Multifractal Model of Asset Returns. Cowles Foundation Discussion Paper, 1164.
- Mantegna, R. N., & Stanley, E. H. (1999). Introduction to Econophysics: correlations and complexity in finance. Cambridge University Press.
- Milos, L. R., Hatiegan, C., Milos, M. C., Barna, F. M., & Botoc, C. (2020). Multifractal detrended fluctuation analysis (MF-DFA) of stock market indexes. Empirical evidence from seven central and eastern European markets. Sustainability (Switzerland), 12(2).
- Patil, A. C., & Rastogi, S. (2020). Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis. Journal of Risk and Financial Management, 13(10), 248.
- Peters, E. E., Peters, E. R., & Peters, D. (1994). Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. Wiley.
- Salisu, A. A., Sikiru, A. A., & Vo, X. V. (2020). Pandemics and the emerging stock markets. Borsa Istanbul Review, 20, S40-S48.
- Sornette, D. (2003). Critical market crashes. Physics Reports, 378(1), 1-98.
- Suárez-García, P., & Gómez-Ullate, D. (2014). Multifractality and long memory of a financial index. Physica A: Statistical Mechanics and Its Applications, 394, 226-234.
- Suhaibu, I., Harvey, S. K., & Amidu, M. (2017). The impact of monetary policy on stock market performance: Evidence from twelve (12) African countries. Research in International Business and Finance, 42(12), 1372-1382.
- Summers, L. H. (1986). Does the Stock Market Rationally Reflect Fundamental Values? The Journal of Finance, XLI(3).
- Taleb, N. N. (2007). Black Swans and the domains of statistics. The American Statistician, 61(3), 198-200.
- Trueman, B. (1994). Analysts forecasts and Herding Behavior.
- Wong, W. K., & McAleer, M. (2009). Mapping the Presidential Election Cycle in U.S. stock markets. Mathematics and Computers in Simulation, 79(11), 3267-3277.
-
Not dead yet: the rise, fall and persistence of the BCG Matrix
Problems and Perspectives in Management Volume 15, 2017 Issue #1 pp. 19-34 Views: 4471 Downloads: 2225 TO CITE АНОТАЦІЯThe BCG Matrix was introduced almost 50 years ago, and is today considered one of the most iconic strategic planning techniques. Using management fashion theory as a theoretical lens, this paper examines the historical rise, fall and persistence of the BCG Matrix. The analysis highlights the role played by fashion-setting actors (e.g., consultants, business schools and business media) in the rise of the BCG Matrix. However, over time, portfolio planning models such as the BCG Matrix were attacked and discredited by a host of different actors, and gradually fell out of favor. Even though the BCG Matrix has fallen from grace, it is still alive and has left an imprint on management education and practice. Despite being largely discredited in academic circles, many practitioners still view it as an important corporate portfolio planning technique.
-
Predictability and herding of bourse volatility: an econophysics analogue
Bikramaditya Ghosh 
,
Krishna M.C. ,
Shrikanth Rao ,
Emira Kozarević ,
Rahul Kumar Pandey
doi: http://dx.doi.org/10.21511/imfi.15(2).2018.28
Investment Management and Financial Innovations Volume 15, 2018 Issue #2 pp. 317-326 Views: 1975 Downloads: 191 TO CITE АНОТАЦІЯFinancial Reynolds number works as a proxy for volatility in stock markets. This piece of work helps to identify the predictability and herd behavior embedded in the financial Reynolds number (time series) series for both CNX Nifty Regular and CNX Nifty High Frequency Trading domains. Hurst exponent and fractal dimension have been used to carry out this work. Results confirm conclusive evidence of predictability and herd behavior for both the indices. However, it has been observed that CNX Nifty High Frequency Trading domain (represented by its corresponding financial Reynolds number) is more predictable and has traces of significant herd behavior. The pattern of the predictability has been found to follow a quadratic equation.
-
State debt assessment and forecasting: time series analysis
Fedir Zhuravka ,
Hanna Filatova ,
Petr Šuleř ,
Tomasz Wołowiec
doi: http://dx.doi.org/10.21511/imfi.18(1).2021.06
Investment Management and Financial Innovations Volume 18, 2021 Issue #1 pp. 65-75 Views: 1897 Downloads: 621 TO CITE АНОТАЦІЯOne of the pressing problems in the modern development of the world financial system is an excessive increase in state debt, which has many negative consequences for the financial system of any country. At the same time, special attention should be paid to developing an effective state debt management system based on its forecast values. The paper is aimed at determining the level of persistence and forecasting future values of state debt in the short term using time series analysis, i.e., an ARIMA model. The study covers the time series of Ukraine’s state debt data for the period from December 2004 to November 2020. A visual analysis of the dynamics of state debt led to the conclusion about the unstable debt situation in Ukraine and a significant increase in debt over the past six years. Using the Hurst exponent, the paper provides the calculated value of the level of persistence in time series data. Based on the obtained indicator, a conclusion was made on the confirmation of expediency to use autoregressive models for predicting future dynamics of Ukraine’s state debt. Using the EViews software, the procedure for forecasting Ukraine’s state debt by utilizing the ARIMA model was illustrated, i.e., the series was tested for stationarity, the time series of monthly state debt data were converted to stationary, the model parameters were determined and, as a result, the most optimal specification of the ARIMA model was selected.
