Investigating Chaos on the Johannesburg Stock Exchange
Abstract
This study investigates the existence of chaos on the Johannesburg Stock Exchange (JSE) and studies three indices namely the FTSE/JSE All Share, FTSE/JSE Top 40 and FTSE/JSE Small Cap. Building upon the Fractal Market Hypothesis to provide evidence on the behavior of returns time series of the above mentioned indices, the BDS test is applied to test for non-random chaotic dynamics and further applies the rescaled range analysis to ascertain randomness, persistence or mean reversion on the JSE. The BDS test shows that all the indices examined in this study do not exhibit randomness. The FTSE/JSE All Share Index and the FTSE/JSE Top 40 exhibit slight reversion to the mean whereas the FTSE/JSE Small Cap exhibits significant persistence and appears to be less risky relative to the FTSE/JSE All Share and FTSE/JSE Top 40contrary to the assertion that small cap indices are riskier than large cap indices.Downloads
References
Adelegan, O. J. (2003). Capital market efficiency and the effects of dividend announcements on share prices in Nigeria. African Development Review, 15(2-3), 218-236. Adelegan, O. J. (2009). Price reactions to dividend announcements on the Nigerian stock market (No. RP_188). Kenya: African Economic Research Consortium. DOI: https://doi.org/10.1111/j.1467-8268.2003.00072.x
African Securities Exchanges Association. (2016). Johannesburg Stock Exchange. Retrieved 25 July, 2016, from http://www.african-exchanges.org/members/jse-ltd Bendel, D., Smit, E. & Hamman, W. (1996). Some evidence of persistence in South African financial time series. Studies in Economics and Econometrics, 20(1), 59-83.
Bhattacharya, A. & Sensarma, R. (2006). Do financial markets exhibit chaotic behavior? Evidence from an emerging economy. Retrieved December 15, 2014, fromhttp://www.igidr.ac.in/conf/money/mfc_08/Do%20Financial%20Markets%20Exihibit...Ayan%20B.&%20Rudra%20Sen%20Sarma.pdf
Brock, W. A., Dechert, W. D., Scheinkman, J. A. & LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews, 15, 197-235. DOI: https://doi.org/10.1080/07474939608800353
Buchanan, M. (2013). Forecast: What Physics, Meteorology, and the Natural Sciences can teach us about Economics. London: Bloomsbury.
Corhay, A. & Rad, A. T. (1994). Statistical properties of daily returns: Evidence from European stock markets. Journal of Business Finance & Accounting, 21, 271-282. DOI: https://doi.org/10.1111/j.1468-5957.1994.tb00318.x
Credit Suisse (2014). European small and mid-caps - only those who look closely are able to unearth true market potential. Retrieved March 15, 2015, fromhttps://www.credit-suisse.com/media/production/asset-management/docs/en/small-and-mid-caps-2014-en.pdf
Fama, E. F. (1965). The behavior of stock-market prices. The Journal of Business, 38, 34-105. DOI: https://doi.org/10.1086/294743
Foley, S. (2014). Fewer active managers beat market than any time in decade. Financial Times Retrieved February 10, 2015, fromhttp://www.ft.com/intl/cms/s/0/5e0bda82-d2f9-11e3-9b72-00144feabdc0.html?siteedition=intl
Fundamental Index. (2008). Indexing in inefficient markets. Retrieved February 10, 2015, from https://www.researchaffiliates.com/Production%20content%20library/F_2008_April_Indexing_in_Inefficient_Markets.pdf
Hand, D. J. (2014). The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day, New York: Macmillan. Harvey, C. R., Liu, Y. & Zhu, H. (2015). ... And the cross-section of expected returns. Review of Financial Studies, 5(9).
Howe, J. S., Martin, W. D. & Wood, B. G., Jr (1997). Fractal structure in the Pacific Rim. In Southwestern Finance Association Conference.
Hurst, H. E. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116, 770-808.
Jefferis, K. & Smith, G. (2005). The changing efficiency of African stock markets. South African Journal of Economics, 73, 54-67. DOI: https://doi.org/10.1111/j.1813-6982.2005.00004.x
JSE. (2013). JSE Overview. Retrieved July 25, 2016, fromhttps://www.jse.co.za/about/history-company-overview
Kuppor, S. (2013). To create jobs, the stock market needs a little inefficiency. Retrieved January 10, 2015, fromhttp://fortune.com/2013/11/11/to-create-jobs-the-stock-market-needs-a-little-inefficiency
Mandelbrot, Ð’. & Hudson, R. (2005). The misbehavior of markets: A fractal view of financial turbulence.Basic Books.
Mansukhani, S. (2012). Predictability of time series. Analytics. Maryland: INFORMS. Mclean, R. D. & Pontiff, J. (2016). Does academic research destroy stock return predictability? The Journal of Finance, 71, 5-32. DOI: https://doi.org/10.1111/jofi.12365
Mehta, M. (1995). Foreign Exchange Markets, in A. N. Refenes, ed., Neural Networks in Capital Markets. Chichester, England: John Wiley.
NPR. (2013). What's a bubble? Planet Money. Retrieved March 15 2015, from http://www.npr.org/sections/money/2013/11/01/242351065/episode-493-whats-a-bubble-nobel-edition.
Oppong, K. K., Mulholland, G. & Fox, A. F. (1999). The behavior of some UK equity indices: An application of Hurst and BDS tests. Journal of empirical finance, 6, 267-282. Peters, E. E. (1994). Fractal Market Analysis. New York: John Wiley and Sons. Peters, E. E. (1996). Chaos and Order in the Capital Markets: A New View of Cycles, Prices, and Market Volatility, New York: John Wiley & Sons. DOI: https://doi.org/10.1016/S0927-5398(99)00004-3
Smith, G. (2008). Liquidity and the informational efficiency of African stock markets. South African Journal of Economics, 76, 161-175. DOI: https://doi.org/10.1111/j.1813-6982.2008.00171.x
Sterge, A. J. (1989). On the distribution of financial futures price changes. Financial Analysts Journal, 45, 75-78. DOI: https://doi.org/10.2469/faj.v45.n3.74
Velasquéz, T. (2009). Chaos theory and the science of fractals, and their application in risk management. (Master’s thesis, Copenhagen Business School). Retrieved January 16, 2015, from http://studenttheses.cbs.dk/bitstream/handle/10417/804/Tania_velasquez.pdf?sequence=3.
Voss, J. (2013). Rescaled range analysis: A method for detecting randomness, persistence or mean reversion in financial markets. Enterprising Investor. Retrieved January 20, 2015, from http//: blogs.cfainstitute.org/investor/2013/01/30/rescaled-range-analysis-a-method-of-detecting-persistence-randomness-or-mean-reversion-in-financial-markets.
World Federation of Exchanges (2016). Monthly Reports. Retrieved July 25, 2016, fromhttp://www.world-exchanges.org/home/index.php/statistics/monthly-reports. Zivot, E. & Wang, J. (2006). Modeling financial time series with S-Plus®, New York: Springer-Verlag.
Copyright (c) 2016 Journal of Economics and Behavioral Studies
This work is licensed under a Creative Commons Attribution 4.0 International License.
Author (s) should affirm that the material has not been published previously. It has not been submitted and it is not under consideration by any other journal. At the same time author (s) need to execute a publication permission agreement to assume the responsibility of the submitted content and any omissions and errors therein. After submission of a revised paper in the light of suggestions of the reviewers, editorial team edits and formats manuscripts to bring uniformity and standardization in published material.
This work will be licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) and under condition of the license, users are free to read, copy, remix, transform, redistribute, download, print, search or link to the full texts of articles and even build upon their work as long as they credit the author for the original work. Moreover, as per journal policy author (s) hold and retain copyrights without any restrictions.