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Is the efficiency of stock market correlated with multifractality? An evidence from the Shanghai stock market

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  • Gu, Rongbao
  • Shao, Yanmin
  • Wang, Qingnan

Abstract

In this paper, we propose an efficiency index and multifractality degree for financial markets, and investigate the dynamics of the relationship between the two indices for the Shanghai stock market employing the technique of rolling window. By using the DCCA cross-correlation coefficient, we find that, for the Shanghai stock market, the increase in the degree of market multifractality can lead to a lower degree of market efficiency before the equity division reforms, whereas it can result in a lower degree of market efficiency in the short-term and a higher degree of market efficiency in the long-term after the equity division reforms. This finding reflects the process of development of the Shanghai stock market and also provides strong evidence which supports Liu’s argument that the increase in the degree of market complexity can improve the market efficiency Liu (2009) [1].

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  • Gu, Rongbao & Shao, Yanmin & Wang, Qingnan, 2013. "Is the efficiency of stock market correlated with multifractality? An evidence from the Shanghai stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(2), pages 361-370.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:2:p:361-370
    DOI: 10.1016/j.physa.2012.09.008
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    1. Podobnik, Boris & Fu, Dongfeng & Jagric, Timotej & Grosse, Ivo & Eugene Stanley, H., 2006. "Fractionally integrated process for transition economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(2), pages 465-470.
    2. Cajueiro, Daniel O. & Tabak, Benjamin M., 2006. "Testing for predictability in equity returns for European transition markets," Economic Systems, Elsevier, vol. 30(1), pages 56-78, March.
    3. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo, 2008. "Short-term predictability of crude oil markets: A detrended fluctuation analysis approach," Energy Economics, Elsevier, vol. 30(5), pages 2645-2656, September.
    4. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    5. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    6. Tabak, Benjamin M. & Cajueiro, Daniel O., 2007. "Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility," Energy Economics, Elsevier, vol. 29(1), pages 28-36, January.
    7. Mandelbrot, Benoit B, 1971. "When Can Price Be Arbitraged Efficiently? A Limit to the Validity of the Random Walk and Martingale Models," The Review of Economics and Statistics, MIT Press, vol. 53(3), pages 225-236, August.
    8. Eom, Cheoljun & Choi, Sunghoon & Oh, Gabjin & Jung, Woo-Sung, 2008. "Hurst exponent and prediction based on weak-form efficient market hypothesis of stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(18), pages 4630-4636.
    9. Cajueiro, Daniel O & Tabak, Benjamin M, 2004. "The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 521-537.
    10. Wang, Yudong & Wei, Yu & Wu, Chongfeng, 2010. "Auto-correlated behavior of WTI crude oil volatilities: A multiscale perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5759-5768.
    11. Zebende, G.F., 2011. "DCCA cross-correlation coefficient: Quantifying level of cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 614-618.
    12. Cajueiro, Daniel O. & Tabak, Benjamin M., 2004. "Evidence of long range dependence in Asian equity markets: the role of liquidity and market restrictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 342(3), pages 656-664.
    13. Zunino, L. & Tabak, B.M. & Figliola, A. & Pérez, D.G. & Garavaglia, M. & Rosso, O.A., 2008. "A multifractal approach for stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6558-6566.
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