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Analysis and comparison of the multifractality and efficiency of Chinese stock market: Evidence from dynamics of major indexes in different boards

Author

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  • Han, Chenyu
  • Wang, Yiming
  • Ning, Ye

Abstract

This paper examines the daily return series of five main indexes, including Shanghai Stock Exchange Composite Index(SSE), Shenzhen Stock Exchange Component Index(SZSE), Shanghai Shenzhen 300 Index(SHSE-SZSE300), Small and Medium Enterprise Board Index(SME), and ChiNext Index(ChiNext), in different boards of Chinese stock market from 2000 to 2018 by multifractal detrended fluctuation analysis (MF-DFA). The return series exhibit significant multifractal properties on the whole time scale and ChiNext has the lowest multifractal properties among the five indexes, indicating the highest market efficiency. The multifractal properties of the five indexes are due to both long-range correlation and fat-tail characteristics of non-Gaussian probability density function, and these two factors have different effects on the multifractality of five indexes. The crosscorrelations among different boards of Chinese stock market reflect the internal linkages between different boards. This paper also aims to compare the multifractality degrees of Main-Board, Small and Medium Enterprise Board(SME Board), and Growth Enterprises Market Board(GEM Board) in three sub-samples divided by the 2015 stock market crash and to study its effects on efficiency and risk of these boards in China’s stock market in each sub-sample, from the statistical and fractal perspectives, which has theoretical and practical significance in the application of Effective Market Hypothesis (EMH) in China’s stock market. The findings of the study may also provide important implications for further study on the dynamic mechanism and efficiency in stock market and help regulators and policymakers effectively control the market risk and achieve more effective resource allocation.

Suggested Citation

  • Han, Chenyu & Wang, Yiming & Ning, Ye, 2019. "Analysis and comparison of the multifractality and efficiency of Chinese stock market: Evidence from dynamics of major indexes in different boards," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C).
    • Handle: RePEc:eee:phsmap:v:528:y:2019:i:c:s037843711930768x
    DOI: 10.1016/j.physa.2019.121305
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    Citations

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    Cited by:

    1. OlaOluwa S. Yaya & Oluwasegun B. Adekoya & Xuan Vinh Vo & Mamdouh Abdulaziz Saleh Al‐Faryan, 2024. "Stock Market Efficiency in Asia: Evidence from the Narayan–Liu–Westerlund's GARCH‐based unit root test," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 91-101, January.
    2. Yaya, OlaOluwa S. & Vo, Xuan Vinh & Adekoya, Oluwasegun B., 2021. "Market Efficiency of Asian Stocks: Evidence based on Narayan-Liu-Westerlund GARCH-based Unit root test," MPRA Paper 109828, University Library of Munich, Germany.
    3. Lin Liu & Qiguang Chen, 2020. "How to compare market efficiency? The Sharpe ratio based on the ARMA-GARCH forecast," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-21, December.
    4. Oluwasegun B. Adekoya, 2021. "Persistence and efficiency of OECD stock markets: linear and nonlinear fractional integration approaches," Empirical Economics, Springer, vol. 61(3), pages 1415-1433, September.
    5. Lee, Min-Jae & Choi, Sun-Yong, 2024. "Insights into the dynamics of market efficiency spillover of financial assets in different equity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).

    More about this item

    Keywords

    MF-DFA; Stock market; Multifractality degree; Market efficiency; Stock market crash;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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