4.6 Article

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

Journal

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.physa.2019.121305

Keywords

MF-DFA; Stock market; Multifractality degree; Market efficiency; Stock market crash

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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. (C) 2019 Elsevier B.V. All rights reserved.

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