Power–law properties of Chinese stock market

Yan, Chao; Zhang, J.W.; Zhang, Y.; Tang, Ya

doi:10.1016/j.physa.2005.02.010

Cited by 31 publications

(16 citation statements)

References 18 publications

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“…Further research showed that the returns have power-law tails with the exponent close to three, known as the inverse cubic law [12][13][14][15]. A number of empirical investigations have been conducted on financial returns at different time scales in different stock markets over different time periods, and the distributions are found to have power-law tails with different tail exponents at short time intervals [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31], which can be explained by the variational theory for turbulent signals [32,33]. In addition, it has been shown that the exponent values are universal for three mature markets that do not display variations with respect to market capitalization or industrial sector [34,35], which is however not the case for emerging markets [36].…”

Section: Introductionmentioning

confidence: 99%

Determinants of immediate price impacts at the trade level in an emerging order-driven market

Zhou¹

2012

New J. Phys.

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Common wisdom argues that, in general, large trades cause large price changes, whereas small trades cause small price changes. However, for extremely large price changes, the trade size and news play a minor role, while liquidity (especially price gaps on the limit order book) is a more influential factor. Hence, there might be other factors influencing the immediate price impacts of trades. In this paper, through mechanical analysis of price variations before and after a trade of arbitrary size, we identify that the trade size, the bid-ask spread, the price gaps and the outstanding volumes at the bid and ask sides of the limit order book have an impact on the changes in prices. We propose two regression models to investigate the influence of these microscopic factors on the price impact of buyer-initiated partially filled trades, seller-initiated partially filled trades, buyer-initiated filled trades and seller-initiated filled trades. We find that they have quantitatively similar explanatory powers and these factors can account for up to 44% of the price impacts. Large trade sizes, wide bid-ask spreads, high liquidity at the same side and low liquidity at the opposite side will cause a large price impact. We also find that the liquidity at the opposite side has a more influential impact than the liquidity at the same side. Our results shed new light on the determinants of immediate price impacts.

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Section: Introductionmentioning

confidence: 99%

Determinants of immediate price impacts at the trade level in an emerging order-driven market

Zhou¹

2012

New J. Phys.

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“…investigate the daily returns of 104 stocks (76 from the Shanghai Stock Exchange and 28 from Shenzhen Stock Exchange) in the Chines stock markets in the period from 1994 to 2001 and argue that the tail exponent is α + = 2.44 for the positive part and α − = 4.29 for the negative part [26]. After removing the opening and close returns of high-frequency data for the Shanghai Stock Exchange Composite index, the tail exponents are much closer to α = 3 [27].…”

Section: Introductionmentioning

confidence: 99%

Empirical distributions of Chinese stock returns at different microscopic timescales

Wei

Zhou

2008

Physica A: Statistical Mechanics and its Applications

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We study the distributions of event-time returns and clock-time returns at different microscopic timescales using ultra-high-frequency data extracted from the limit-order books of 23 stocks traded in the Chinese stock market in 2003. We find that the returns at the one-trade timescale obey the inverse cubic law. For larger timescales (2-32 trades and 1-5 minutes), the returns follow the Student distribution with power-law tails. With the decrease of timescale, the tail becomes fatter, which is consistent with the vibrational theory.

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“…Also, the grouping of companies in the MST can be identified and extended to portfolio optimisation, and the companies of the US market are clearly grouped with the industry category or business sector [11]. Several papers show that the characteristics of a mature market cannot be simply extended to an emerging market [12,13], and that they change dynamically due to the globalization [14]. Especially, the Korean market is synchronized to external markets due to globalization, and the tendency of grouping by industry categories has disappeared [15].…”

Section: Introductionmentioning

confidence: 99%

Group dynamics of the Japanese market

Jung

Wang

Kaizoji

et al. 2008

Physica A: Statistical Mechanics and its Applications

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We investigated the network structures of the Japanese stock market through the minimum spanning tree. We defined grouping coefficient to test the validity of conventional grouping by industrial categories, and found a decreasing in trend for the coefficient. This phenomenon supports the increasing external influences on the market due to the globalization. To reduce this influence, we used S&P500 index as the international market and removed its correlation with every stock. We found stronger grouping in this measurement, compared to the original analysis, which agrees with our assumption that the international market influences to the Japanese market.Comment: 9 page

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Power–law properties of Chinese stock market

Cited by 31 publications

References 18 publications

Determinants of immediate price impacts at the trade level in an emerging order-driven market

Determinants of immediate price impacts at the trade level in an emerging order-driven market

Empirical distributions of Chinese stock returns at different microscopic timescales

Group dynamics of the Japanese market

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