Peculiar statistical properties of Chinese stock indices in bull and bear market phases

Zhou, Wei; Xu, Haiyan; Cai, Zhiye; Wei, Jianrong; Zhu, Xiaoyu; Wang, W.; Zhao, Longfeng; Huang, Jiping

doi:10.1016/j.physa.2008.11.028

Cited by 17 publications

(8 citation statements)

References 18 publications

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“…If we define for any positive real number , then has two properties:To comply with our notations, here represents pitch fluctuation . Therefore the positive tail and negative tail of CDF can be calculated separately to make a comparison [22].…”

Section: Resultsmentioning

confidence: 99%

A Statistical Physics View of Pitch Fluctuations in the Classical Music from Bach to Chopin: Evidence for Scaling

et al. 2013

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Because classical music has greatly affected our life and culture in its long history, it has attracted extensive attention from researchers to understand laws behind it. Based on statistical physics, here we use a different method to investigate classical music, namely, by analyzing cumulative distribution functions (CDFs) and autocorrelation functions of pitch fluctuations in compositions. We analyze 1,876 compositions of five representative classical music composers across 164 years from Bach, to Mozart, to Beethoven, to Mendelsohn, and to Chopin. We report that the biggest pitch fluctuations of a composer gradually increase as time evolves from Bach time to Mendelsohn/Chopin time. In particular, for the compositions of a composer, the positive and negative tails of a CDF of pitch fluctuations are distributed not only in power laws (with the scale-free property), but also in symmetry (namely, the probability of a treble following a bass and that of a bass following a treble are basically the same for each composer). The power-law exponent decreases as time elapses. Further, we also calculate the autocorrelation function of the pitch fluctuation. The autocorrelation function shows a power-law distribution for each composer. Especially, the power-law exponents vary with the composers, indicating their different levels of long-range correlation of notes. This work not only suggests a way to understand and develop music from a viewpoint of statistical physics, but also enriches the realm of traditional statistical physics by analyzing music.

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Section: Resultsmentioning

confidence: 99%

A Statistical Physics View of Pitch Fluctuations in the Classical Music from Bach to Chopin: Evidence for Scaling

et al. 2013

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“…Due to the asymmetry and market frictions of market information transmission, the public and private information that should have responded immediately is often lagging and persistent, thus the stocks that constitute the Shanghai Composite Index possess varied information feedback speed, resulting in autocorrelation of the volatility sequence. The autocorrelation curve possesses a significant Zhou et al (2009). Meanwhile, it also finds that the monetary policy affects the strength of volatility autocorrelation, in which the autocorrelation is the strongest under the loose monetary policy period.…”

Section: Autocorrelation Analysis Of Volatilitymentioning

confidence: 90%

“…Among them, the volatility autocorrelation is There is significant autocorrelation in the volatility of the upward, steady and downward trend, which is consistent with the mainstream trend of economists, that is, the volatility of stock price possesses a strong memory. Meanwhile, we found that the volatility autocorrelation curve possesses significant periodicity with K = 1 and k = 100, that is, there is a high correlation between the daily opening price of China's stock market, which may be due to the existence of the intraday model [21]. However, this study attempts to alter the initial values of the samples and respectively takes 9:27, 10:34, 11:39, 13:51, 14:47 and 15:26 as the initial data.…”

Section: Autocorrelation Analysis Of Volatilitymentioning

confidence: 93%

The Influence of Trading Volume, Market Trend, and Monetary Policy on Characteristics of the Chinese Stock Exchange: An Econophysics Perspective

2019

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The paper adopts the financial physics approach to investigate influence of trading volume, market trend, as well as monetary policy on characteristics of the Chinese Stock Exchange. Utilizing 1-minute high-frequency data at various time intervals, the study examines the probability distribution density, autocorrelation and multi-fractal of the Shanghai Composite Index. Our study finds that the scale of trading volume, stock market trends, and monetary policy cycles all exert significant influences on micro characteristics of Shanghai Composite Index. More specifically, under the conditions of large trading volumes, loose monetary policies, and downward stock trends, the market possesses better fitting on Levy’s distribution, the volatility self-correlation is stronger, and multifractal trait is more salient. We hope our study could provide better guidance for investment decisions, and form the basis for policy formulation aiming for a healthy growth of the financial market.

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“…Zhou, Xu, Cai et al [6] point out the statistical features for Chinese stock market by focusing the data spanning from January 2006 to October 2007, which was a big bull market and from January 2001 to December 2005, which was a big bear market. By using the statistical indices of log-return r(t) for both markets, three important features were found and these statistical features help understanding Chinese economic systems.…”

Section: Chinese Stock Marketsmentioning

confidence: 99%

Efficiency Frontier for Chinese Stock Markets under Influence of Financial Crises

Guo¹

2011

Lecture Notes in Electrical Engineering

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Financial crises, like Asian financial crisis in 1997 and credit crunch from USA in 2007, have huge influence worldwide. For the importance and uniqueness of China's stock market, it is interesting and attractive to research it under influence of financial crises. The purpose for this research is to find out the influence of financial crises on Chinese stock market and focus on efficiency frontier. For research methodology, the efficiency frontiers are built using cross-sectional data through linear programming with an optimization model for different periods for Chinese stock markets portfolios and for a portfolio from Chinese, UK and USA stock markets together under current financial crisis. This research finds out that Chinese stock markets have a positive risk return trade off and it varies under the influence of financial crises; the investment efficiency in Chinese stock markets are relative good currently and if further diversified with western markets, higher investment efficiency can be achieved. The implication for this paper is to offer a suitable method to construct Chinese stock market efficiency frontier, and to improve investment efficiency through further diversification. This paper enjoys the originality that it perhaps is the first paper to build Chinese stock market efficiency frontier through linear programming.

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Peculiar statistical properties of Chinese stock indices in bull and bear market phases

Cited by 17 publications

References 18 publications

A Statistical Physics View of Pitch Fluctuations in the Classical Music from Bach to Chopin: Evidence for Scaling

A Statistical Physics View of Pitch Fluctuations in the Classical Music from Bach to Chopin: Evidence for Scaling

The Influence of Trading Volume, Market Trend, and Monetary Policy on Characteristics of the Chinese Stock Exchange: An Econophysics Perspective

Efficiency Frontier for Chinese Stock Markets under Influence of Financial Crises

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