An empirical analysis of the equity markets in China

Mookerjee, Rajen; Qiao, Yu

doi:10.1016/s1058-3300(99)00006-3

Cited by 117 publications

(71 citation statements)

References 24 publications

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“…There are universal and idiosyncratic behaviors in the developed and emerging stock markets, such as the log-periodic power-law antibubble pattern [25] or return anomalies [26] in China's stock markets. In this paper, we have verified that the probability density of exit time follows a universal power law with exponent α = 1.5 for large τ ρ for daily indexes and stock prices and 5-min high-frequency data in both the developed and emerging markets.…”

Section: Discussionmentioning

confidence: 99%

Inverse statistics in stock markets: Universality and idiosyncracy

Zhou

Yuan

2005

Physica A: Statistical Mechanics and its Applications

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Investigations of inverse statistics (a concept borrowed from turbulence) in stock markets, exemplified with filtered Dow Jones Industrial Average, S&P 500, and NASDAQ, have uncovered a novel stylized fact that the distribution of exit time follows a power law p(τ ρ ) ∼ τ −α ρ with α ≈ 1.5 at large τ ρ and the optimal investment horizon τ * ρ scales as ρ γ [1,2,3]. We have performed an extensive analysis based on unfiltered daily indices and stock prices and high-frequency (5-min) records as well in the markets all over the world. Our analysis confirms that the power-law distribution of the exit time with an exponent of about α = 1.5 is universal for all the data sets analyzed. In addition, all data sets show that the power-law scaling in the optimal investment horizon holds, but with idiosyncratic exponent. Specifically, γ ≈ 1.5 for the daily data in most of the developed stock markets and the five-minute highfrequency data, while the γ values of the daily indexes and stock prices in emerging markets are significantly less than 1.5. We show that there is of little chance that this discrepancy in γ stems from the difference of record sizes in the two kinds of stock markets. IntroductionEconophysics is an interdisciplinary science which applies statistical physics and complex system theories to economics [4,5,6,7]. More than ten stylized facts of asset returns have been discovered or re-discovered in the community [8], some of which are inspired originally by the analogy between finance markets and turbulence [9,10]. In these works, the asset return, as the counterpart of velocity difference in turbulence, plays a central role, which is defined as the difference of logarithmic prices at a given time lag. Recently, a new stylized fact have been unveiled dealing with the inverse statistics of the exit time in the Dow Jones Industrial Average [1,2,3] and in the foreign exchange markets [11]. Interestingly, this concept of inverse statistics was also borrowed from turbulence [12] and applied in turbulence extensively [13,14,15,16,17,18].For a given series of log prices {s i } where i corresponds to trading days, the exit time (or first passage time) τ at time i for a given return threshold ρ > 0 is defined as the minimal time span needed for the difference of log prices exceeds ρ for the first time. In other words, one says mathematicallyWe see that τ ρ ≥ 1 is integer. If the stock price rises, τ ρ = 1. It is argued that more small τ ρ in a period implies a bullish market while large τ ρ indicates a lasting bearish market. For fractional Brownian motion of Hurst exponent H, Ding and Yang [19] have found that the distribution density p(τ ρ ) scales aswith α = 2 − H, when τ ρ is large. For Brownian motion, the exit time distribution has been solved analytically [20,21,22] where K is the generalized diffusion constant. The most probable exit time τ * ρ (also called the optimal investment horizon in Finance) is thus scaled asThe first passage time was studied in physics, biology and engineering [20,21,22, and r...

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Section: Discussionmentioning

confidence: 99%

Inverse statistics in stock markets: Universality and idiosyncracy

Zhou

Yuan

2005

Physica A: Statistical Mechanics and its Applications

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“…These unique factors make the analysis of seasonalities within this market a valuable and useful exercise. Several studies have described the unique characteristics of the Chinese markets in some detail, notably, Ma (1996), Chui and Kwok (1998), Mookerjee and Yu (1999), Xu (2000), Chen, Kwok and Rui (2001) and Chen, Lee and Rui (2001). The two main institutional aspects pertinent to our study are: (i) the closed nature of the Chinese A stock markets; and (ii) the segmentation of the stock market into A-and B-shares.…”

Section: Institutional Aspects Of Chinese Stock Marketmentioning

confidence: 99%

Seasonalities in China's Stock Markets: Cultural or Structural?

Mitchell

Ong²

2006

IMF Working Papers

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This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate.In this paper, we examine returns in the Chinese A and B stock markets for evidence of calendar anomalies. We find that both cultural and structural (segmentation) factors play an important role in influencing the pricing of both A-and B-shares in China. There is some evidence of a February turn-of-the-year effect, partly owing to the timing of the Chinese Lunar New Year (CNY); and the holiday effect around the CNY period is stronger and more persistent compared with the other public holidays. The segmentation between the two markets is apparent in the day-of-the-week effect, where B stock markets tend to post significant negative returns on Tuesdays, corresponding with overnight developments in the United States, while significant negative returns are observed on Mondays in the A stock markets. Investment strategies based on some of these calendar anomalies, and allowing for transaction costs, suggest that the A stock markets tend to offer more economically significant returns. JEL Classification Numbers: G11, G12, G14, G15

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“…However, in a smaller subset of markets such as Japan, Australia and Turkey, a "Tuesday" effect has also been documented, in which it is the mean Tuesday return that is found to be significantly negative and less than the average returns combined of Wednesdays, Thursdays and Fridays. As for stock market volatility, there is evidence suggesting that the day-ofthe-week effect actually appears in the volatility of stock returns (French and Roll 1996;Viswanathan 1990, 1993;Mookerjee and Yu 1999;Franses and Paap 2000;Berument and Kiymaz 2001;Kiymaz and Berument 2003;Savva et al 2006;French and Roll 1986) find that the variance of returns from Friday close to Monday close is higher than the variance of returns from Monday close to Tuesday close. They explain the variance pattern with the different patterns of private and public information releases.…”

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confidence: 99%

“…Moreover, Foster and Viswanathan (1990) argue that informed traders have more information on Mondays than on other days and thus Mondays are when the variance of price changes are highest. Agrawal and Tandon (1994), Mookerjee and Yu (1999), Franses and Paap (2000) and Savva et al (2006) assess the day-of-the-week effect in stock returns as well as volatilities. When they examine whether volatility is different for each day of week, they use different measurements for volatility.…”

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confidence: 99%