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Shenzhen, China

Gu G.-F.,East China University of Science and Technology | Ren F.,East China University of Science and Technology | Ni X.-H.,East China University of Science and Technology | Chen W.,Shenzhen Stock Exchange | And 2 more authors.
Physica A: Statistical Mechanics and its Applications | Year: 2010

We study the statistical regularities of an opening call auction using the ultra-high-frequency data of 22 liquid stocks traded on the Shenzhen Stock Exchange in 2003. The distribution of the relative price, defined as the relative difference between the order price in the opening call auction and the closing price on the last trading day, is asymmetric and that the distribution displays a sharp peak at the zero relative price and a relatively wide peak at the negative relative price. The detrended fluctuation analysis (DFA) method is adopted to investigate the long-term memory of relative order prices. We further study the statistical regularities of order sizes in the opening call auction, and observe a phenomenon of number preference, known as order size clustering. The probability density function (PDF) of order sizes could be well fitted by a q-Gamma function, and the long-term memory also exists in order sizes. In addition, both the average volume and the average number of orders decrease exponentially with the price level away from the best bid or ask price level in the limit-order book (LOB) established immediately after the opening call auction, and a price clustering phenomenon is observed. © 2009 Elsevier B.V. All rights reserved. Source


Ni X.-H.,East China University of Science and Technology | Jiang Z.-Q.,East China University of Science and Technology | Gu G.-F.,East China University of Science and Technology | Ren F.,East China University of Science and Technology | And 3 more authors.
Physica A: Statistical Mechanics and its Applications | Year: 2010

The order submission and cancelation processes are two crucial aspects in the price formation of stocks traded in order-driven markets. We investigate the dynamics of order cancelation by studying the statistical properties of inter-cancelation durations, defined as the waiting times between consecutive order cancelations of 22 liquid stocks traded on the Shenzhen Stock Exchange of China in year 2003. Three types of cancelations are considered, including cancelation of any limit orders, of buy limit orders and of sell limit orders. We find that the distributions of the inter-cancelation durations of individual stocks can be well modeled by Weibulls for each type of cancelation, and the distributions of rescaled durations of each type of cancelations exhibit a scaling behavior for different stocks. Complex intra-day patterns are also unveiled in the inter-cancelation durations. The detrended fluctuation analysis (DFA) and the multifractal DFA show that the inter-cancelation durations possess long-term memory and multifractal nature, which are not influenced by the intra-day patterns. No clear crossover phenomenon is observed in the detrended fluctuation functions with respect to the time scale. These findings indicate that the cancelation of limit orders is a non-Poisson process, which has potential worth in the construction of order-driven market models. © 2010 Elsevier B.V. All rights reserved. Source


Chen K.,South University of Science and Technology of China | Li X.,City University of Hong Kong | Xu B.,Shenzhen Stock Exchange | Yan J.,University of Zurich | Wang H.,South University of Science and Technology of China
Enterprise Information Systems | Year: 2015

Market surveillance systems have increasingly gained in usage for monitoring trading activities in stock markets to maintain market integrity. Existing systems primarily focus on the numerical analysis of market activity data and generally ignore textual information. To fulfil the requirements of information-based surveillance, a multi-agent-based architecture that uses agent intercommunication and incremental learning mechanisms is proposed to provide a flexible and adaptive inspection process. A prototype system is implemented using the techniques of text mining and rule-based reasoning, among others. Based on experiments in the scalping surveillance scenario, the system can identify target information evidence up to 87.50% of the time and automatically identify 70.59% of cases depending on the constraints on the available information sources. The results of this study indicate that the proposed information surveillance system is effective. This study thus contributes to the market surveillance literature and has significant practical implications. © 2015 Taylor & Francis Source


Liu J.-Y.,Zhengzhou University | Li C.-Y.,Shenzhen Stock Exchange | Jian Z.-H.,Huazhong University of Science and Technology
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | Year: 2015

Considering systematic jumps and heterogeneity jumps as tail events, we discuss the tail characteristics of distribution of stock return from the perspective of the extreme value theory. We use time of day (TOD) method to eliminate intraday effect of high-frequency data, apply index-stock method to decompose systematic jumps and heterogeneity jumps, and the peak over threshold (POT) method to estimate the left tail and right tail parameters. Empirical studies show that the intraday effect of A-share market possesses apparent “L” type feature. Each stock exists significant systematic and heterogeneity jumps. And the tails of two jump types are obvious thick. The times and contributions of right tail jumps are larger than left in all stocks. This suggests that the frequent appearance of jumps and jump tail characteristics are an important reason for non-normal distribution of stock return. ©, 2015, Systems Engineering Society of China. All right reserved. Source


Wan Y.-L.,East China University of Science and Technology | Xie W.-J.,East China University of Science and Technology | Gu G.-F.,East China University of Science and Technology | Jiang Z.-Q.,East China University of Science and Technology | And 4 more authors.
PLoS ONE | Year: 2015

Price limit trading rules are adopted in some stock markets (especially emerging markets) trying to cool off traders' short-term trading mania on individual stocks and increase market efficiency. Under such a microstructure, stocks may hit their up-limits and down-limits from time to time. However, the behaviors of price limit hits are not well studied partially due to the fact that main stock markets such as the US markets and most European markets do not set price limits. Here, we perform detailed analyses of the high-frequency data of all A-share common stocks traded on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2000 to 2011 to investigate the statistical properties of price limit hits and the dynamical evolution of several important financial variables before stock price hits its limits. We compare the properties of up-limit hits and down-limit hits. We also divide the whole period into three bullish periods and three bearish periods to unveil possible differences during bullish and bearish market states. To uncover the impacts of stock capitalization on price limit hits, we partition all stocks into six portfolios according to their capitalizations on different trading days. We find that the price limit trading rule has a cooling-off effect (object to the magnet effect), indicating that the rule takes effect in the Chinese stock markets. We find that price continuation is much more likely to occur than price reversal on the next trading day after a limit-hitting day, especially for down-limit hits, which has potential practical values for market practitioners. © 2015 Wan et al. Source

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