Informed Trading and Liquidity in the Shanghai Stock Exchange

Wong, Woon K.; Tan, Dijun; Tian, Yuan

doi:10.2139/ssrn.1084794

Cited by 4 publications

(4 citation statements)

References 46 publications

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“…individuals) from participating in the market further, in the context of Foster and Viswanathan (1990) price formation model. In the Chinese market, Wong, Tan, and Tian (2009) report an increase in informed trading in both liquid and illiquid stocks during periods when equity markets are active, consistent with the findings of Dufour and Engle (2000) for NYSE exchange. Nevertheless, in the context of Statman et al (2006), this 7 Graphs are not reported but are available upon request.…”

Section: Aggregate Trading Volume and Past Day Returns In Chinasupporting

confidence: 76%

Buy and sell dynamics following high market returns: Evidence from China

Wongchoti

Young

2009

International Review of Financial Analysis

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Section: Aggregate Trading Volume and Past Day Returns In Chinasupporting

confidence: 76%

Buy and sell dynamics following high market returns: Evidence from China

Wongchoti

Young

2009

International Review of Financial Analysis

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“…To identify two alternative regimes, we define the dummy variable i ϭ 1 when the number of transactions in the spell x i is larger than the average of the whole series; this almost splits the distribution of the number of transactions per spell into two fairly equal partitions [6]. Our strategy partially follows that of Wong et al (2008), although their paper investigates a completely different hypothesis. We make use of i to propose the following nonlinear specification of i :…”

Section: Traders and Timementioning

confidence: 99%

“…Most of the time, this approach is applied to the so-called aggregated durations (e.g. volume or price durations) because of their relationship with some market features Wong et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Traders and time: who moves the market?

Ferriani

2015

Studies in Economics and Finance

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Purpose – This paper is aimed to investigate the impact of different categories of traders on price and volume durations at Euronext Paris. The two series are respectively related to the instantaneous volatility and the market liquidity; hence, they are particularly suited to test microstructure hypotheses. Design/methodology/approach – A Log-autoregressive conditional duration model was adopted to include the information on the traders’ identity at the transaction level. High-frequency data were used and how the informed traders and the liquidity provider affect the arrival of market events was studied. The robustness of our results was also checked by testing different distributions and controlling for microstructure effects. Findings – It was found that informed traders and the liquidity provider exert a dominant role in accelerating the market activity. This result depends on the state of the market, i.e. it is effective only during periods of high frequency of transactions. The estimates for price durations show that a high instantaneous volatility can be mainly ascribed to a great concentration of informed traders. Informed traders are also found to shorten volume durations by clustering small-size orders to disguise their private signal. For both durations, the liquidity provider is also found to foster the market activity, likely because of his contractual duties. Originality/value – The article is of interest for researchers in the field of market microstructure, as well as for specialists in the high-frequency trading. Results provide an empirical confirmation of information models which theorize an accelerating effect for informed trading. To the best of the authors’ knowledge, this is the first contribution to study the impact of traders’categories at the transaction level and with different definitions of durations.

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“…Additional duration and market microstructure variables were appended to the model with the intention of jointly evaluating their impact. This approach has been successfully used in many previous studies (see Engle (2000), Bauwens and Giot (2000) and Wong, Tan, and Tian (2009)). By including these new variables into the ACD framework, the conditional return variance was given by:…”

Section: The Autoregressive Conditional Duration (Acd) Modelmentioning

confidence: 99%