Informed trading and liquidity in the Shanghai Stock Exchange

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

doi:10.1016/j.irfa.2008.11.002

Cited by 17 publications

(11 citation statements)

References 53 publications

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“…This depicts that trading costs and trading activity of the earlier periods define the crucial movements in present-day costs. This mainly arises when informed traders desire to immediately materialize the benefit of their informational advantage [63] and thus a large increase in their trading activity raises the cost of immediate execution. Moreover, a higher intensity in trading smoothens the flow of information among all market participants and thereby enables optimal trading, which further eases the trading costs.…”

Section: Plos Onementioning

confidence: 99%

Measuring liquidity in Indian stock market: A dimensional perspective

2020

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Market liquidity ensures the marketability of security and is an indispensable feature of stock markets. Previous studies have emphasized the role of stock market liquidity in empirical finance. However, they have inadequately explored its multidimensional nature. This study eliminates the ambiguities related to market liquidity by precisely measuring it by using popular and proven liquidity measures. As such, the present study aims to evaluate market liquidity in terms of depth, breadth, tightness, and immediacy in the Indian equity market and also identifies crucial interdependencies between liquidity dimensions. The study selects 500 stocks constituting the NIFTY 500 index of the National Stock Exchange, India, as of 26th May 2019. The data on trading volume, bid price, ask price, the number of shares outstanding, closing share prices were retrieved for the period from 1st April 2009 to 31st March 2019. The study employs Share Turnover, Amihud Illiquidity Ratio, Relative Quoted Spreads, and Coefficient of Elasticity of Trading for liquidity measurement. The Vector Auto-Regressive (VAR) model is used to establish the simultaneous relationships between liquidity dimensions. The analysis is conducted at the aggregate market level as well as across turnover based stock groups divided based on their rankings in terms of stock specific share turnover. The empirical results evidenced the presence of consistent depth, strong breadth, and immediacy but lower tightness in the Indian equity market. The market depth and tightness appear to be relevant in determining dimensional interdependencies. Also, less frequently traded stocks exhibit higher illiquidity in the wake of lower tightness. The findings of this study will assist the investors to wisely understand the multifaceted nature of market liquidity and base their trading decisions accordingly. Moreover, the regulators of the stock exchange can devise liquidity enhancing policies based on the directional movements among liquidity dimensions.

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Section: Plos Onementioning

confidence: 99%

Measuring liquidity in Indian stock market: A dimensional perspective

2020

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“…The fad trading activities also triggered the increased trading volume, namely the action of buying and selling securities (equity) that are not based on facts and the content of accurate information about the value of assets of the object of the transaction or not information-based decision (Benos & Sagade, 2016). Thus, the fad trading factor can trigger excessive stock trading volume both in responding to good or bad information (Wong, Tan, & Tian, 2009). In general, fad trading is caused by uninformed traders who do not know the fair value of the shares traded.…”

Section: Trading Volume and Price Reversalmentioning

confidence: 99%

Determinants of price reversal in high-frequency trading: empirical evidence from Indonesia

Santosa

2020

Investment Management and Financial Innovations

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This article analyzes whether the factors of the mechanism of high-frequency trading (HFT) or intraday trading affect the process of price reversal and continuation. The price reversal phenomenon is gaining importance rapidly due to the increasingly intensive use of IT/Fintech-based trading automation facilities on the Indonesia Stock Exchange. However, one knows little about how their trading affects volatility and liquidity pressures that cause price reversals. A new research approach uses the factors of market microstructure mechanism based on high-frequency data (HFD-intraday). The research method uses purposive random sampling, which classified price fractions into three groups, specifically low price, medium price, and high price, which are analyzed by logistic panel regression. The research variables used include price reversal (dependent), stock return, trading volume, transaction frequency, volume/frequency (V/F) proxy, volatility, and liquidity. According to low price model research findings, all variables show a significant effect on price reversal; for medium price model, all variables except liquidity show a significant effect on price reversal; and for high price model, all variables have a significant effect on price reversal, except trading volume and volatility. In conclusion, low price shares tend to have higher price reversal probability compared to continuity because they tend to be liquid, low institutional ownership, and minimal reporting/analysis and are controlled by HFTs (uninformed traders). Some variables are not significant because of the bounce effect around the bid-ask spread. AcknowledgmentMany thanks to Armida S. Alisjahbana, Roy H. Sembel, Budiono, Rahardi S. Rahmanto, and the anonymous referee/reviewer for valuable inputs and feedback.

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“…As forecasted by a work of literature on that topic, e.g. Brockman & Chung (1999);; 2003; Easley et al (2008); Wong, Tan & Tian (2009); we consider the impact of informed trading on intraday liquidity: the bid-ask spread and the bid and ask depth. Accordingly, we are able to assess how the continually changing 'information state' of the EUR/PLN market impacts the behavior of FX dealers and as a result, how it changes the liquidity of the market.…”

Section: Introductionmentioning

confidence: 99%

Informed and Uninformed Trading in the EUR/PLN Spot Market

Bien

2011

SSRN Journal

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* The author wants to thank the Thomson Reuters for providing the data from the Reuters Dealing 3000 Spot Matching system. Helpful comments by Piotr Odrzywolek, Pawel Sobolewski and Dobieslaw Tymoczko are fully acknowledged. Any remaining errors are the responsibility of the author. The views and opinions presented herein are those of the author and do not have to necessarily reflect those of the National Bank of Poland.

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Informed trading and liquidity in the Shanghai Stock Exchange

Cited by 17 publications

References 53 publications

Measuring liquidity in Indian stock market: A dimensional perspective

Measuring liquidity in Indian stock market: A dimensional perspective

Determinants of price reversal in high-frequency trading: empirical evidence from Indonesia

Informed and Uninformed Trading in the EUR/PLN Spot Market

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