Liquidity Dynamics in an Electronic Open Limit Order Book: an Event Study Approach

The liquidity provision strategies by institutional traders on the ASX have changed over the period 2006 to 2012. Besides using smaller‐sized orders more frequently than their retail counterparts, they have increased the use of passive limit orders. Institutional traders are found to be more sensitive and responsive to changes in market conditions. Analyses on order placement and price impact suggest that institutional traders are better informed. However, their limit orders are found to have a lower price impact at the intra‐day level in the 2012 subsample period. We show evidence this is associated with the proliferation of algorithmic and high frequency trading.

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“…The dynamics of liquidity in an electronic system in the context of market reaction to liquidity shocks is analysed by Gomber et al . ().…”

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confidence: 97%

The Evolution of Informed Liquidity Provision: Evidence from an Order‐driven Market

Wee

Yang

2015

Euro Fin Management

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“…Bid-ask spread (e.g. Benston and Hagerman, 1974;Stoll, 1978;Fleming and Remolona, 1999) and Exchange Liquidity Measure (XLM) (see Cooper, Groth and Avera, 1985;Gomber, Schweickert and Theissen, 2015). AutoRegressive models have been employed to describe the dynamics of the liquidity measures.…”

Section: Vfarrandbidaskcurveplotmentioning

confidence: 99%

Forecasting Limit Order Book Liquidity Supply-Demand Curves with Functional Autoregressive Dynamics

2016

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Limit order book contains comprehensive information of liquidity on bid and ask sides. We propose a Vector Functional AutoRegressive (VFAR) model to describe the dynamics of the limit order book and demand curves and utilize the fitted model to predict the joint evolution of the liquidity demand and supply curves. In the VFAR framework, we derive a closed-form maximum likelihood estimator under sieves and provide the asymptotic consistency of the estimator. In application to limit order book records of 12 stocks in NASDAQ * Corresponding author: Wee Song Chua Email: a0054070@u.nus.edu Phone: +65-6516 3470 Fax: +65-6872 3919 This research was supported by the FRC grant at the National University of Singapore. Support from IRTG 1792 "High Dimensional Non Stationary Time Series", Humboldt-Universität zu Berlin, is gratefully acknowledged. 1 traded from 2 Jan 2015 to 6 Mar 2015, it shows the VAR model presents a strong predictability in liquidity curves, with R 2 values as high as 98.5 percent for insample estimation and 98.2 percent in out-of-sample forecast experiments. It produces accurate 5−, 25− and 50−minute forecasts, with root mean squared error as low as 0.09 to 0.58 and mean absolute percentage error as low as 0.3 to 4.5 percent.

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“…Around large intraday price changes in the Shenzhen Stock Exchange, the volatility, the volume of orders, the bid-ask spread, and the volume imbalance decay slowly as a power law [15]. Differently, There shows fast resiliency to "normal levels" after large transactions in [16]. In addition, all dimensions of liquidity are found to revert to their steady-state values within 15 orders after a very aggressive market order shock, based on a VAR model [17].…”

Section: Introductionmentioning

confidence: 96%

Limit-order book resiliency after effective market orders: spread, depth and intensity

Xu¹,

Chen²,

Xiong³

et al. 2017

J. Stat. Mech.

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Abstract. In order-driven markets, limit-order book (LOB) resiliency is an important microscopic indicator of market quality when the order book is hit by a liquidity shock and plays an essential role in the design of optimal submission strategies of large orders. However, the evolutionary behavior of LOB resilience around liquidity shocks is not well understood empirically. Using order flow data sets of Chinese stocks, we quantify and compare the LOB dynamics characterized by the bid-ask spread, the LOB depth and the order intensity surrounding effective market orders with different aggressiveness. We find that traders are more likely to submit effective market orders when the spreads are relatively low, the same-side depth is high, and the opposite-side depth is low. Such phenomenon is especially significant when the initial spread is 1 tick. Although the resiliency patterns show obvious diversity after different types of market orders, the spread and depth can return to the sample average within 20 best limit updates. The price resiliency behavior is dominant after aggressive market orders, while the price continuation behavior is dominant after less-aggressive market orders. Moreover, the effective market orders produce asymmetrical stimulus to limit orders when the initial spreads equal to 1 tick. Under this case, effective buy market orders attract more buy limit orders and effective sell market orders attract more sell limit orders. The resiliency behavior of spread and depth is linked to limit order intensity.

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Liquidity Dynamics in an Electronic Open Limit Order Book: an Event Study Approach

Cited by 51 publications

References 44 publications

The Evolution of Informed Liquidity Provision: Evidence from an Order‐driven Market

The Evolution of Informed Liquidity Provision: Evidence from an Order‐driven Market

Forecasting Limit Order Book Liquidity Supply-Demand Curves with Functional Autoregressive Dynamics

Limit-order book resiliency after effective market orders: spread, depth and intensity

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