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The causal impact of algorithmic trading on market quality

Nidhi Aggarwal () and Susan Thomas ()

Indira Gandhi Institute of Development Research, Mumbai Working Papers from Indira Gandhi Institute of Development Research, Mumbai, India

Abstract: The causal impact of algorithmic trading on market quality has been difficult to establish due to endogeneity bias. We address this problem by using the introduction of co-location, an exogenous event after which algorithmic trading is known to increase. Matching procedures are used to identify a matched set of firms and set of dates that are used in a difference-in-difference regression to estimate causal impact. We find that securities with higher algorithmic trading have lower liquidity costs, order imbalance, and order volatility. There is new evidence that higher algorithmic trading leads to lower intraday liquidity risk and a lower incidence of extreme intraday price movements.

Keywords: Electronic limit order book markets; matching; difference-in-difference; efficiency; liquidity; volatility; flash crashes (search for similar items in EconPapers)
JEL-codes: G10 G18 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-mst
Date: 2014-07
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