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.

Citation: The causal impact of algorithmic trading on market quality, Nidhi Aggarwal, Susan Thomas. IGIDR Working paper WP-2014-023, July 2014.

Links

• Working paper
• RePEc Page

Commentary

• What does algorithmic trading do to market quality? by Nidhi Aggarwal, Susan Thomas, Ajay Shah's blog, 1^st September 2014.