Low latency trading and the comovement of order flow, prices, and market conditions*

We examine the impact of algorithmic trading (AT) in equities on the comovement of order flow, returns, liquidity, and volatility to assess how AT affects the market’s susceptibility to systemic shocks. Using order-level data around a natural experiment at the National Stock Exchange of India, which in 2010 has introduced features that promote HFT, we find that more intense AT reduces commonality in order flow, returns, liquidity, and volatility, and therefore reduce the market’s susceptibility to systemic shocks. These declines are more pronounced for algorithmic order flow and for large-cap firms. We attribute our findings to more intense competition among algorithmic than non-algorithmic traders. * This paper is part of the NSE-NYU Stern School of Business Initiative for the Study of Indian Capital Markets. The authors acknowledge the support of the initiative. The views expressed in this Working Paper are those of the authors and do not necessarily represent those of NSE or NYU. The authors also thank seminar and conference participants at the National University of Singapore and the 2013 Australasian Finance and Banking Conference.