Algorithmic Trading and Information

We examine algorithmic trades (AT) and their role in the price discovery process in the 30 DAX stocks on the Deutsche Boerse in January 2008. AT liquidity demand represents 52% of volume and AT supplies liquidity on 50% of volume. AT act strategically by monitoring the market for liquidity and deviations of price from fundamental value. AT consume liquidity when it is cheap and supply liquidity when it is expensive. AT contribute more to the efficient price by placing more efficient quotes and AT demanding liquidity to move the prices towards the efficient price. ∗We thank Bruno Biais, Doug Foster, and seminar and conference participants at the University of Texas at Austin, University of Sydney 4th Annual Microstructure Conference, New York University Courant Institute of Mathematical Sciences 2nd Annual Algorithmic Trading Conference, 2009 Workshop on Information Systems and Economics, 2009 German Finance Association, and IDEI-R Conference on Investment Banking and Financial Markets for helpful comments. Hendershott gratefully acknowledges support from the Net Institute, the Ewing Marion Kauffman Foundation, and the Lester Center for Entrepreneurship and Innovation at the Haas School at UC Berkeley. Riordan gratefully acknowledges support from the Deutsche Forschungsgemeinschaft graduate school of Information and Market Engineering at the Karlsruhe Institute of Technology. Data was provided by the Deutsche Boerse and by the Securities Industry Research Centre of Asia-Pacific (SIRCA) on behalf of Reuters.