Does Algorithmic Trading Improve Liquidity?





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    • Hendershott is at Haas School of Business, University of California Berkeley. Jones is at Columbia Business School. Menkveld is at VU University Amsterdam. We thank Mark van Achter, Hank Bessembinder, Bruno Biais, Alex Boulatov, Thierry Foucault, Maureen O'Hara, Sébastien Pouget, Patrik Sandas, Kumar Venkataraman, the NASDAQ Economic Advisory Board, and seminar participants at the University of Amsterdam, Babson College, Bank of Canada, CFTC, HEC Paris, IDEI Toulouse, Southern Methodist University, University of Miami, the 2007 MTS Conference, NYSE, the 2008 NYU-Courant algorithmic trading conference, University of Utah, the 2008 Western Finance Association meetings, and Yale University. We thank the NYSE for providing system order data. Hendershott gratefully acknowledges support from the National Science Foundation, the Net Institute, the Ewing Marion Kauffman Foundation, and the Lester Center for Entrepreneurship and Innovation at the Haas School at UC Berkeley. Menkveld gratefully acknowledges the College van Bestuur of VU University Amsterdam for a VU talent grant.


Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade-related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes.