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Does algorithmic trading improve liquidity?

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  • Hendershott, Terrence
  • Jones, Charles M.
  • Menkveld, Albert J.

Abstract

Algorithmic trading has sharply increased over the past decade. Equity market liquidity has improved as well. Are the two trends related? For a recent five-year panel of New York Stock Exchange (NYSE) stocks, we use a normalized measure of electronic message traffic (order submissions, cancellations, and executions) as a proxy for algorithmic trading, and we trace the associations between liquidity and message traffic. Based on within-stock variation, we find that algorithmic trading and liquidity are positively related. To sort out causality, we use the start of autoquoting on the NYSE as an exogenous instrument for algorithmic trading. Previously, specialists were responsible for manually disseminating the inside quote. As stocks were phased in gradually during early 2003, the manual quote was replaced by a new automated quote whenever there was a change to the NYSE limit order book. This market structure change provides quicker feedback to traders and algorithms and results in more message traffic. For large-cap stocks in particular, quoted and effective spreads narrow under autoquote and adverse selection declines, indicating that algorithmic trading does causally improve liquidity. --

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Bibliographic Info

Paper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2008/41.

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Date of creation: 2008
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Handle: RePEc:zbw:cfswop:200841

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Keywords: Liquidity; Algorithmic Trading; Microstructure;

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  1. Hasbrouck, Joel & Ho, Thomas S Y, 1987. " Order Arrival, Quote Behavior, and the Return-Generating Process," Journal of Finance, American Finance Association, vol. 42(4), pages 1035-48, September.
  2. Keim, Donald B. & Madhavan, Ananth, 1995. "Anatomy of the trading process Empirical evidence on the behavior of institutional traders," Journal of Financial Economics, Elsevier, vol. 37(3), pages 371-398, March.
  3. Pankaj K. Jain, 2005. "Financial Market Design and the Equity Premium: Electronic versus Floor Trading," Journal of Finance, American Finance Association, vol. 60(6), pages 2955-2985, December.
  4. Andrew W. Lo & A. Craig MacKinlay & June Zhang, 1997. "Econometric Models of Limit-Order Executions," NBER Working Papers 6257, National Bureau of Economic Research, Inc.
  5. FOUCAULT, Thierry & RÖELL, Ailsa & SANDAS, Patrik, 2000. "Market Making with Costly Monitoring : An Analysis of the SOES Controversy," Les Cahiers de Recherche 702, HEC Paris.
  6. Ranaldo, Angelo, 2004. "Order aggressiveness in limit order book markets," Journal of Financial Markets, Elsevier, vol. 7(1), pages 53-74, January.
  7. Bessembinder, Hendrik, 2003. "Issues in assessing trade execution costs," Journal of Financial Markets, Elsevier, vol. 6(3), pages 233-257, May.
  8. Glosten, Lawrence R, 1994. " Is the Electronic Open Limit Order Book Inevitable?," Journal of Finance, American Finance Association, vol. 49(4), pages 1127-61, September.
  9. Ekkehart Boehmer & Gideon Saar & Lei Yu, 2005. "Lifting the Veil: An Analysis of Pre-trade Transparency at the NYSE," Journal of Finance, American Finance Association, vol. 60(2), pages 783-815, 04.
  10. Anna Obizhaeva & Jiang Wang, 2005. "Optimal Trading Strategy and Supply/Demand Dynamics," NBER Working Papers 11444, National Bureau of Economic Research, Inc.
  11. Lee, Charles M C & Ready, Mark J, 1991. " Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-46, June.
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