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Algorithmic Trading and Information

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    We examine algorithmic trades (AT) and their role in the price discovery process in the 30 DAX stocks on the Deutsche Boerse. 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.

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    File URL: http://www.netinst.org/Hendershott_Riordan_09-08.pdf
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    Paper provided by NET Institute in its series Working Papers with number 09-08.

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    Length: 40 pages
    Date of creation: Mar 2009
    Date of revision: Aug 2009
    Handle: RePEc:net:wpaper:0908
    Contact details of provider: Web page: http://www.NETinst.org/

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    1. 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.
    2. Mitchell A. Petersen, 2005. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches," NBER Working Papers 11280, National Bureau of Economic Research, Inc.
    3. Ranaldo, Angelo, 2004. "Order aggressiveness in limit order book markets," Journal of Financial Markets, Elsevier, vol. 7(1), pages 53-74, January.
    4. Biais, Bruno & Hillion, Pierre & Spatt, Chester, 1995. " An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse," Journal of Finance, American Finance Association, vol. 50(5), pages 1655-89, December.
    5. 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.
    6. 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.
    7. 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.
    8. Bessembinder, Hendrik, 2003. "Issues in assessing trade execution costs," Journal of Financial Markets, Elsevier, vol. 6(3), pages 233-257, May.
    9. Hasbrouck, Joel, 1991. " Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    10. Kumar Venkataraman, 2001. "Automated Versus Floor Trading: An Analysis of Execution Costs on the Paris and New York Exchanges," Journal of Finance, American Finance Association, vol. 56(4), pages 1445-1485, 08.
    11. Copeland, Thomas E & Galai, Dan, 1983. " Information Effects on the Bid-Ask Spread," Journal of Finance, American Finance Association, vol. 38(5), pages 1457-69, December.
    12. Harald Hau, 2001. "Location Matters: An Examination of Trading Profits," Journal of Finance, American Finance Association, vol. 56(5), pages 1959-1983, October.
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