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Algorithmic Trading and Information

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Abstract

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.

Suggested Citation

  • Terrence Hendershott & Ryan Riordan, 2009. "Algorithmic Trading and Information," Working Papers 09-08, NET Institute, revised Aug 2009.
  • Handle: RePEc:net:wpaper:0908
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    File URL: http://www.netinst.org/Hendershott_Riordan_09-08.pdf
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    1. Hasbrouck, Joel, 1991. " Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
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    6. Ranaldo, Angelo, 2004. "Order aggressiveness in limit order book markets," Journal of Financial Markets, Elsevier, vol. 7(1), pages 53-74, January.
    7. Lo, Andrew W. & MacKinlay, A. Craig & Zhang, June, 2002. "Econometric models of limit-order executions," Journal of Financial Economics, Elsevier, vol. 65(1), pages 31-71, July.
    8. Copeland, Thomas E & Galai, Dan, 1983. " Information Effects on the Bid-Ask Spread," Journal of Finance, American Finance Association, vol. 38(5), pages 1457-1469, December.
    9. 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.
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    Citations

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    Cited by:

    1. Rohini Grover, 2017. "The informational role of algorithmic traders in the option market," Working Papers id:11701, eSocialSciences.
    2. Hoffmann, Peter, 2016. "Adverse selection, market access, and inter-market competition," Journal of Banking & Finance, Elsevier, vol. 65(C), pages 108-119.
    3. repec:eee:jbfina:v:79:y:2017:i:c:p:1-11 is not listed on IDEAS
    4. Dugast, J., 2013. "Limited attention and news arrival in limit order markets," Working papers 449, Banque de France.
    5. Siegmann, Arjen & Stefanova, Denitsa, 2017. "The evolving beta-liquidity relationship of hedge funds," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 286-303.
    6. Kasing Man & Junbo Wang & Chunchi Wu, 2013. "Price Discovery in the U.S. Treasury Market: Automation vs. Intermediation," Management Science, INFORMS, vol. 59(3), pages 695-714, September.
    7. Albert J. Menkveld, 2011. "High Frequency Trading and the New-Market Makers," Tinbergen Institute Discussion Papers 11-076/2/DSF21, Tinbergen Institute, revised 15 Aug 2011.

    More about this item

    Keywords

    Algorithmic trading; information technology; price discovery; market microstructure; price efficiency;

    JEL classification:

    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • G1 - Financial Economics - - General Financial Markets

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