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Trader Anonymity, Price Formation and Liquidity

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  • THIESSEN, Eric

Abstract

We analyze price formation and liquidity in a non-anonymous specialist market. Our main hypothesis is that the non-anonymity allows the specialist to assess the probability that a trader trades on the basis of private information. He uses this knowledge to price discriminate. This can be achieved by quoting a large spread and granting price improvement to traders deemed uninformed. Our empirical results confirm this view. We document that price improvement reflects lower adverse selection costs but does not lead to a reduction in the specialist's profit. We further show that the quote adjustment following transactions at the quoted prices is more pronounced than the quote adjustment after transactions at prices inside the spread. The results thus support the notion that a non-anonymous environment allows the identification of informed traders and may thus alleviate the adverse selection problem.

Suggested Citation

  • THIESSEN, Eric, 2000. "Trader Anonymity, Price Formation and Liquidity," HEC Research Papers Series 701, HEC Paris.
    • Handle: RePEc:ebg:heccah:0701
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    References listed on IDEAS

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    1. Chung, Kee H. & Van Ness, Bonnie F. & Van Ness, Robert A., 1999. "Limit orders and the bid-ask spread," Journal of Financial Economics, Elsevier, vol. 53(2), pages 255-287, August.
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    4. Benveniste, Lawrence M. & Marcus, Alan J. & Wilhelm, William J., 1992. "What's special about the specialist?," Journal of Financial Economics, Elsevier, vol. 32(1), pages 61-86, August.
    5. Ellis, Katrina & Michaely, Roni & O'Hara, Maureen, 2000. "The Accuracy of Trade Classification Rules: Evidence from Nasdaq," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(4), pages 529-551, December.
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    More about this item

    Keywords

    Anonymity; specialist; bid-ask spread;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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