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A Dynamic Analysis of Bid-Ask Spreads with Multiple Trade Sizes

Author

Listed:
  • Shino Takayama

    (The University of Sydney)

  • Han Ozsoylev

    (University of Oxford)

Abstract

This paper studies how the trade size and the historical sequence of trades affect bid-ask spreads, investors’ trading strategies, and the market maker’s learning process in a multi-period economy. First, we show that there is a nonzero cut-off size below which informed traders never buy or sell, and that larger trade sizes have positive bid-ask spreads, while smaller sizes do not. Then, we prove that the cut-off size decreases stochastically . We also derive the functional relationship between bid-ask spreads and trade sizes and show that bid- ask spreads are monotonically increasing in trade sizes. Moreover, we prove that when additional trade sizes are introduced to the market, the market maker’s learning process can be impaired and the bid-ask spreads for the previously existing trade sizes can vanish under a mild condition. We prove that the smaller trade sizes that do not have a positive bid-ask spread result in zero price change, while for larger trade sizes the rate at which price change increases is a decreasing function of the trade size in all trading periods. Most of our results are broadly consistent with the empirical findings.

Suggested Citation

  • Shino Takayama & Han Ozsoylev, 2005. "A Dynamic Analysis of Bid-Ask Spreads with Multiple Trade Sizes," Finance 0509007, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpfi:0509007
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    References listed on IDEAS

    as
    1. Hasbrouck, Joel, 1991. "Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    2. Jones, Charles M & Kaul, Gautam & Lipson, Marc L, 1994. "Transactions, Volume, and Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 7(4), pages 631-651.
    3. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(1), pages 109-126, March.
    4. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    5. Easley, David & Kiefer, Nicholas M & O'Hara, Maureen, 1997. "One Day in the Life of a Very Common Stock," The Review of Financial Studies, Society for Financial Studies, vol. 10(3), pages 805-835.
    6. Hasbrouck, Joel, 1988. "Trades, quotes, inventories, and information," Journal of Financial Economics, Elsevier, vol. 22(2), pages 229-252, December.
    7. Easley, David & O'Hara, Maureen, 1987. "Price, trade size, and information in securities markets," Journal of Financial Economics, Elsevier, vol. 19(1), pages 69-90, September.
    8. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2003. "A theory of power-law distributions in financial market fluctuations," Nature, Nature, vol. 423(6937), pages 267-270, May.
    9. Vasiliki Plerou & Parameswaran Gopikrishnan & Xavier Gabaix & H. Eugene Stanley, 2001. "Quantifying Stock Price Response to Demand Fluctuations," Papers cond-mat/0106657, arXiv.org.
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    More about this item

    Keywords

    Market microstructure; insider trading; Glosten-Milgrom Model; asymmetric information; bid-ask spreads;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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